Students find these Class 11 AI MCQ Chapter 2 Unlocking your Future in AI Class 11 MCQ Online Test with Answers helpful for self-assessment and preparation.

Class 11 Unlocking your Future in AI MCQ

MCQ on Unlocking your Future in AI Class 11

Class 11 Unlocking your Future in AI MCQ – Unlocking your Future in AI MCQ Class 11

Multiple Choice Questions

Question 1.
A ______ is an AI application that can mimic a real conversation with a user in their natural language.
(a) Chatbot
(b) ChatGPT
(c) Text recognition
(d) Robot
Answer:
(a) Chatbot

Question 2.
Which of the following is the first robot to be granted citizenship in a nation?
(a) SOPHIA
(b) MONK
(C) AIBO
(d) JARVIX
Answer:
(a) SOPHIA

Question 3.
Which of the following permits text-based or audio-based, communication on websites?
(a) Computer vision
(b) Chatbots
(C) Artificial Intelligence
(d) Driverless cars
Answer:
(b) Chatbots

Question 4.
Rule-based chatbots are also known as _____.
(a) Plant bots
(b) Decision tree bots
(c) Chatbots
(d) Robots
Answer:
(b) Decision tree bots

Question 5.
______ is an area of artificial intelligence that employs natural language for interaction between computers and humans.
(a) OCR
(b) IE
(C) ASR
(d) NLP
Answer:
(d) NLP

Question 6.
Which of these helps us to uniquely identify and delineate each object of interest that appears in an image?
(a) Weather forecasting
(b) Object detection
(c) Instance segmentation
(d) Semantic segmentation
Answer:
(c) Instance segmentation

Question 7.
How many tasks computer vision undertakes?
(a) six
(b) two
(c) four
(d) three
Answer:
(c) four

Question 8.
Which of the following is a job role in AI related to the automobile industry?
a. Robotics Engineer
b. Virtual Nurse Assistant
c. Sales Forecasting Analyst
d. Autonomous Vehicle Engineer
Answer:
d. Autonomous Vehicle Engineer

Question 9.
Identify the important soft skill required for AI professionals.
a. Expertise in neural networks
b. Effective communication
c. Proficiency in Python
d. Knowledge of big data technologies
Answer:
b. Effective communication

Question 10.
Which industry uses AI for personalized travel recommendations?
a. Tourism
b. Banking
c. Healthcare
d. Geospatial
Answer:
a. Tourism

Question 11.
What is the purpose of the website “Data Science Central”?
a. Providing a community experience for data practitioners
b. Offering free video-based courses on AI
c. Analyzing market trends using AI algorithms
d. Providing insight into emerging trends in big data
Answer:
a. Providing a community experience for data practitioners

Question 12.
Which industry uses AI for market analysis and fraud detection?
a. Finance
b. Media
c. Textile
d. Design
Answer:
a. Finance

Question 13.
What has AI become an indispensable part of in modern society?
a) Fictional stories
b) Daily lives
c) Historical analysis
d) Traditional manufacturing
Answer:
b) Daily lives

Question 14.
How should individuals view AI, according to the content?
a) As a threat
b) As a fleeting trend
c) As an opportunity for advancement and innovation
d) As a traditional role replacer
Answer:
c) As an opportunity for advancement and innovation

Question 15.
What is driving the global demand for AI talent?
a) Decline in technology
b) Rapid pace of technological advancements
c) Traditional job roles
d) Static industry requirements
Answer:
b) Rapid pace of technological advancements

Question 16.
Which industry does NOT leverage AI to optimize processes and deliver enhanced services?
a) Healthcare
b) Finance
c) Literature
d) Transportation
Answer:
c) Literature

Question 17.
What role involves developing scalable data science models capable of handling vast volumes of real-time data?
a) Data Scientist
b) Business Intelligence Developer
c) Machine Learning Engineer
d) AI Ethicist
Answer:
c) Machine Learning Engineer

Question 18.
Which role requires proficiency in big data platforms like Hadoop, Pig, and Spark?
a) Data Scientist
b) Natural Language Processing (NLP) Engineer
c) Robotics Engineer
d) Computer Vision Engineer
Answer:
a) Data Scientist

Question 19.
Who is responsible for designing and maintaining AI-powered robots?
a) Business Intelligence Developer
b) AI Consultant
c) Software Engineer
d) Robotics Engineer
Answer:
d) Robotics Engineer

Question 20.
Which role specializes in human language processing?
a) AI Consultant
b) Natural Language Processing (NLP) Engineer
c) Machine Learning Engineer
d) Data Scientist
Answer:
b) Natural Language Processing (NLP) Engineer

Question 21.
What type of skills are essential for managing multiple projects and meeting deadlines in AI careers?
a) Only technical skills
b) Soft skills
c) Programming languages
d) Mathematical priaciples
Answer:
b) Soft skills

Question 22.
Which programming language is known for its flexibility and object-oriented functions used in AI?
a) Python
b) R
c) Java
d) C++
Answer:
d) C++

Question 23.
Which industry is mentioned as employing AI for crop health monitoring and irrigation optimization?
a) Retail
b) Agriculture
c) Healthcare
d) Finance
Answer:
b) Agriculture

Question 24.
What role in the healthcare industry involves analyzing medical images using AI algorithms?
a) Financial Advisor
b) Autonomous Vehicle Engineer
c) Medical Imaging Analyst
d) Geographic Information Systems (GIS) Specialist
Answer:
c) Medical Imaging Analyst

Question 25.
Which AI role focuses on identifying potential drug candidates using AI?
a) Virtual Nurse Assistant
b) Drug Discovery Researcher
c) Sales Forecasting Analyst
d) Fraud Detection Analyst
Answer:
b) Drug Discovery Researcher

Question 26.
What is one of the free learning resources mentioned for AI skills?
a) Kaggle
b) IBM SkillsBuild
c) freeCodeCamp.org
d) All of the above
Answer:
d) All of the above

Assertion-Reasoning Based Questions

Study the two statements labeled as assertion (a) and reason (r).
Point out if:
(a) Both, a and r, are true and r is the correct explanation of a
(b) Both, a and r, are true but r is not the correct explanation of a
(c) If a is true but r is false
(d) If a is false but r is true

Question 1.
Assertion(A): AI based chatbots also learn from previous experience and reinforced learning and it keeps on evolving.
Reason (R): Rule based chatbots follows a set of pre-defined rules in responding to user’s questions.
Answer:
(b) Both, a and r, are true but r is not the correct explanation of a

Question 2.
Assertion(A):Limitation of a rule-based bot is that it only understands a pre-defined set of options.
Reason (R):Rule-based bot understands only the value that we supply. However, it won’t understand the intent and context of the user’s conversation with it.
Answer:
(a) Both, a and r, are true and r is the correct explanation of a

Question 3.
Assertion (A): NLP essentially comprises of natural language understanding and natural language generation.
Reason (R): The main objective of NLP is to read, interpret, comprehend, and coherently make sense of the human language such that it creates value for all.
Answer:
(b) Both, a and r, are true but r is not the correct explanation of a

Question 4.
Assertion (A): The global demand for AI talent is skyrocketing.
Reason (R): AI technologies are replacing traditional roles and not creating any new job opportunities.
Answer:
c) A is true, but R is false.

Question 5.
Assertion (A): AI professionals are in high demand in various industries such as healthcare, finance, and transportation.
Reason (R): AI helps in streamlining operations, optimizing processes, and enhancing services.
Answer:
a) Both A and R are true, and R is the correct explanation of A.

Question 6.
Assertion (A): Machine Learning Engineers require strong mathematical skills and proficiency in programming languages like Java, Python, and Scala.
Reason (R): Machine Learning Engineers develop scalable data science models capable of handling vast volumes of real-time data.
Answer:
a) Both A and R are true, and R is the correct explanation of A .

Question 7.
Assertion (A): AI ethicists provide guidance on ethical frameworks and policies for AI systems.
Reason (R): AI ethicists ensure that AI technologies are used responsibly and ethically.
Answer:
a) Both A and R are true, and R is the correct explanation of A.

Question 8.
Assertion (A): Effective communication skills are crucial for AI professionals.
Reason (R): AI professionals need to convey complex technical concepts to non-technical stakeholders.
Answer:
a) Both A and R are true, and R is the correct explanation of A .

Question 9.
Assertion (A): Python is a versatile programming language for AI development.
Reason (R): Python has pre-made libraries for advanced computing and scientific computation.
Answer:
a) Both A and R are true, and R is the correct explanation of A.

Question 10.
Assertion (A): AI technologies are beneficial in agriculture for monitoring crop health and optimizing irrigation.
Reason (R): AI in agriculture involves the use of drones and sensors for precision farming.
Answer:
a) Both A and R are true, and R is the correct explanation of A .

Question 11.
Assertion (A): AI professionals in the healthcare industry can work as Virtual Nurse Assistants.
Reason (R): Virtual Nurse Assistants provide personalized healthcare recommendations.
Answer:
a) Both A and R are true, and R is the correct explanation of A.

Question 12.
Assertion (A): AI tools and platforms are essential for AI professionals.
Reason (R): Tools like TensorFlow, SciPy, and NumPy help in developing and deploying AI solutions.
Answer:
a) Both A and R are true, and R is the correct explanation of A.

Question 13.
Assertion (A): AI-powered Fashion Stylists recommend personalized clothing combinations. Reason (R): AI in fashion analyzes trends and consumer preferences to provide personalized recommendations.
Answer:
a) Both A and R are true, and R is the correct explanation of A.

Case-Based Questions

Question 1.
Which AI application is used in the following cases?
a. Fani, a rare summer cyclone in the Bay of Bengal, hits eastern India on May 03,2019. It is one of the strongest cyclones to have hit India in the last 20 years, according to the Indian government’s meteorological department. Storm surges and powerful winds reaching 125 mph blew off roofs, damaged power lines, and uprooted countless trees.

But the worst-affected state, Odisha, has been successful in keeping the loss of life and numbers of affected people to a minimum. This is the result of a very effective strategy of disaster preparation and effective weather forecasting.

(Source: https://qz.com/india/1618717/indias-handling-of-cyclone-fani-has-a-lesson-for-theus/ )

b. When we speak in our device, the microphones of the device hear our audio and plots the graphs of our sound frequencies. As light-wave has a standard frequency for each colour, so does sound. Every sound (phonetics) has a unique frequency graph.

For example:
You: Alexa, what is the date today?
Alexa: It is the 18-March-2020.
Answer:
a) Computer Vision
b)Natural Language Processing

Question 2.
Sumedha and Neha in conversation with each other talked about the working of AI CCTV camera installed in their school. But they don’t know how it works. Help them in understanding the underlying concept behind it.
Answer:
AI CCTV cameras are advanced devices that does motion detection, face detection and similar features. The main job of the AI here is to make use of advanced analytical programs that have been integrated into their software. So, that all the activities of a human being can be recorded and analysed.

Question 3.
While reading newspaper Kanak, saw that a new startup company has launched an app that helps farmers using the AI technology. But she doesn’t know how AI helps to farmers. Help her in understanding the usage of AI.
Answer:
When it comes to the commodity market, global consumption outweighs local output. For this reason, price forecasting is highly advantageous to farmers. Generally speaking, agricultural products or crops are more vulnerable to weather, demand, and price risk than any other type of product.

As a result, small farmers frequently sell their products for extremely low prices. Crop waste can occasionally result from harsh weather or over harvesting. When crops fail and when there are abundant harvests, farmers are distressed. Farmers will be able to control their price and make educated decisions with the help of a trustworthy price forecasting tool.

Question 4.
In the class teacher explained about ‘Recommendation System’. But Mohan does not grasp the concept and he also can’t differentiate it from ‘Autonomous System’. What do you think are both same? Reason out your answer.
Answer:
Autonomous System: The technology through which machine understands the system and reacts without human intervention. The autonomous systems often use Artificial intelligence. For example, self -driving car, a floor cleaning robot etc. are autonomous system. Even IoT device like a smart home system is another example of an autonomous system. For instance, according to room temperature Acs are ON or not.

Recommendation System: Whereas, a recommendation system recommends or suggests products, services, information to users based on analysis of data that are dependent on a number of factors such as the history, behavior, preference, and interest of the user, etc.

Recommendation System are data driven; they need data for training. For instance, YouTube, suggests based on our past searches or preferences. So, in short it gathers data from the history of your activities and behavior. Similarly, Flipkart recommends the types of products that you normally prefer or buy. All these are classic examples of recommendation, but you must know that Machine Learning (AI) is working behind all this to give you a good user experience.

Question 5.
Medha just finished her Class X exams and dreams of a career in AI for Finance. She’s unsure what subjects to choose in higher studies (Class XI & XII) to prepare for this future goal. What subjects would you recommend for Medha to take in school, and are there any additional courses she can consider to strengthen her profile?
Answer:
Medha can pursue a job in finance while pursuing her goals in AI. Her academic priorities should be in mathematics, computer science, and commerce (business studies, accounting, and economics). Furthermore, online courses that bridge the gap between finance and AI may be found on sites like Kaggle, Udemy, and Data Science programmes. These courses might impart useful technical skills. She will be well-prepared for a prosperous future in AI for Finance with this combo.

Question 6.
Akash, a recent graduate, just joined the IT and AI startup company as a software developer. He’s brilliant at coding but feels a bit nervous about collaborating with his new team. He feels explaining technical concepts to non-technical team members and clearly documenting code could be a challenge. Also, he’s not sure how to effectively collaborate with others and handle situations where there might be disagreements about the best approach. List some soft skills that can help him excel in this environment:
Answer:
Effective Communication: Being able to explain complex technical ideas in clear and concise language, both verbally and in writing, is crucial.

Teamwork and Collaboration: Success in an AI startup often depends on working effectively with others. Actively listen to your colleagues, share ideas openly, and be willing to compromise when necessary.

Problem-solving: AI projects often involve unexpected hurdles. Develop a logical approach to problem-solving, considering different solutions and collaborating with your team to find the best approach.

Question 7.
Saloni, a career counselor, is visiting a Class XII assembly to talk about exciting career opportunities in Artificial Intelligence (AI). After explaining various AI fields, a student named Aditya raises his hand and asks, “This all sounds fascinating, but how is AI being used in healthcare specifically?” How will Saloni address Aditya’s query?
Answer:
In response to Aditya’s question, Saloni demonstrated how artificial intelligence (AI) is transforming the healthcare industry through projects including robotic surgery, medication development, virtual assistants, medical image analysis, risk assessment, and administrative work automation. This demonstrates the enormous potential of employment in artificial intelligence at the nexus of technology and medicine.

Question 8.
Naveen is a student. Help him to differentiate the commonly used AI tools, platforms and programming languages used for the development of AI applications. Python, GW-BASIC, R, MS-Word, IBM Watson, TensorFlow, MS-Paint, SciPy, Perl.
Answer:
Python, R, IBM Watson, TensorFlow and SciPy.

Question 9
Mr. Sourav Shukla is a journalist and owns a media house. He wants to leverage AI in his field. List some employment opportunities and some job roles related to the field of media.
Answer:
Visual effects, content generation, and audience analysis. Job roles are: Visual Effect Artist, Content Creator, Audience or Data Analyst, and targeted advertising.

Scenario Based Question Answer :

1. Scenario: Sarah has recently graduated with a degree in computer science and is interested in pursuing a career in AI. She is particularly passionate about developing algorithms that can interpret visual information from images or videos.

Question:
Which AI job role would be most suitable for Sarah, and what skills should she focus on acquiring to excel in this role?
Answer:
The most suitable job role for Sarah would be a Computer Vision Engineer. To excel in this role, she should focus on acquiring skills in developing algorithms and systems for analyzing and interpreting visual information. This includes proficiency in image processing techniques and programming languages such as Python and C++.

2. Scenario: John works as a business intelligence developer and wants to transition into a role that involves leveraging AI to provide strategic guidance to organizations. He has strong analytical skills and understands business processes well.

Question:
Which AI job role would be a good fit for John, and what additional knowledge or skills might he need?
Answer:
The AI job role that would be a good fit for John is an AI Consultant. In addition to his current skills, John might need to deepen his understanding of AI technologies and their applications in various industries. He should also develop strong communication skills to effectively convey AI strategies and solutions to organizational stakeholders.

3. Scenario: Maria is a data scientist who has been working with machine learning models to extract insights from large datasets. She is now interested in exploring roles where she can work on developing scalable AI models that handle real-time data.

Question:
Which job role should Maria consider, and what technical skills are essential for this position?
Answer:
Maria should consider the role of a Machine Learning Engineer. Essential technical skills for this position include strong mathematical skills, experience in machine learning and deep learning, and proficiency in programming languages like Java, Python, and Scala. She should also be familiar with big data tools and programming frameworks.

4. Scenario: David is a robotics engineer interested in designing AI-powered robots that can perform tasks based on human commands. He is looking for a role that combines his expertise in mechanical and electrical engineering with AI.

Question:
What job role would be most appropriate for David, and what areas should he focus on to succeed?
Answer:
The most appropriate job role for David is a Robotics Engineer. To succeed in this role, he should focus on areas such as programming, robotic engineering, mechanical engineering, and electrical engineering. He should also stay updated with the latest AI technologies used in robotics.

5. Scenario: Emily is passionate about natural language processing (NLP) and wants to work on projects involving voice assistants and speech recognition. She has a background in computational linguistics.

Question:
Which AI job role would align with Emily’s interests, and what should she concentrate on learning or improving?
Answer:
The AI job role that aligns with Emily’s interests is an NLP Engineer. She should concentrate on improving her knowledge of human language processing and developing skills in programming languages relevant to NLP, such as Python. She should also stay updated on the latest advancements in voice assistants and speech recognition technologies.

6. Scenario: Alex is working in the finance industry and wants to transition into an AI role that focuses on fraud detection and risk management. He has strong analytical and statistical skills.

Question:
What AI job role should Alex pursue, and what additional skills or knowledge might he need?
Answer:
Alex should pursue the role of a Fraud Detection Analyst. Additional skills or knowledge he might need include proficiency in machine learning algorithms, experience with big data platforms, and an understanding of AI models used in fraud detection. Enhancing his programming skills in languages like SQL, Python, and Scala would also be beneficial.

7. Scenario: Lisa is interested in working as an AI ethicist. She has a background in philosophy and law and wants to ensure that AI technologies are used responsibly.

Question:
What should Lisa focus on to become a successful AI ethicist, and what key responsibilities would her role include?
Answer:
To become a successful AI ethicist, Lisa should focus on gaining expertise in AI technology and understanding its ethical implications. Key responsibilities in her role would include addressing ethical considerations, providing guidance on ethical frameworks and policies, and promoting fairness, transparency, and accountability in AI systems.

8. Scenario: Kevin is an AI professional who enjoys collaborating with multidisciplinary teams and solving complex problems. He wants to take on a role that involves both technical expertise and strategic decision-making.

Question:
Which’AI job role would suit Kevin’s preferences, and what skills are crucial for this role?
Answer:
The AI job role that would suit Kevin’s preferences is an AI Consultant. Crucial skills for this role include a deep understanding of AI technologies, strong communication and analytical skills, and the ability to assess business needs and develop strategic AI initiatives. Collaboration and problem-solving abilities are also essential.

9. Scenario: Laura has been working as a software engineer and is interested in developing AIpowered applications. She wants to stay updated on the latest AI technologies and integrate them into software products.

Question: What’AI job role should Laura consider, and what areas should she focus on to excel in this role?
Answer:
Laura should consider the role of an AI Software Engineer. To excel in this role, she should focus on staying updated on the latest AI technologies, developing proficiency in software engineering and programming languages, and understanding statistical analysis. She should also enhance her knowledge of AI application development.

10. Scenario: Mike is a marketing professional interested in using AI to optimize marketing campaigns and enhance customer segmentation. He has experience in business studies and mathematics.

Question:
What AI job role would be ideal for Mike, and what additional skills or tools might he need to learn?
Answer:
The ideal AI job role for Mike is a Marketing Campaign Automation Specialist. Additional skills or tools he might need to learn include AI algorithms for campaign automation, customer segmentation techniques using AI, and proficiency in relevant AI tools and platforms. Enhancing his understanding of data analytics and AI-driven marketing strategies would also be beneficial.

The post Unlocking your Future in AI Class 11 MCQ Online Test appeared first on Learn CBSE.

Students find these Class 11 AI MCQ Chapter 3 Python Programming Class 11 MCQ Online Test with Answers helpful for self-assessment and preparation.

Class 11 Python Programming MCQ

MCQ on Python Programming Class 11

Class 11 Python Programming MCQ – Python Programming MCQ Class 11

Multiple Choice Questions

Question 1.
Which of the following describes a numerical arrangement that is rectangular?
a-Scalar
b-Matrix
c-Vector
d-Sets
Answer:
b-Matrix

Question 2.
Which item in the following lists is not arranged in a particular order?
a-Set
b-Vector
c-Determinant
d-Matrix
Answer:
a-Set

Question 3.
Which kind of linear regression involves linear variation in both variables?
a-Complex
b-Linear
c-Simple
d-Logistic
Answer:
a-Complex

Question 4.
Out of these, which one combines the output of the left and right joins?
a-Inner join
b-Left outer join
c-Full join
d-Right outer join
Answer:
d-Right outer join

Question 5.
In a scatterplot, which line comes closest to the majority of the data points?
a-Line of best-fit
b-Linear Regression
c-Fitting Line
d-Either (a) or (b)
Answer:
a-Line of best-fit

Question 6.
Identify the datatype L = “45”
a. String
b. int
c. float
d. tuple
Answer:
a. String

Question 7.
Which of the following function converts a string to an integer in python?
a. int(x)
b. long(x)
c. float(x)
d. str(x)
Answer:
a. int(x)

Question 8.
Which special symbol is used to add comments in python?
a. $
b. //
c. /*…. */
d. #
Answer:
d. #

Question 9.
Which of the following variable is valid?
a. Str name
b. 1str
c. _str
d. #Str
Answer:
c. _str

Question 10.
Elements in the list are enclosed in _____ brackets
a. ()
b. {}
c. []
d. /**/
Answer:
c. []

Question 11.
Index value of last element in list is ______.
a. 0
b. -10
c. -1
d. 10
Answer:
c. -1

Question 12.
What will be the output of the following code?
a = [10,20,30,40,50]
print (a[o])
a. 20
b. 50
c. 10
d. 40
Answer:
c

Question 13.
Name thep function that displays the data type of the variable.
a. data()
b. type()
c. datatype()
d. int()
Answer:
b. type()

Question 14.
Which library helps in manipulating csv files?
a. files
b. csv
c. math
d. print
Answer:
b. csv

Question 15.
Which keyword can be used to stop a loop?
a. stop
b. break
c. brake
d. close
Answer:
b. break

Question 16.
What is the primary data structure used in NumPy to represent arrays of any dimension?
a) Series
b) DataFrame
c) ndarray
d) Panel
Answer:
c) ndarray

Question 17.
Which of the following is not a valid method to access elements of a Pandas DataFrame?
a) Using column names as attributes.
b) Using row and column labels with the loc[] accessor.
c) Using integer-based indexing with the iloc[] accessor.
d) Using the .get() method.
Answer:
d) Using the .get() method.

Question 18.
What is the purpose of the head() method in Pandas?
a) To display the first few rows of a DataFrame.
b) To display the last few rows of a DataFrame.
c) To count the number of rows in a DataFrame.
d) To perform aggregation operations on a DataFrame.
Answer:
a) To display the first few rows of a DataFrame.

Question 19.
Which method is used to drop rows with missing values from a DataFrame in Pandas?
a) drop_rows()
b) remove_missing()
c) dropna()
d) drop_missing_values 68
Answer:
c) dropna()

Question 20.
Which is not a module of Sklearn?
a) load_iris
b) train_test_split
c) metrics
d) Scikit
Answer:
d) Scikit

Question 21.
Who created Python and when was it released?
A) Guido van Rossum, 1989
B) Guido van Rossum, 1991
C) Dennis Ritchie, 1991
D) Bjarne Stroustrup, 1989
Answer:
B) Guido van Rossum, 1991

Question 22.
Which of the following is NOT a feature of Python?
A) High-Level Language
B) Interpreted Language
C) Platform Dependent
D) Easy to use and learn
Answer:
C) Platform Dependent

Question 23.
Which of the following is a Python editor or IDE?
A) Jupyter Notebook
B) Visual Studio
C) Sublime Text
D) Notepad
Answer:
A) Jupyter Notebook

Question 24.
What is a token in a Python program?
A) The smallest unit of a program that the interpreter or compiler recognizes
B) A high-level function in Python
C) A data type in Python
D) An error in Python
Answer:
A) The smallest unit of a program that the interpreter or compiler recognizes

Question 25.
Which of the following is a type of literal in Python?
A) String Literal
B) Numeric Literal
C) Boolean Literal
D) All of the above
Answer:
D) All of the above

Question 26.
Which statement is used to load functions from a library in Python?
A) import
B) include
C) using
D) require
Answer:
A) import

Question 27.
Which of the following is NOT a built-in data type in Python?
A) Integer
B) List
C) Float
D) Queue
Answer:
D) Queue

Question 28.
Which Python function is used to accept input from the user?
A) scanf()
B) input()
C) read()
D) gets()
Answer:
B) input()

Question 29.
What is the return type of the input() function in Python?
A) int
B) float
C) string
D) list
Answer:
C) string

Question 30.
Which control flow statement is used to execute a block of code repeatedly in Python?
A) if
B) elif
C) for
D) switch
Answer:
C) for

Question 31.
What is the default index of a Pandas Series?
A) Starts from 1
B) Alphabetical
C) Starts from 0
D) None
Answer:
C) Starts from 0

Question 32.
Which method is used to check for missing values in a Pandas DataFrame?
A) isnull()
B) ismissing()
C) checknull()
D) nullcheck()
Answer:
A) isnull()

Question 33.
Which library in Python is used for numerical computing?
A) NumPy
B) Pandas
C) Matplotlib
D) Scikit-learn
Answer:
A) NumPy

Question 34.
Which of the following libraries is built on top of NumPy?
A) Pandas
B) Matplotlib
C) Scikit-learn
D) All of the above
Answer:
D) All of the above

Question 35.
Which Python library is used for machine learning and statistical modeling?
A) TensorFlow
B) Keras
C) Scikit-learn
D) PyTorch
Answer:
C) Scikit-learn

Assertion-Reasoning Based Questions

Study the two statements labeled as assertion (a) and reason (r).
Point out if:
(a) Both, a and r, are true and r is the correct explanation of a
(b) Both, a and r, are true but r is not the correct explanation of a
(c) If a is true but r is false
(d) If a is false but r is true

Question 1.
Assertion(A): A matrix with 3 rows and 3 columns is called a square matrix.
Reason(R): A matrix having equal number of rows and columns is called square matrix.
Answer:
(a) Both, a and r, are true and r is the correct explanation of a

Question 2.
Assertion(A): Two vectors of unequal length when subtracted creates a third vector.
Reason(R) : A vector can be defined as a set of n numbers.
Answer:
(b) Both, a and r, are true but r is not the correct explanation of a

Question 3.
Assertion(A): If Determinant of a matrix o, then its inverse matrix exists.
Reason (R):Matrix inversion is a process that finds another matrix, when multiplied with the matrix, results in an identity matrix.
Answer:
(d) If a is false but r is true

Question 4.
Assertion (A): Vector arithmetic is used in Machine learning for training an algorithm.
Reason (R): Vectors are the one-dimensional matrix enclosed in square brackets.
Answer:
(b) Both, a and r, are true but r is not the correct explanation of a

Case-Based Questions

Question 1.
Madhu is confused on uses of the matrices AI help her in understanding the same.
Answer:
In machine learning matrices are used for computing. It help in :

• Sales forecasting and predicting price
• In processing images
• In recommendation systems
• In processing natural language or NLP

Question 2.
Shyama is little bit confused while evaluating transpose and determinants of a matrix. Help her in understanding the concepts with an example.
Answer:
Transpose of a matrix. When a matrix obtained by a interchanging its rows and column then that matrix is called a transpose of a matrix (A) and is denoted by A^T.

Determinants of a matrix : Determinant is a scalar value it may be either a real number of a complex number bht it is a unique number. For example

Question 3.
Jagdish is watching that Shyama was trying to understand the determinants and transpose of a matrix now he want to discuss her about inverse of a matrix. Suggest a way to help them.
Answer:
Inverse of a matrix is the process of finding another matrix, which when multiplied with the original matrix gives an identity matrix. It is denoted by A^-1. For example

Question 4.
Help Priya to differentiate the given information into various datatypes of Python. Name of the student, email id, student id, marks in 5 subjects which can be changed at any point, 3 extra subjects chosen which cannot be changed later.
Answer:
The different datatypes of Python for the given information are:

1. Name of the student-String type
2. Email id-String type
3. Student id-Integer type
4. Marks in 5 subjects which can be changed at any point-List type.
5. Extra 3 subjects which cannot be changed later-Tuple type.

Question 5.
For any analysis to be done, a huge amount of data needs to be collected and stored in a proper format. Rohan has stored the information in a delimited file that stores tabular data which is separated by comma. Which type of file is Rohan using?
Answer:
CSV file (Comma Separated Values)

Question 6.
Athrav is confused about the different libraries of Python. Help him choose the correct library for the following tasks. a. Data manipulation and aggregation functionalities b . Numerical computing c. Mathematical operations like square root, cosine values d. Machine learning and statistical modeling
Answer:
a. pandas
b. numpy
c. math
d. scikit-learn

Question 7.
Rohit wants to input the data about the runs scored by his 50 classmates. Which is the most appropriate loop to be used in this case?
Answer:
‘for’ loop

Scenario Based Question Answer:

Scenario: 1.

Question:
As a beginner in Python programming, you are interested in using Jupyter Notebook for your data science projects. Explain the steps you would follow to install and use Jupyter Notebook on your computer.
Answer:
To install and use Jupyter Notebook for your Python projects, follow these steps:
Installation:

• If you’re using Anaconda, Jupyter Notebook comes pre-installed. Simply launch Anaconda Navigator and click on the Jupyter Notebook icon.
• If you prefer using pip, open your command prompt or terminal and type pip install notebook.
• Once installed, you can start Jupyter Notebook by typing jupyter notebook in your command prompt or terminal.

Opening Jupyter Notebook:

• After starting Jupyter Notebook, a new browser window or tab will open showing the Jupyter Dashboard.
• Here, you can navigate to the directory where you want to create or open your Python notebooks.

Creating a New Notebook:

• Click on the “New” button on the top right corner and select “Python 3” (or any other kernel you need) to create a new notebook.
• This will open a new tab where you can start typing your Python code.

Writing and Running Code:

• In the notebook, you can write Python code in cells. To execute a cell, press Shift + Enter or click the “Run” button in the toolbar.
• Output will appear below each cell where you’ve written code.

Saving and Closing:

• To save your work, click on the “File” menu and select “Save and Checkpoint”, or use Ctrl + S (Cmd +S on macOS).
• When you’re done, you can close the notebook by clicking on the “Quit” button in the Jupyter Dashboard or by closing the browser tab.

The post Python Programming Class 11 MCQ Online Test appeared first on Learn CBSE.

Students find these Class 11 AI MCQ Chapter 4 Introduction to Capstone Project Class 11 MCQ Online Test with Answers helpful for self-assessment and preparation.

Class 11 Introduction to Capstone Project MCQ

MCQ on Introduction to Capstone Project Class 11

Class 11 Introduction to Capstone Project MCQ – Introduction to Capstone Project MCQ Class 11

Multiple Choice Questions

Question 1.
Unstructured data cannot be stored in a __________ which makes it difficult to process and analyze.
(a) DBMS
(b) RDBMS
(c) My SQL
(d) Ms Excel
Answer:
(b) RDBMS

Question 2.
What are the sources of AI bias?
(a) Societal bias
(b) Ethical concerns
(c) Computing power
(d) Cyber security
Answer:
(a) Societal bias

Question 3.
How many sources of AI bias are?
(a) Three
(b) One
(C) Four
(d) None of the above
Answer:
(a) Three

Question 4.
What was the name of the Artificial Intelligence chatbot that was originally launched by Microsoft corporation on Twitter march 23, 2016?
(a) Excel
(b) Aid
(c) Tay
(d) All of the above
Answer:
(c) Tay

Question 5.
Which of these is supposed to be unbiased?
(a) Data
(b) Internet
(C) Employment
(d) Technology
Answer:
(d) Technology

Question 6.
When Artificial Intelligence algorithms become more powerful what issues does it create?
(a) Cyber security
(b) Trust related problem
(C) Job loss problem
(d) Computing power
Answer:
(b) Trust related problem

Question 7.
Which is NOT correct about the Capstone project?
a. It is a project where students must research independently
b. Students can integrate all their knowledge to develop a solution to the problem
c. It is the final project of an academic program.
d. Students are made to look at real world situations, exchange their point of view and discuss potential solutions to the problem.
Answer:
a. It is a project where students must research independently

Question 8.
Which is the fundamental question which is asked before deciding to embark on an AI development journey?
a. Should we do the project?
b. Is there a solution?
c. Can we leave the problem?
d. Is there a pattern?
Answer:
d. Is there a pattern?

Question 9.
The question, ‘which option should be taken?’ comes under which category?
a. Classification
b. Regression
c. Anomaly detection
d. Recommendation
Answer:
d. Recommendation

Question 10.
Which stage in Design Thinking is missing (Prototype, Ideate, Test, Define)?
a. Evaluation
b. Empathise
c. Evolution
d. Enrichment
Answer:
b. Empathise

Question 11.
Real computational tasks are complicated. To accomplish them, you need to __________ before coding.
a. Test the problem causing variables
b. Ask for finance help from stakeholders
c. Break down the problem into smaller units
d. write it in a paper and read it to your partners
Answer:
c. Break down the problem into smaller units

Question 12
Which one is not a part of the 5 W 1 H method?
a. Who
b. While
c. Where
d. When
Answer:
b. While

Question 13.
__________ is the process of generating ideas and solutions through sessions such as sketching, brainstorming etc.
a. Define
b. Empathise
c. Ideate
d. Prototype
Answer:
c. Ideate

Question 14.
Which one among the following is known as individual brainstorming?
a. Brain writing
b. Brain dump
c. Brainstorm
d. Brain explore
Answer:
a. Brain writing

Question 15.
__________ is a useful process during ideation.
a. Storyboarding
b. Empathy map
c. Prototyping
d. Data collection
Answer:
a. Storyboarding

Question 16.
The __________ stage involves creating a model designed to solve consumer’s problems in Design Thinking.
a. Modeling
b. Prototype
c. Training
d. Deployment
Answer:
b. Prototype

Question 17.
__________ Analysis helps in identifying the patterns by asking different questions which fall into different approaches.
a. Predictive
b. Prescriptive
c. Probability
d. Prototype
Answer:
a. Predictive

Question 18.
What is the primary purpose of a Capstone project?
A. To learn basic programming skills
B. To deeply research a specific topic and integrate knowledge
C. To practice solo project management
D. To conduct laboratory experiments
Answer:
B. To deeply research a specific topic and integrate knowledge

Question 19.
During a Capstone project, students learn skills such as:
A. How to work alone efficiently
B. How to manage large teams effectively
C. How to ethically gather and use data
D. How to perform physical experiments
Answer:
C. How to ethically gather and use data

Question 20.
Which stage of Design Thinking involves empathizing with users to understand their needs?
A. Ideate
B. Prototype
C. Empathize
D. Define
Answer:
C. Empathize

Question 21.
What is the purpose of creating an Empathy Map in Design Thinking?
A. To generate a wide range of ideas
B. To prototype solutions quickly
C. To understand users’ needs and behaviors
D. To define the problem clearly
Answer:
C. To understand users’ needs and behaviors

Question 22.
Which Sustainable Development Goal (SDG) focuses on ‘Zero Hunger’?
A. SDG 2
B. SDG 7
C. SDG 5
D. SDG 12
Answer:
A. SDG 2

Question 23.
Which AI technique is used to group similar items together based on their characteristics?
A. Classification
B. Regression
C. Clustering
D. Anomaly Detection
Answer:
C. Clustering

Question 24.
What does ‘critical thinking’ involve?
A. Generating innovative ideas
B. Making reasoned judgments based on facts
C. Testing hypotheses in a controlled environment
D. Creating physical prototypes
Answer:
B. Making reasoned judgments based on facts

Question 25.
During Problem Decomposition, what is the purpose of breaking down complicated issues into smaller parts?
A. To generate new ideas
B. To understand the problem deeply
C. To manage project timelines effectively
D. To test hypotheses iteratively
Answer:
B. To understand the problem deeply

Question 26.
In the Design Thinking process, what does the ‘Prototype’ stage aim to achieve?
A. To understand users’ needs deeply
B. To define the problem clearly
C. To create a model to test with users
D. To generate as many ideas as possible
Answer:
C. To create a model to test with users

Question 27.
What do the initials ‘SDG’ stand for in the context of global development goals?
A. Sustainable Development Goals
B. Social Development Goals
C. Strategic Development Goals
D. Sustainable Design Goals
Answer:
A. Sustainable Development Goals

Assertion-reasoning Based Questions

Study the two statements labeled as assertion (a) and reason (r).
Point out if:
(a) Both, a and r, are true and r is the correct explanation of a
(b) Both, a and r, are true but r is not the correct explanation of a
(c) If a is true but r is false
(d) If a is false but r is true

Question 1.
Assertion (A) AI produces skewed or leaned output.
Reason (R) AI algorithm are created by individuals who may have conscious or unconscious preferences that was undiscovered until the algorithm were used publicly.
Answer:
(a) Both, a and r, are true and r is the correct explanation of a

Question 2.
Assertion (A) AI tool for recruitment developed by Amazon was a failure.
Reason (B) The tool was meant to recruit only men.
Answer:
(c) If a is true but r is false

Question 3.
Assertion (A) AI is essentially a ‘black box’ to humans.
Reason (B AI system is so complicated that a user cannot understand how it works resulting to trust its decision making capability.
Answer:
(a) Both, a and r, are true and r is the correct explanation of a

Question 4.
Assertion (A) Robots are hired for monotonous and repetitive jobs.
Reason (R) Human get easily carried away by internal and external factors.
Answer:
(a) Both, a and r, are true and r is the correct explanation of a

The post Introduction to Capstone Project Class 11 MCQ Online Test appeared first on Learn CBSE.

Students find these Class 11 AI MCQ Chapter 5 Data Literacy Data Collection to Data Analysis Class 11 MCQ Online Test with Answers helpful for self-assessment and preparation.

Class 11 AI Data Literacy Data Collection to Data Analysis MCQ

MCQ on Data Literacy Data Collection to Data Analysis Class 11

Class 11 Data Literacy Data Collection to Data Analysis MCQ – Data Literacy Data Collection to Data Analysis MCQ Class 11

Multiple Choice Questions

Question 1.
Which one of the following is not a crucial component of a data interpreting?
(a) Narrative
(b) Human experience
(c) Data
(d) Visuals
Answer:
(b) Human experience

Question 2.
Storytelling is a _________.
(a) Distributive
(b) Commutative
(c) Communicative
(d) All of these
Answer:
(c) Communicative

Question 3.
The process of creating and classifying variables is called _________.
(a) Data wrangling
(b) Data enlighten
(c) Data analysis
(d) None of these
Answer:
(a) Data wrangling

Question 4.
Which of these offers a range of tools for data visualization and basic to advanced analysis?
(a) Data mining
(b) Data analysis
(C) Data wrangling
(d) Either (a) or (b)
Answer:
(b) Data analysis

Question 5.
The act of narrating a storyreferred to as storytelling.
(a) Compelling
(b) Narratively
(c) Both (a) and (b)
(d) Intensely
Answer:
(b) Narratively

Question 6.
Which of the following best defines data literacy?
A) The ability to read and write data
B) The ability to find and use data effectively
C) The ability to unalyse data using AI
D) The ability to collect and store data securely
Answer:
B) The ability to find and use data effectively

Question 7.
What is the purpose of data preprocessing?
A) To make data more complex
B) To make data less accessible
C) To clean and prepare data for analysis
D) To increase the size of the dataset
Answer:
C) To clean and prepare data for analysis

Question 8.
How can missing data be handled in a dataset?
A) By ignoring it
B) By replacing missing values with estimates
C) By deleting rows or columns with missing values
D) By converting missing values to zero
Answer:
B) By replacing missing values with estimates

Question 9.
Which of the following statements about the quantity of data needed for machine learning projects is true?
A) More data is always better for good predictions.
B) Small batches of data are sufficient for complex models.
C) Data quantity depends solely on the number of features.
D) Data diversity is not essential for model performance.
Answer:
A) More data is always better for good predictions.

Question 10.
Which of the following is an example of a primary source of data collection?
A) Web scraping
B) Social media data tracking
C) Surveys
D) Kaggle datasets
Answer:
C) Surveys

Question 11.
What method of data collection involves direct communication with individuals or groups to gather information?
A) Observations
B) Experiments
C) Interviews
D) Marketing campaigns
Answer:
C) Interviews

Question 12.
Which of the following is an example of ratio scale data?
A) Grading students’ exam papers as “A,” “B,” “C,” “D,” and “F”
B) Measuring the temperature in Celsius
C) Rating a meal at a restaurant as “unpalatable,” “unappetizing,” “just okay,” “tasty,” and “delicious”
D) Recording the weight of a person in kilograms
Answer:
C) Rating a meal at a restaurant as “unpalatable,” “unappetizing,” “just okay,” “tasty,” and “delicious”

Question 13.
What is the distinguishing feature of ratio scale data?
A) It involves categories without a specific order
B) It has a zero point and allows for ratios to be calculated
C) It involves categories with a strict order but no measurable differences between categories
D) It has a definite order, but the differences between categories cannot be measured
Answer:
B) It has a zero point and allows for ratios to be calculated

Question 14.
Which statistical measure is most suitable for data sets with evenly spread values and no exceptionally high or low values?
A) Mean
B) Median
C) Mode
D) Variance
Answer:
A) Mean

Question 15.
What is the term used to describe the graphical or pictorial representation of data?
A) Statistical summary
B) Data organization
C) Data visualization
D) Data interpretation
Answer:
C) Data visualization

Question 16.
What does data literacy involve?
A) Only collecting data
B) Understanding, evaluating, and using data effectively
C) Storing data securely
D) Ignoring data quality
Answer:
B) Understanding, evaluating, and using data effectively

Question 17.
Which type of data collection method involves scraping product information from websites?
A) Survey
B) Experiment
C) Web Scraping
D) Observation
Answer:
C) Web Scraping

Question 18.
In data exploration, which level of measurement allows for ranking data but does not have equal intervals?
A) Nominal
B) Ordinal
C) Interval
D) Ratio
Answer:
B) Ordinal

Question 19.
What statistical measure is used to summarize the central value of a dataset?
A) Mean
B) Mode
C) Median
D) All of the above
Answer:
D) All of the above

Assertion-Reasoning Based Questions

Study the two statements labeled as assertion (a) and reason (r).
Point out if:
(a) Both, a and r, are true and r is the correct explanation of a
(b) Both, a and r, are true but r is not the correct explanation of a
(c) If a is true but r is false
(d) If a is false but r is true

Question 1.
Assertion (A) Stories have the power to inspire, motivate and can change peoples opinion.
Reason (R) A good story has a chemical effect on the brain resulting to absorb, digest and store information.
Answer:
(a) Both, a and r, are true and r is the correct explanation of a

Question 2.
Assertion (A) Story telling is only used in some cultures.
Reason (R) Story telling enhances global networking by increasing the awareness about the cultural differences and enhancing cross- cultural understanding.
Answer:
(d) If a is false but r is true

Question 3.
Assertion (A) In story telling information is presented in a structured manner.
Reason (R Story telling persuade people to execute plans towards a certain future or objective.
Answer:
(a) Both, a and r, are true and r is the correct explanation of a

The post Data Literacy Data Collection to Data Analysis Class 11 MCQ Online Test appeared first on Learn CBSE.

Students find these Class 11 AI MCQ Chapter 6 Machine Learning Algorithms Class 11 MCQ Online Test with Answers helpful for self-assessment and preparation.

Class 11 Machine Learning Algorithms MCQ

MCQ on AI Machine Learning Algorithms Class 11

Class 11 Machine Learning Algorithms MCQ – Machine Learning Algorithms MCQ Class 11

Multiple Choice Questions

Question 1.
An empathy map’s “does quadrant” shows the persona’s “; _____
a-Actions
b-Thoughts
c-Feelings
d-Statements
Answer:
a-Actions

Question 2.
Which of the following doesn’t belong in the design thinking process?
a-Define
b-Test
c-Data scoping
d-Ideate
Answer:
c-Data scoping

Question 3.
Which of the following, by using the appropriate questions, helps to gather specific information regarding issues and user expectations?
a – SW1 H
c – SH1W
Answer:
a – SW1 H

Question 4.
Through _____ we can think about others and learn how they feel about their issues, circumstances, or situations.
a-Empathy
b-Prototype building
c-Design thinking
d -Testing
Answer:
a-Empathy

Question 5.
Design Thinking is _____ , which implies that it bases its decisions on actual consumer behaviour as opposed to how businesses anticipate customers will behave.
a-People-oriented
b-Process-oriented
c-Product-oriented
d-Both (a) and (c)
Answer:
a-People-oriented

Question 6.
Which of the following are the types of correlation?
a. Positive correlation
b. Negative Correlation
c. No correlation
d. All of the above
Answer:
d. All of the above

Question 7.
Which of the following techniques is an analysis of the relationship between two variables to provide the prediction mechanism?
a. Standard error
b. Correlation
c. Regression
d. None of the above
Answer:
c. Regression

Question 8.
Which of the given plots is suitable for testing the linear relationship between a dependent and independent variable?
a. Bar chart
b. Scatter plot
c. Histograms
d. All of the above
Answer:
b. Scatter plot

Question 9.
Which of the following scatter plots represents a positive correlation?
a. points scattered randomly with no apparent trend
b. points forming a diagonal line and bottom left to top right
c. points forming a diagonal line from top left to bottom right
d. points clustered around a central point
Answer:
b. points forming a diagonal line and bottom left to top right

Question 10.
Which regression technique is used when there is only one independent variable?
a. logistic regression
b. multiple linear regression
c. simple linear regression
d. polynomial regression
Answer:
c. simple linear regression

Question 11.
What is one advantage of linear regression analysis?
a. it is robust to outliers
b. it can capture nonlinear relationships between variables
c. it is simple and easy to interpret
d. it is suitable for classification tasks
Answer:
c. it is simple and easy to interpret

Question 12.
What is supervised learning in Artificial Intelligence?
a. training a computer algorithm on input data that is not labelled.
b. training a computer algorithm on input data that has been labelled for a specific output.
c. training a computer algorithm without any input data
d. training a computer algorithm to perform unsupervised tasks
Answer:
b. training a computer algorithm on input data that has been labelled for a specific output.

Question 13.
Which type of classification involves categorizing data into two distinct classes?
a. multi-class classification
b. binary classification
c. unsupervised classification
d. regression classification
Answer:
b. binary classification

Question 14.
What is logistic regression commonly used for in binary classification?
a. categorizing observations into multiple classes
b. predicting continuous values for input data
c. categorizing observations into two distinct classes
d. identifying unstructured data patterns
Answer:
c. categorizing observations into two distinct classes

Question 15.
What is the primary goal of classification in AI?
a. categorizing data into random groups
b. locating and classifying things or concepts into predefined groups
c. predicting continuous values for input data
d. identifying unstructured data patterns
Answer:
b. locating and classifying things or concepts into predefined groups

Question 16.
Which algorithm is commonly used for binary classification?
a. Decision trees
b. Support Vector Machine
c. Logistic Regression
d. k-Nearest Neighbors
Answer:
c. Logistic Regression

Question 17.
The K-Nearest Neighbors (KNN) algorithm assigns a class to new data point by considering:
a. Distance from the data point to a predefined decision boundary
b. Majority vote of its K nearest neighbors in the training data
c. Similarity of the data point to a cluster centroid
d. probability of each class given the data point’s features.
Answer:
b. Majority vote of its K nearest neighbors in the training data

Question 18.
What does a classification model in AI ultimately want to achieve?
a. to identify patterns and associations in data
b. to predict continuous numerical values
c. to categorize input data into predefined classes or labels
d. to optimize decision-making processes
Answer:
c. to categorize input data into predefined classes or labels

Question 19.
What are some challenges in applying classification models to real-world problems?
a. Data bias and fairness
b. Interpretability and explainability
c. overfitting and underfitting
d. All of the above
Answer:
d. All of the above

Question 20.
What is clustering?
a. Grouping labeled dataset
b. Dividing data into different clusters
c. Finding linear association between variables
d. Predicting future behaviors of a dependent variable
Answer:
b. Dividing data into different clusters

Question 21.
Which type of learning does clustering belong to?
a. Supervised learning
b. Unsupervised learning
c. Semi-supervised learning
d. Reinforcement learning
Answer:
b. Unsupervised learning

Question 22.
Which method is used to group highly dense areas into clusters?
a. Partitioning clustering
b. Density-based clustering
c. Distribution model-based clustering
d. Hierarchical clustering
Answer:
c. Distribution model-based clustering

Question 23.
Which algorithm is an example of partitioning clustering?
a. Mean-shift algorithm
b. DBSCAN algorithm
c. K-Means algorithm
d. Fuzzy clustering algorithm
Answer:
c. K-Means algorithm

Question 24.
Which clustering method allows data objects to belong to more than one group or cluster?
a. Partitioning clustering
b. Density-based clustering
c. Distribution model-based clustering
d. Fuzzy clustering
Answer:
d. Fuzzy clustering

Question 25.
Which clustering algorithm is sensitive to outliers?
a. K-Means algorithm
b. Mean-shift algorithm
c. DBSCAN algorithm
d. Hierarchical clustering
Answer:
a. K-Means algorithm

Assertion-Reasoning Based Questions

Study the two statements labeled as assertion (a) and reason (r).
Point out if:
(a) Both, a and r, are true and r is the correct explanation of a
(b) Both, a and r, are true but r is not the correct explanation of a
(c) If a is true but r is false
(d) If a is false but r is true

Question 1.
Assertion (A) dn Ideation stage one must not be concerned whether the generated ideas are actually feasible or not.
Reason (R) Numerous ideas are generate as a part of the problem solving phase.
Answer:
(a) Both, a and r, are true and r is the correct explanation of a

Question 2.
Assertion (A) Creating a prototype is not a whole process.
Reason (R) In prototype stage a model of actual consumers problem is created.
Answer:
(a) Both, a and r, are true and r is the correct explanation of a

Question 3.
Assertion (A) In design thinking framework designers are expected to ask a lot of questions to the customers/users.
Reason (B) Designers usually follow 5W1H method.
Answer:
(b) Both, a and r, are true but r is not the correct explanation of a

The post Machine Learning Algorithms Class 11 MCQ Online Test appeared first on Learn CBSE.

Students find these Class 11 AI MCQ Chapter 7 Leveraging Linguistics and Computer Science Class 11 MCQ Online Test with Answers helpful for self-assessment and preparation.

Class 11 Leveraging Linguistics and Computer Science MCQ

MCQ on AI Leveraging Linguistics and Computer Science Class 11

Class 11 Leveraging Linguistics and Computer Science MCQ – Leveraging Linguistics and Computer Science MCQ Class 11

Multiple Choice Questions

Question 1.
Which of the following offers a simple way to compare amounts across different categories using a graphical representation?
a-Scatter plot
b-Bar graph
c -Line chart
d-Pie chart
Answer:
b-Bar graph

Question 2.
Determine the kind of association in the statement, “Pollution is effecting my health.”
a-No correlation
b-Positive correlation
c-Negative correlation
d-Either (b) or (c)
Answer:
c-Negative correlation

Question 3.
Out of the following, which one is NOT a categorical data example?
a-Gender
b-Age group
c-Pin code
d-Height
Answer:
d-Height

Question 4.
Select the correct format for the date 02/22/2018.
a-mm/dd/yyyy
b-dd/mm/yyyy
c-dd/mm/yy
d-mm/ dd/ yy
Answer:
a-mm/dd/yyyy

Question 5.
What is NOT a source of structured data among the following?
a-Audio files
b-DBMS
c- Excel file
d-Online forms
Answer:
a-Audio files

Question 6.
Which of the following is NOT a common task in NLP?
a) Machine translation
b) Text summarization
c) Speech recognition
d) Image recognition
Answer:
d) Image recognition

Question 7.
What is the main challenge/s of NLP?
a) Handling Ambiguity of Sentences
b) Handling Tokenization
c) Handling POS-Tagging
d) All of the mentioned
Answer:
a) Handling Ambiguity of Sentences

Question 8.
What is a chatbot?
a) A physical robot used for chatting purposes.
b) A computer program designed to simulate conversation with human users, especially over the internet.
c) An advanced form of search engine.
d) A tool used for sending automated emails.
Answer:
b) A computer program designed to simulate conversation with human users, especially over the internet.

Question 9.
Which of the following is an application of Natural Language Processing (NLP)?
a. Autonomous vehicles
b. Predicting stock prices
c. Sentiment analysis
d. Virtual reality gaming
Answer:
c. Sentiment analysis

Question 10.
Which of the following statements about Voice Recognition Interfaces is true?
a. They solely rely on text-based inputs.
b. They are incapable of understanding multiple languages.
c. They convert spoken language into text or commands.
d. They require a physical keyboard for interaction.
Answer:
c. They convert spoken language into text or commands.

Question 11.
What is the primary focus of linguistics?
A) The study of human behavior
B) The strategic application of linguistic principles and practices to meet specific objectives
C) The development of computer algorithms
D) The analyşis of mathematical structures
Answer:
B) The strategic application of linguistic principles and practices to meet specific objectives

Question 12.
What does NLP stand for?
A) Natural Light Processing
B) Natural Language Processing
C) Neural Language Programming
D) Networked Language Protocol
Answer:
B) Natural Language Processing

Question 13.
Which of the following is NOT an example of NLP applications?
A) Email spam filtering
B) Virtual assistants
C) Document summarization
D) Image processing
Answer:
D) Image processing

Question 14.
Introduction to Natural Language Processing
What is tokenization in NLP?
A) Converting speech to text
B) Splitting text into smaller parts for easier classification
C) Translating text to another language
D) Analyzing the tone of a document
Answer:
B) Splitting text into smaller parts for easier classification

Question 15.
In the sentence “I shot an elephant in my pajamas,” which word is considered an entity?
A) Shot
B) In
C) Elephant
D) My
Answer:
C) Elephant

Question 16.
Emotion Detection and Sentiment Analysis
What does emotion detection identify?
A) The topic of a conversation
B) Distinct human emotion types
C) The grammatical structure of a sentence
D) The language spoken by the user
Answer:
B) Distinct human emotion types

Question 17.
Which of the following best describes sentiment analysis?
A) Identifying parts of speech in a sentence
B) Measuring the strength of an emotion
C) Translating text between languages
D) Summarizing a large document
Answer:
B) Measuring the strength of an emotion

Question 18.
What is a rule-based chatbot?
A) A chatbot that uses natural language processing and machine learning
B) A chatbot that operates on predefined rules and decision trees
C) A chatbot that can learn from user interactions
D) A chatbot designed for entertainment purposes only
Answer:
B) A chatbot that operates on predefined rules and decision trees

Question 19.
Which of the following is a component of a chatbot’s backend?
A) User interface
B) Application logic and memory
C) Voice recognition
D) Email filtering
Answer:
B) Application logic and memory

Question 20.
NLP Processing Steps
What is lexical analysis?
A) The study of word meanings in context
B) Understanding and examining the structure of words in a language
C) Checking grammar and word relationships
D) Analyzing sentence tone and sentiment
Answer:
B) Understanding and examining the structure of words in a language

Question 21.
What does syntactical analysis check?
A) Grammar, word layouts, and word relationships
B) The tone of the text
C) The emotional content of a sentence
D) The context of preceding sentences
Answer:
A) Grammar, word layouts, and word relationships

Question 22.
Applications of NLP
What is the purpose of sentiment analysis in NLP?
A) To translate text between different languages
B) To evaluate consumer comments and social media posts
C) To filter out spam emails
D) To summarize long documents
Answer:
B) To evaluate consumer comments and social media posts

Question 23.
How do voice assistants like Siri and Alexa work?
A) By using voice recognition, natural language interpretation, and NLP
B) By manually processing user commands
C) By analyzing user behavior patterns
D) By using image recognition software
Answer:
A) By using voice recognition, natural language interpretation, and NLP

Assertion-Reasoning Based Questions

Study the two statements labeled as assertion (a) and reason (r).
Point out if:
(a) Both, a and r, are true and r is the correct explanation of a
(b) Both, a and r, are true but r is not the correct explanation of a
(c) If a is true but r is false
(d) If a is false but r is true

Question 1.
Assertion (A) Structured data can be stored and retrieved easily in a database.
Reason (R) Structured data is highly organized and easily understood by machine language.
Answer:
(b) Both, a and r, are true but r is not the correct explanation of a

Question 2.
Assertion (A) Only average of a numerical data is calculated.
Reason (R) A persons gender, language, religion are considered as categorical data type.
Answer:
(b) Both, a and r, are true but r is not the correct explanation of a

Question 3.
Assertion (A) The common form of data representation is graphical way.
Reason (R) Non graphical techniques are suitable for deriving a conclusion after analyzing the set of data.
Answer:
(c) If a is true but r is false

Question 4.
Assertion (A): Linguistics can be strategically applied in marketing, advertising, and education.
Reason (R): Understanding the structure, semantics, pragmatics, and sociolinguistics of language helps tailor messages and improve communication.
Answer:
(a) Both, a and r, are true and r is the correct explanation of a

Question 5.
Assertion (A): NLP is a branch of AI that allows computers to understand and manipulate human speech.
Reason (R): NLP can only be applied to written text, not speech.
Answer:
(c) If a is true but r is false

Question 6.
Assertion (A): Sentiment analysis measures the strength of an emotion.
Reason (R): Sentiment analysis is used to identify distinct human emotion types such as anger or happiness.
Answer:
(c) If a is true but r is false

Question 7.
Assertion (A): AI-powered chatbots utilize natural language processing (NLP) and machine learning algorithms.
Reason (R): Rule-based chatbots follow programmed rules to respond to user input without using NLP or machine learning.
Answer:
(b) Both, a and r, are true but r is not the correct explanation of a

Question 8.
Assertion (A): Syntactical analysis involves checking grammar, word layouts, and word relationships.
Reason (R): Semantic analysis is used to break down text into words and phrases.
Answer:
(c) If a is true but r is false

Question 9.
Assertion (A): Voice assistants like Siri and Alexa use NLP to understand and respond to human speech.
Reason (R): Voice assistants are only capable of making calls and creating reminders without understanding the spoken language.
Answer:
(c) If a is true but r is false

The post Leveraging Linguistics and Computer Science Class 11 MCQ Online Test appeared first on Learn CBSE.

Students find these Class 11 AI MCQ Chapter 8 AI Ethics and Values Class 11 MCQ Online Test with Answers helpful for self-assessment and preparation.

Class 11 AI Ethics and Values MCQ

MCQ on AI Ethics and Values Class 11

Class 11 AI Ethics and Values MCQ – AI Ethics and Values MCQ Class 11

Multiple Choice Questions

Question 1.
Of the following, which one is NOT appropriate for analyzing the correlation between two quantitative variables?
a. Contingency table
b. Bar chart
c. Correlation
d. Scatterplot
Answer:
b. Bar chart

Question 2.
What is the correct statement regarding residual error?
a-Higher value is better
b-Either a or b
c -Lower value is better
d -All of these
Answer:
c -Lower value is better

Question 3.
Which of the following describes a drawback when analyzing a dataset with a regression line?
a-Presence of one or more outliers
b-Inappropriately group combining
c-Curvilinear data
d -Response variable is qualitative –
Answer:
a-Presence of one or more outliers

Question 4.
In the simple linear regression equation, the symbol x represents the
a- Independent variable
b-Estimated intercept
c-Estimated or predicted response
d-Estimated slope
Answer:
a- Independent variable

Question 5.
m stands for ______ in the linear regression equation y=mx+b.
a-Estimated intercept
b-Independent variable
c-Estimated or predicted response
d-Estimated slope
Answer:
d-Estimated slope

Question 6.
What is the primary focus of AI ethics?
a. Ensuring AI systems are efficient and error-free
b. Guiding the development and use of AI technologies according to ethical principles
c. Maximizing the profitability of AI technologies
d. Focusing solely on the technological advancements in AI
Answer:
d. Focusing solely on the technological advancements in AI

Question 7.
Which ethical principle is concerned with fairness and justice in AI systems?
a. Transparency
b. Accountability
c. Bias and fairness
d. Privacy
Answer:
c. Bias and fairness

Question 8.
What role does transparency play in AI ethics?
a. Ensuring AI systems are invisible to users
b. Making the AI decision-making process clear and understandable
c. Keeping the AI technologies a secret
d. None of the above
Answer:
b. Making the AI decision-making process clear and understandable

Question 9.
What is a major ethical concern related to AI and privacy?
a. AI systems improving data security automatically
b. AI technologies accessing and using personal data without consent
c. AI enhancing user privacy by default
d. None of the above
Answer:
b. AI technologies accessing and using personal data without consent

Question 10.
How can bias in AI systems impact society?
a. By making AI systems more efficient
b. By perpetuating and exacerbating existing inequalities
c. By improving fairness and justice automatically
d. None of the above
Answer:
b. By perpetuating and exacerbating existing inequalities

Question 11.
Which of the following strategies is suggested for mitigating bias in AI systems?
a. Ignoring the bias
b. Increasing the complexity of AI algorithms
c. Regular audits and inclusive data practices
d. Focusing solely on the technological aspects
Answer:
c. Regular audits and inclusive data practices

Question 12.
What is the purpose of ethical frameworks and guidelines in AI?
a. To restrict the development of AI technologies
b. To ensure AI development aligns with human values and ethical principles
c. To make AI systems less transparent
d. None of the above

Question 13.
Who is responsible for ensuring the ethical use of AI systems?
a. AI developers only
b. Government bodies only
c. Users only
d. All stakeholders, including developers, governments, and users
Answer:
d. All stakeholders, including developers, governments, and users

Question 14.
What is an example of AI assisting humanity as mentioned in the unit?
a. Reducing energy efficiency in buildings
b. Enhancing mental health care through AI-powered chatbots
c. Increasing bias in hiring processes
d. Decreasing crop yield predictions accuracy
Answer:
b. Enhancing mental health care through AI-powered chatbots

Question 15.
What is the role of AI ethics in the context of autonomous vehicles?
a. To ensure vehicles are aesthetically pleasing
b. To navigate ethical dilemmas such as decision-making in critical situations
c. To make autonomous vehicles less safe
d. None of the above
Answer:
b. To navigate ethical dilemmas such as decision-making in critical situations

Question 16.
What is AI ethics primarily concerned with?
A) Enhancing computational speed
B) Ensuring fair and accountable AI use
C) Developing complex algorithms
D) Reducing data privacy
Answer:
B) Ensuring fair and accountable AI use

Question 17.
What does bias in AI refer to?
A) Unfair or inaccurate decisions due to flawed data or assumptions
B) High computational efficiency
C) Transparent decision-making processes
D) Robust and reliable AI systems
Answer:
A) Unfair or inaccurate decisions due to flawed data or assumptions

Question 18.
Which of the following is not one of the pillars of AI ethics?
A) Explainability
B) Fairness
C) Efficiency
D) Privacy
Answer:
C) Efficiency

Question 19.
What is the purpose of the Moral Machine game?
A) To teach coding skills
B) To explore ethical dilemmas in AI decision-making
C) To develop AI algorithms
D) To promote AI safety
Answer:
B) To explore ethical dilemmas in AI decision-making

Question 20.
How can bias in AI systems be mitigated?
A) By using diverse data and fair algorithms
B) By increasing computational complexity
C) By ignoring ethical considerations
D) By reducing transparency
Answer:
A) By using diverse data and fair algorithms

Question 21.
Which of the following is an example of AI bias in healthcare?
A) Predictive policing algorithms
B) Credit scoring systems
C) Lower accuracy for black patients in diagnostic AI
D) Facial recognition errors
Answer:
C) Lower accuracy for black patients in diagnostic AI

Question 22.
What is the significance of developing AI policies?
A) To increase AI bias
B) To decrease transparency
C) To ensure responsible AI use and public trust
D) To discourage AI innovation
Answer:
C) To ensure responsible AI use and public trust

Question 23.
What does the term “ethical dilemma” refer to in the context of AI ?
A) Unsolvable AI problems
B) Situations with conflicting moral principles or choices
C) High AI performance metrics
D) Theoretical AI concepts
Answer:
B) Situations with conflicting moral principles or choices

Assertion-Reasoning Based Questions

Study the two statements labeled as assertion (a) and reason (r).
Point out if:
(a) Both, a and r, are true and r is the correct explanation of a
(b) Both, a and r, are true but r is not the correct explanation of a
(c) If a is true but r is false
(d) If a is false but r is true

Question 1.
Assertion (A): Regressions is used as a statistical tool for finding out the relationship between a dependent variable and an independent variable.
Reason (R) : In regression, the independent variable is the function of the dependent variable.
Answer:
(c) If a is true but r is false

Question 2.
Assertion (A):The Pearson correlation coefficient, r, can take a range of values from +1 to -1.
Reason(R):The Pearson’s correlation coefficient ‘ r ‘ shows the association of the two variables, whether positive or negative.
Answer:
(a) Both, a and r, are true and r is the correct explanation of a

Question 3.
Assertion (A): Pearson’s correlation co-efficient might result to a valid result, if the variables are measured at the continuous level.
Reason (R): The linear relationship between two variables is calculated using Scatterplot.
Answer:
(b) Both, a and r, are true but r is not the correct explanation of a

Question 4.
Assertion (A):Pearson’s correlation coefficient has no outliers.
Reason (R): Outliers are single data points within your data.
Answer:
(a) Both, a and r, are true and r is the correct explanation of a

The post AI Ethics and Values Class 11 MCQ Online Test appeared first on Learn CBSE.

These Class 11 AI Important Questions Chapter 1 Communication Skills Class 11 Important Questions and Answers NCERT Solutions Pdf help in building a strong foundation in artificial intelligence.

Communication Skills Class 11 Important Questions

Class 11 AI Communication Skills Important Questions

Important Questions of Communication Skills Class 11 – Class 11 Communication Skills Important Questions

Communication Skills Class 11 Very Short Answer Type Questions

Question 1.
What is communication style?
Answer:
A communication style includes both verbal and non-verbal skills. Correct communication style is more effective as other person can understand it in a better way.

Question 2.
What are the four types of communication style?
Answer:
The four styles of communication are:

• Passive
• Passive aggressive
• Aggressive
• Assertive

Question 3.
What are the disadvantage of non-verbal communication?
Answer:
Some of the disadvantage of non verbal communications are:

• Long conversation is cumbersome.
• It varies from culture to culture.
• Particularities of the message is not clear.
• It is difficult to understand.
• Not considered as a formal way of communication
• Less influential and cannot be used everywhere

Question 4.
What is AEIOU model in communication?
Answer:

• A-Ask questions about the importance of the task.
• E-Express your feelings, Be polite.
• I-Inform the other person your reasons for saying ‘No’.
• O-Offer other options.
• U-Reach a mutual understanding.

Question 5.
What are the “7Cs” of effective communication stand for?
Answer:
Clear, Concise, Concrete, Correct, Coherent, Complete, Courteous

Question 6.
How many ways are there to communicate?
Answer:
Non-verbal, Verbal and Visual

Question 7.
What methods should we follow to avoid fear in Public speaking?
Answer:
One can use the 3 Ps (Prepare, Practice, Perform) method to get over your fears, and become a confident and effective speaker.

Question 8.
Meaning of phonetics?
Answer:
We use sounds to speak. Every word is made up of one or more sounds. We put these sounds together to pronounce words. Phonetics is the study of the sounds that we make when we speak.

Question 9.
What are connecting words conjunctions?
Answer:
And’, ‘but’, ‘or’ such words are called ‘conjunctions’ and are used to connect two sentences.

Question 10.
Definition of sentence types.
Answer:
There are four types of English sentence, classified by their purpose: declarative sentence (statement) interrogative sentence (question) imperative sentence (command) exclamative sentence (exclamation).

Question 11.
What is Noun?
Answer:
A noun is a ‘naming’ word that is used for a person, place, thing, or idea. Some examples of nouns are Ali, Delhi, football and music.

Communication Skills Class 11 Short Answer Type Questions

Question 1.
Define visual communication?
Answer:
Visual communication, which involves sending and understanding messages only through images or pictures. The main advantage of this type of communication is that you do not need to know any particular language for understanding it.

Question 2.
What is Greetings?
Answer:
There are two types of Greetings. Formal greetings and Informal greetings.
Formal greetings- If you don’t know the person, you should use a formal greeting. It is used to greet senior citizens, the elderly, and formal acquaintances like coworkers and clients. In workplaces, companies, meetings, and colleges, this is utilized more frequently.
Informal greetings- Informal greetings are used when you talk to friends, family or know the person very well.

Communication Skills Class 11 Long Answer Type Questions

Question 1.
Compare all the methods of communication. Stating their advantage and disadvantages. Answer:
There are different methods of communications- Verbal, Non-verbal and visual communication. Written below their comparisons.

Question 2.
Write about different communication styles also compare their disadvantages.
Answer:

Question 3.
Define capitalization rules using ‘MINTS’ rule.
Answer:
We know that all sentences begin with capital letters. It is easy to know what to capitalize if you remember the word ‘MINTS’. MINTS is a set of simple rules that help you capitalize words correctly. Each letter in the word MINTS refers to one

Question 4.
Why punctuation is important?
Answer:
Punctuation is a series of symbols, such the comma and full stop, that aid in separating clauses in a sentence and elucidating their meaning. Punctuation follows certain principles and is equally as vital to sentences as capitalization. Here in Table, a few typical punctuation symbols are displayed.

Case-Based Questions

Question 1.
Suman, Mohit, Kamlesh, Nita and Gita are five best friend’s among these five Suman appear sweet on the outside but behind the scenes she is very sarcastic and not a loyal to her friends. What do you think which kind of communication style she is applying?
A-Assertive communication
B-Aggressive communication
C-Passive aggressive communication
D-Passive communication
Answer:
C-Passive aggressive communication

Question 2.
Suman and Mugdha appeared in an interview for the same post Suman got rejected and Mugdha selected although there is two vacancy post. After the interview both of them discussed that they were asked the same question and apparently there answers are also similar. Suman answered “no” some question directly without any thought but Mugdha though said “no ” using AEIOU model. What do you think which of the following option is correct about AEIOU model?
A-Directly say no
B-Instead of saying no ask questions
C- Engage the person in a polite statements
D- Ask questions, include a reason engage your answer in a polite way and offer other options also.
Answer:
D- Ask questions, include a reason engage your answer in a polite way and offer other options also.

Question 3.
Your friend wants you to come play basketball with him but you have not finished your homework that is due tomorrow. He insists that if you don’t’ come they will not win and it will be your fault. He keeps insisting and even asks other people to convince you to play the match. What should you do?
A- Play the match because otherwise your friend will be upset
B-Play the match and make some excuse to your teacher about the homework
C- Make an excuse that you are sick and avoid playing the match
D- Explain clearly that you need to complete your homework so you cannot play
Answer:
D- Explain clearly that you need to complete your homework so you cannot play

Question 4.
A good friend of yours is very upset as her best friend has had a terrible accident. You are tired from the day’s work. The phone rings and the caller I.D. shows that it’s her. What should you do?
A- Ignore the call because you are tired
B- Answer the call and talk to her for as long as she wants to
C-Tell her that you are too tired and she should call someone else
D- Listen to her for some time and then tell her that you will talk again tomorrow
Answer:
D- Listen to her for some time and then tell her that you will talk again tomorrow

Question 5.
You have been standing in a queue for a long time to buy tickets for a movie. A man suddenly comes and stands in front of you, out of turn. What should you do?
A-Scream at the person so that he never repeats the mistake
B- Inform him politely that there is a queue and everyone is waiting for their turn
C-Stay quiet and let the person go ahead
D- Not say anything directly but complain about him to other people
Answer:
B- Inform him politely that there is a queue and everyone is waiting for their turn

Communication Skills Class 11 Notes

Define Communication: Communication is a two-way exchange of information, i.e., giving and receiving. Speaking and writing to someone are examples of giving information. Reading and listening to someone are examples of receiving information.

7 Cs of effective Communication-

Verbal Communication- Verbal communication is the sharing of information using words.

Public Speaking- Speaking in front of a large group makes most people nervous. One can use the 3 Ps (Prepare, Practice, Perform) method to get over the fears, and become a confident and effective speaker.

Non-Verbal Communication- Non-verbal communication is the message we send to others without using any words as shown in Figure 1.4

Visual Communication- It involves sending and understanding messages only through images or pictures. The main advantage of this type of communication is that you do not need to know any particular language for understanding it.

Phonetics- We use sounds to speak. Every word is made up of one or more sounds. We put these sounds together to pronounce words. Phonetics is the study of the sounds that we make when we speak.

Using Capital- We know that all sentences begin with capital letters. But Do we know at what other points in a sentence we should use capital letters? It is easy to know what to capitalize if you remember the word ‘MINTS’. MINTS is a set of simple rules that help you capitalize words correctly. Each letter in the word MINTS refers to one capitalization rule as shown in Table 1.14.

Paragraph-A paragraph is made up of several sentences. Make sure each sentence in a paragraph you write has a common theme. Create a new paragraph if you want to talk about a different concept. If you are writing about your school, for instance, the opening paragraph could contain sentences describing the school’s name, location, size, and other relevant information. You can use sentences in the following paragraph to express your preferences for your school.

Greetings-There are two types of Greetings one is format and another is informal greetings. Formal greetings are used when you don’t know the person and informal greetings are used when you know the friend, family and know the person very well.

The post Communication Skills Class 11 Questions and Answers appeared first on Learn CBSE.

These Class 11 AI Important Questions Chapter 2 Self Management Skills Class 11 Important Questions and Answers NCERT Solutions Pdf help in building a strong foundation in artificial intelligence.

Self Management Skills Class 11 Important Questions

Class 11 AI Self Management Skills Important Questions

Important Questions of Self Management Skills Class 11 – Class 11 Self Management Skills Important Questions

Self Management Skills Class 11 Very Short Answer Type Questions

Question 1.
Define Grooming.
Answer:
Grooming is the process of making yourself look neat, tidy and clean.

Question 2.
What can be guidelines for dressing and grooming?
Answer:
There are certain general norms and guidelines for dressing and grooming for example : if you are a sales person, you will be formally dressed without accessories or make up but if you are a beautician, you may have to dress up fashionably and wear make up.

Question 3.
What is Personal Hygiene and how it help general life.
Answer:
Personal hygiene is the habit or practice of keeping ourselves clean. Cleanliness helps us to maintain our health and well-being. Being healthy allows you to be regular at school, college or work.

Question 4.
What is Team Work?
Answer:
A team is a group of people working together towards achieving a common goal. The process of working together in a group is TEAM WORK.

Self Management Skills Class 11 Short Answer Type Questions

Question 1.
What is Self-Motivation?
Answer:
Self-motivation is the internal power that propels you to action. We are driven to enhance our quality of life, accomplish our goals, and feel joyful by our own internal motivation. In other words, it is our capacity to carry out our duties without being influenced by anyone or anything.

Question 2.
What is goal setting and how to set Goal?
Answer:
Setting goals is crucial for both your personal and professional life.
Setting goals in your life will assist you in making decisions about how to live your life, where you want to go, and how you want to be in the future. From below mentioned picture one can set goal.

Self Management Skills Class 11 Long Answer Type Questions

Question 1.
What is Team Work and how it helps organization success.
Answer:
TEAM stands for Together, Everyone, Achieve and More. A team is a group of people working together towards achieving a common goal. Every team has a set of goals to achieve. The process of working together in a group is called as TEAM WORK. Here are the example of best Team.

Benefits of Team Work:

• Better problem solving. …
• Increased potential for innovation. …
• Happier team members. …
• Enhanced personal growth. …
• Less burnout. …
• More opportunities for growth. …
• Boosted productivity. …
• Smarter risk taking.

Question 2.
Define Networking skills and its advantages.
Answer:

• Knowing people, keeping in touch with them over time, and utilizing their expertise or ability in a way that benefits both sides is networking.
  Networking skills involve two very important skills – listening sincerely and asking thoughtful questions.
• Listening with attention shows that you respect and value what the other person is saying. Asking thoughtful questions shows that you are interested in the opinion of the other person and it helps to build trust in a relationship.
• Benefits of Networking Skills Networking helps you to make dependable and trusted friends with whom you can
• Share expertise and skills – For instance, you could ask a friend or neighbor who works for a company to assist you in getting ready for an interview.
• Seek and provide support in difficult times – For instance, if you are going to be late for class or need help finishing a project for school, ask your buddies for assistance.
• Inform and be informed about new opportunities – For instance, you could request the assistance of your parents’ coworkers to find you a summertime part-time employment.

Question 3.
What is Time management and what are the steps for effective time management?
Answer:
The ability to effectively plan and manage how you spend the hours of your day and complete what you want to achieve is known as time management. When you decide to finish your schoolwork right away after school so you have time to watch TV later in the evening, it is a good example of time management.

Four Keys to Time Management Success
Here are some pointers for putting the four principles of efficient time management into practice:

• Avoid delaying or cancelling any scheduled activities.
• Organize your space and school desk.
• Create a “NO DISTURBANCE ZONE” where you may sit and work on vital things.
• Utilize downtime productively.
• Swap out pointless activities with useful ones.

Case-Based Questions:

Question 1.
Mahadev and Ramesh are two good friends, with same degree and equivalent scores. Mahadev applies to a certain number of selected companies with a chosen role responsibility. Whereas, Ramesh applies in every company. Still, he could not get a job.
What do you think what’s the reason behind this?
a) Mahadev is more talented
b) Ramesh is lazy
c) Mahadev has done self-exploration and knows his strength.
d) Ramesh has no reference
Answer:
c) Mahadev has done self-exploration and knows his strength.

Question 2.
Radha is working as a senior manager in a reputed organization. As she has lots of role responsibilities, she is always busy with lots of online meetings. Many times, she did not wash her clothes and cover herself with different dupatta for online meetings. Do you think clean clothes are necessary for every human being?
(a) Not really, without cleaned clothes meetings can be done.
(b) Clothes has no impact on your well-being
(c) Cleaned clothes is only wastage of time
(d) Personal hygiene is important for self -management
Answer:
(d) Personal hygiene is important for self -management

Question 3.
Do you think people living in hill stations can skip taking bath for many days?
(a) No, irrespective of the climate, one should take a bath regularly.
(b) Not taking bath for many days is acceptable for people staying in cold climate.
(c) If you wipe yourself with a wet cloth, that is enough.
(d) None of the above
Answer:
(a) No, irrespective of the climate, one should take a bath regularly.

Self Management Skills Class 11 Notes

Knowing Yourself: Knowing “yourself” is being aware of who you are, your likes and dislikes, your beliefs and opinions, your background, as well as your strengths and weaknesses. Knowing yourself is crucial since only then can you evaluate your strengths and limitations.

Grooming: Clothing is referred to as dressing. To groom oneself is to present oneself as clean, organized, and well-groomed.
Your grooming and clothing choices can communicate your intelligence and self-assurance. Due to the fact that it enables us to.

• Look neat and clean.
• Feel confident about ourselves.
• Make a good impression of ourselves on others including customers.

Personal Hygiene : Personal hygiene is the habit or practice of keeping ourselves clean. Cleanliness helps us to maintain our health and well-being. Being healthy allows you to be regular at school, college or work.

Importance of Personal Hygiene-Personal hygiene is important because it

• Helps us stay healthy.
• Creates a good image of ourselves.
• Helps us avoid feeling ashamed in public due to our bad breath, body odour, etc.

Essential Steps of Hand Washing-

Team Work-A team is a collection of individuals who cooperate to accomplish a common objective. Each team has a set of objectives to accomplish. Teamwork is the act of cooperating as a group.

The post Self Management Skills Class 11 Questions and Answers appeared first on Learn CBSE.

These Class 11 AI Important Questions Chapter 3 Basic ICT Skills Class 11 Important Questions and Answers NCERT Solutions Pdf help in building a strong foundation in artificial intelligence.

Basic ICT Skills Class 11 Important Questions

Class 11 AI Basic ICT Skills Important Questions

Important Questions of Basic ICT Skills Class 11 – Class 11 Basic ICT Skills Important Questions

Basic ICT Skills Class 11 Very Short Answer Type Questions

Question 1.
Define advantages of word processor?
Answer:
Below are the advantages:

• People can type words faster than writing in a pen….
• Word processors have fonts that make the text clear or easy to read, compared to distinct handwriting.
• Through synchronization, a text can be generated by people in two separate locations.
• Useful tools such as spell checker, grammar checker available to improve the quality of your work.

Question 2.
Define difference between status bar and menu bar.
Answer:
The status bar is located bottom of the window and the menu bar located top of the window and has several options, such as File, Edit, View, Insert, Format, Styles, Table, Form, Tools, Windows and Help.

Question 3.
What is toolbar?
Answer:
The toolbar is present just below the menu bar and has icons that directly run the command without going through the menu.

Question 4.
Give the keyboard shortcuts for the following:
a-Bold
b-Italic
c-Underline
D-Insert table
Answer:
a- Bold-Ctrl + B
b-Italic-Ctrl+I
c-Underline-Ctrl +U
d-Insert table-Ctrl+F12

Basic ICT Skills Class 11 Short Answer Type Questions

Question 1.
What is the uses of find and replace command in word?
Answer:
You can simply find the text you’re looking for by typing it. The text can also be changed.
With a different text. By selecting Replace All, you can identify and replace every instance of matching text in the document at once, or you can search for and replace each instance individually.

Question 2.
Uses of spelling and grammar in word processor?
Answer:
When we write official letters or make a bio-data to apply for a job, it is especially important that we use correct language. Normally we check manually by reading the letter again. Word processors provide a tool to check spellings and grammar errors.

Question 3.
What is the use of Print Preview?
Answer:
Print Preview is a functionality that lets users see the pages that are about to print, allowing the users to see exactly how the pages will look when they are printed.

Question 4.
What is text formatting?
Answer:
Text formatting is a feature in word processors used to change a text’s appearance, such as its size and color.

Question 5.
What are the advantages of using a word processor.
Answer:
Below are the some points of benefits of using word processor:

• A document can be stored in a computer for future reference
• Have special editing tools e.g. spelling and grammar checkers
• One can easily insert or replace a word or phrase without affecting the neatness of a . document
• One can easily produce many copies using a printer without retyping

Basic ICT Skills Class 11 Long Answer Type Questions

Question 1.
What do you mean when you say “ICT”? Provide examples.
Answer:
ICT, or information and communications technology (or technologies), is the infrastructure and components that enable modern computing.

Examples are: software applications and operating systems; web-based information and applications such as distance learning; telephones and other telecommunications products; video equipment and multimedia products that may be distributed on videotapes, CDs, DVDs, email, or the World Wide Web; office products such as :

Question 2.
What is menu bar? Explain the functions of the various options in the menu bar.
Answer:
The area of a browser or application window known as the menu bar, which is normally found at the upper left corner, contains drop-down menus that let users interact with the application or content in a variety of ways.

The objective of the menu bar is to give a common home for window- or application-specific menus that offer access to tasks like opening files, interacting with a programme, or showing help materials or manuals.

Case-Based Questions:

Question 1.
Radhika has made some changes in the document received by Megha. But she wants to view, how the document will appear after the changes without actually incorporating them in the document. She doesn’t know how to do it. Suggest her the right way to do so.
a. By clicking on Compare Documents OpenOffice
b. By clicking on Accept All command in Track options
c. By clicking on the Show option to display the document
d. None of the above
Answer:
c. By clicking on the Show option to display the document

Question 2.
Mohan wants to mention his name and page number in her document. He does not know how to do it. Help him in doing so.
a. By adding header and footer into the document.
b. By clicking on Track changes option.
c. By manually doing after getting the hardcopy
d. By saving the document
Answer:
a. By adding header and footer into the document.

Question 3.
Yatish wants to search a word ‘morning’ in the document and replace it with ‘evening’. Name the feature of word processor that he can use for this purpose.
Answer:
Find and Replace feature

Question 4.
Vedika has to make the important words darker than the normal text. Which command of word processer can she use to make the text darker than the normal text? What is the keyboard shortcut for this command?
a. Ctrl+U
b. Ctrl+I
c. Ctrl+P
d. Ctrl+B
Answer:
d. Ctrl+B

Basic ICT Skills Class 11 Notes

Uses and advantages of word processor: Instead of using a pen and paper to compose a letter or create a report, we can use a word processor on a computer. A word processor is a piece of software that helps us produce documents by typing in text, revising it, organizing it neatly (formatting), and printing it.
Advantages of word processing:

• If you want to add something extra or remove some text after getting your report checked by a teacher, you can easily do it in a word document.
• Avoid making any spelling errors when creating a resume (bio-data). To ensure that you utilise proper grammar, spelling, and language, a word processor can assist you verify your spelling and grammar.

Menu Bar-The menu is visible in the bar directly beneath the title bar. File is the first item on the menu, followed by Edit, View, Insert, Format, Tools, Table, Window, and Help.

Status Bar: At the bottom of the window is the Status bar. To read the document as a single page, multipage, ar book, use the Document View choices at the bottom right.

Tool Bar-The toolbar is present just below the menu bar and has icons that directly run the command without going through the menu.

The post Basic ICT Skills Class 11 Questions and Answers appeared first on Learn CBSE.

These Class 11 AI Important Questions Chapter 4 Entrepreneurial Skills Class 11 Important Questions and Answers NCERT Solutions Pdf help in building a strong foundation in artificial intelligence.

Entrepreneurial Skills Class 11 Important Questions

Class 11 AI Entrepreneurial Skills Important Questions

Important Questions of Entrepreneurial Skills Class 11 – Class 11 Entrepreneurial Skills Important Questions

Entrepreneurial Skills Class 11 Very Short Answer Type Questions

Question 1.
Who is an entrepreneur?
Answer:
An entrepreneur is an individual who with efforts tries to meet customer needs through new ideas or ways of doing business and ultimately gets profit.

Question 2
What is an entrepreneurship?
Answer:
Entrepreneurship is a process where customer needs are closely analyzed and a business plan is developed. Then running of a business with innovative ideas to meet customer needs, in order to make a profit.

Question 3.
Once a business is established, do entrepreneurs sit silently?
Answer:
Once, a successful business is made, entrepreneurs think about ways to improve it and grow. They add value to their business for attracting more customers.

Question 4.
What is business plan?
Answer:
A business plan is a detailed plan of entrepreneurs goal setting and the way to achieve it.

Question 5.
What do you think; funds estimation is required before planning a business?
Answer: Through fund estimation a rough financial planning of a business is estimated. This includes fund estimation for the growth, performance, investments and requirement of funds for a given period of the business.

Question 6.
What are the types of business activities?
Answer:
The various types of business activities are: Manufacturing, Trading and Services.

Question 7.
How confidence makes an Entrepreneur successful?
Answer:
Confidence is the self- belief through which an entrepreneur have trust on his/her own chosen course of action. Through confidence only , any individual can take step to start a new business. Further, helps in try new things to grow their business.

Question 8.
What is problem-solving?
Answer:
With this process of thinking, entrepreneurs can come up with many solutions to improve their business.

Question 9.
Define the meaning of business idea?
Answer:
Mostly entrepreneur gets an idea of business either from needs of customer or inspiration from what others are doing. There are many ways in which one can come up with innovative, yet practical ideas for a business.

Question 10.
How an entrepreneur gets an idea of business?
Answer:
Some of the ideas are;

• Based on location
• Based on Season
• Based on events
• Based on self-interest
• Based on study

Entrepreneurial Skills Class 11 Short Answer Type Questions

Question 1.
What are the ways of understanding the market?
Answer:
Ultimate goal of an entrepreneur is to grow business by understanding customers’ needs by providing them the desired product or service offered by the business. Thus, an entrepreneur has to keep in mind two things for understanding the market:

• Customer’s need and preferences.
• Competitors business

Therefore, in this stage of business, understanding exactly what most of the customers want in terms of the following factors is very important.

Question 2.
How can an entrepreneur find out about customer needs?
Or
Why customer survey is important and how it is done?
Answer:
Through customer survey, entrepreneurs try to understand
Customer needs and their preferences. From this they analyze whether they are meeting customer needs and also understand what their preferences are. Customer survey is one of the most effective ways to find out what customers want. It involves talking directly to customers and asking them questions. Later in this session, you will be doing an activity to practice taking a customer survey

Entrepreneurial Skills Class 11 Long Answer Type Questions

Question 1.
What are some questions an entrepreneur should consider while launching a new company?
Answer:

• Who is the client, and what need is being satisfied?
• Which good or service should I sell?
• Which substance or materials are needed to construct the product?
• What kinds of company operations are involved?
• What function does each team member perform?
• How many clients are you trying to reach?

Question 2.
How may tenacity and self-assurance contribute to an entrepreneur’s success?
Answer:
Perseverance is the ability to move through adversity and keep moving forward. Operating a business comes with a lot of challenges. No matter what, a good entrepreneur rises above these obstacles, absorbs their lessons, and continues. When times are hard, entrepreneurs must remain upbeat and driven. An entrepreneur must consider growth and progress in addition to hard effort. Repetition of the same technique is not a sign of perseverance.

It entails coming up with novel and inventive solutions to problems and keeping the end goal in mind no matter what occurs along the route. Being confident entails having faith in your strategy and in yourself. Self-assurance enables entrepreneurs to take the initial leap to launch a new company and then explore novel approaches to expand their enterprise. It motivates business owners to keep going even when they fail.

Additionally, self-assurance motivates business owners to contact clients and ask for input on their goods or services. Entrepreneurs that receive feedback are able to expand and enhance their businesses.

Case-Based Questions:

Question 1.
Soham and Aditya are business partners, they want start their own business. So, what would be their first step?
a. Planning for a business idea
b. Surveying existing customer need
c. Seeking a space for their business
d. Arranging money for their business
Answer:
b. Surveying existing customer need

Question 2.
What value does the entrepreneur have?
a. Rohit runs a special hair salon in Bidar. Young boys come to him and ask him to give a Justin Bieber haircut. Rohit doesn’t know about this haircut so he searches on the Internet and finds the pictures.
What value does Rohit have?
Answer:
Problem solving

b. Shalu makes special Indian snacks and goes door-to-door in her town to sell it to different people. Only 1 in 10 people to whom she goes, buys her products.
What value does Shalu have?
Answer:
Hard working

Entrepreneurial Skills Class 11 Notes

Entrepreneurship- It is the process of analyzing society, finding an innovative idea, developing a business plan, and then doing business in a way that meets customer needs while ultimately generating profits.

Various types of Business activities- With the changing business needs and customer desires, there are various products in the markets thus there are various business activities. i.e. manufacturing, trading, services.

Values of a successful Entrepreneur- Some of the qualities of an Entrepreneurs are :

• Innovativeness and Creativity.
• Independence or Ownership.
• Quest for Outstanding Performance or Achievement.
• Respect for Work.

Attitudes of an Entrepreneur:

Steps for generating a business idea-The various steps for generating a business idea are:

• Developing a business idea
• Understanding the market
• Planning the business
• Finally generating the business

Understanding customer needs- As Entrepreneurial journey is all about to understand the customer needs and to present it is in the form of business. Categorizing the needs are as follows:

• Fulfilled needs
• Partially fulfilled needs
• Unfulfilled and known needs
• Unknown needs

Understanding business plan and its necessities- Without planning a business cannot be grow and to flourish the business a successful plan is the must.

Importants of business planning- Business planning is required for the following reasons.

• For estimating fund requirement
• For estimating material cost
• For planning business strategies
• Goal setting

The post Entrepreneurial Skills Class 11 Questions and Answers appeared first on Learn CBSE.

These Class 11 AI Important Questions Chapter 5 Green Skills Class 11 Important Questions and Answers NCERT Solutions Pdf help in building a strong foundation in artificial intelligence.

Green Skills Class 11 Important Questions

Class 11 AI Green Skills Important Questions

Important Questions of Green Skills Class 11 – Class 11 Green Skills Important Questions

Green Skills Class 11 Very Short Answer Type Questions

Question 1.
What is Green Economy?
Answer:
A green economy also known as sustainable economy is an economic system with goals to promote sustainable development and reduce environmental jmpact while promoting economic growth and well being.

Question 2.
Does green economy replaces sustainable development?
Answer:
No rather green economy puts emphasis on the economy, investment, capital, infrastructure, jobs and skills.

Question 3.
Is green economy an approach to long term economy growth yes green economy is a macro economic approach to long term economic growth as it main focus is an investments, job creation and skill development.

Question 4.
In how many sectors green economy can be categorized into?
Answer:
Green economy categorized various sectors: Agriculture, transport, west management, water management, forestry.

Question 5.
What are the initiative taken by the government to enforce green economy?
Answer:
Indian government has made different policies to achieve green economy.

• National solar mission
• National mission for enhanced energy efficiency
• National mission on sustainable habitats
• National water mission
• National mission for sustaining Himalayan ecosystem
• National mission for a Greener India
• National mission for a Greener India
• National mission on sustainable agriculture
• National mission on strategic knowledge on Cinema change
• Swachh Bharat Abhiyan (SBA)
• National Green Tribunal (NGT)

Green Skills Class 11 Short Answer Type Questions

Question 1.
Who are the stakeholders of the green economy?
Answer:
All the pupil who are interested to achieve green economy are said to be the major stakeholders in green economy. They are:

• Government
• Non-government organizations
• Companies
• Workers
• Farmers
• Native tribes
• Science and technology
• Government and private agencies

Question 2.
Name some green projects in India
Answer:
Some of the examples of green projects in India are

• Eco friendly product store
• Sustainable fashion brand
• Reducing water wastage
• Cycling and sustainable transportation
• Promoting eco tourism

Question 3.
How to do water management?
Answer:
Water management can be done by conserving water through rainwater harvesting and reducing water wastage preventing pollution water sources i.e. pond, lake, river etc.

Question 4.
Do tribals can ever achieve green economy?
Answer:
Of course tribals too can achieve green economy by promoting local products. Collaborating with NGO’s and mixing with pupils of other community they can achieve green economy.

Question 5.
How in agriculture sector green economy can be achieved?
Answer:
Agriculture sector feeds us it is the most significant sections of the green economy in this sector. Green economy can be achieved by using:

Organic and natural farming process i.e. free from pesticides and fertilizers.
By growing own vegetables.
Focus should be on consuming seasonal food rather than packaged food.

Green Skills Class 11 Long Answer Type Questions

Question 1.
Briefly explain the sectors of green economy.
Answer:
Some of the key sectors of the green economy are:

1. Agriculture and food production-Sustainable agriculture focuses on environment friendly farming practices. Like organic farming, reduced use of chemicals and pesticides it also focuses promoting local and organic food systems.
2. Forestry-Sustainable forestry practices involve the responsible management of forest and they are afforestation and reforestration. It also aims to maintain by diversity to protect ecosystem.
3. Water management-Water management aims to conserve efficient use of water resources in farming, industry and urban areas. It also includes practices like rain water harvesting, waste water treatment and water conservation.
4. Waste management-Waste management involves the responsible disposal and recyeling of waste material to maintain land fill uses and reduce pollution. It also includes composting.

Question 2.
What is Green India Mission?
Answer:
The Green India Mission also known as national mission for green India is included in the eight missions under NAPCC, to promote forest and biodiversity conservation, ecosystem services and sustainable management of natural resources. Key objectives are green India mission are :

• Climate change mitigation
• Sustainable forest management
• Eco restoration of water sheds
• Community participation

Question 3.
Briefly explain about stakeholders in Green economy?
Answer:
Since Green economy impacts everyone, its huge responsibilities of every single individual so its stakeholders are many. Those pupil who are interested in green economy are said to be the major stakeholder categories. Some of them are:

• Government- Various government policies laws, and agreement with other countries promotes sustainable developments.
• NGO-These type of organization work actively with government to creating awareness to adopt green economy among the community.
• Companies-Various private and public PSU’s promote green economy by making business operations to protect the environment.

Rural and urban sectors-Rural and urban workers to attain green economy, workers play a crucial role, by adopting green policies provide them decent living and job in a healthy environment. They can access a good health, clean water and sanitation. They can get good skilled education.

Case-Based Questions:

Question 1.
Simran is a student recently she found one of our family member is having massive health problems due to the air congestion. So she thought to collaborate with NGO’s to aware pupil using public transport. Due to think it is a necessary step to attain green economy.
Answer:
Of coursenit is a very good step due to two reasons:

• Use of public transport to pupil of same destination can lead to less pollution and helps in protecting environments.
• It also safes money.

Question 2.
Rohan is interested to know the government’s initiation to limit the use of on non-renewal energy to attain sustainable development. Due to think is it possible?
Answer:
Yes it is possible due to Government of India’s national solar mission that address to use the solar energy for the environment protection. In India DIU is the first union territory of India to use hundred percent solar energy.

Question 3.
Madan is a social worker he wants to change the tribal’s life like any other Individual. So he research about how to change their life to attain sustainable developments. Can you help him by suggesting some majors?
Answer:
Life of tribal’s can be changed by increasing the sale of their local products and by awaring them about their specialties and uniqueness among the community also making their local products to a global product will certainly change their lives.

Green Skills Class 11 Notes

Green Economy-The term ‘green economy’ refers to a low-carbon resourceefficient and socially inclusive economy. In a green economy, public and private investment in economic activities, infrastructure and assets that allow for lower carbon emission and pollution.

Sectors of Green Economy-Agriculture, Transport, Water management, Waste management, Forestry etc.

Swachh Bharat Abhiyan (SBA)- On October 2, 2014 the Prime Minister of India launched the Swachh Bharat Mission to accelerate efforts to attain universal sanitation coverage and to focus attention on sanitation. Under the project all villages, Gram Panchayats, Districts, States and Union Territories in India pledged to be “open defecation free” (ODF) by October 2, 2019 the 150th birth anniversary of Mahatma Gandhi by constructing over 100 million toilets in rural India.

National Green Tribunal (NGT)- The National Green Tribunal functions similarly to a court in disputes involving environmental preservation, forest conservation and other natural resources conservation.

Government-Governments and municipal bodies create and implement laws, policies, strategies, standards, programs, agreements with other countries, and activities to promote sustainable development.

The post Green Skills Class 11 Questions and Answers appeared first on Learn CBSE.

These Class 11 AI Important Questions Chapter 1 AI for Everyone Introduction Class 11 Important Questions and Answers NCERT Solutions Pdf help in building a strong foundation in artificial intelligence.

AI for Everyone Introduction Class 11 Important Questions

Class 11 AI for Everyone Introduction Important Questions

Important Questions of AI for Everyone Introduction Class 11 – Class 11 AI for Everyone Introduction Important Questions

AI for Everyone Introduction Class 11 Very Short Answer Type Questions

Question 1.
What is an artificial intelligence?
Answer:
Artificial intelligence is the branch of computer science that emphasizes on making machine’s (computer system’s) intelligent, in order to make them think and act like human beings.

Question 2.
Who is the father of artificial intelligence.
Answer:
John McCarthy is known as the father of artificial intelligence as he first coined the term ‘Artificial Intelligence’.

Question 3.
When McCarthy coined the term artificial intelligence?
Answer:
1956

Question 4.
What are the types of AI?
Answer:
There are three types of AI-

• Artificial Narrow Intelligence
• Artificial General Intelligence
• Artificial Super Intelligence

Question 5.
What are the domains of AI?
Answer:
Artificial Intelligence has the following sub-domains.

• Machine Lęarning
• Neural Network
• Computer Vision
• Robotics
• Expert Systems
• Natural Language Processing
• Fuzzy logic Systems

Question 6.
What is Data?
Answer:
Data is the collection of raw facts and figures.

Question 7.
Why Data is important in AI?
Answer:
The whole concept of Artificial Intelligence is roaming around Data. Using data only machines became Intelligent.

Question 8.
How Machine Learning works?
Answer:
Machine learning works on the basis of previous database experience available with it.

Question 9.
What are the categories of ML?
Answer:
ML is divided into three categories-Supervised, Unsupervised and Reinforcement learning.

Question 10.
What is neural network?
Answer:
Neural network is a technique to build computer program that learns from the data. It is the layers of software units called neurons. (or nodes ) Connected with different nodes (neurons) in a layered manner.

Question 11.
Write the structure of neural network?
Answer:
The entire neural network is divided into three separate nodes or layers known as input, hidden and output.

Question 12.
Is deep learning is the imitation of the Human brain?
Answer:
Yes

Question 13.
How is machine learning related to AI ?
Answer:
Machine learning enables machines to learn, forecast, and improve on their own, contributing to the broader field of AI.

Question 14.
How do you understand whether a machine/application is AI based or not? Explain with the help of an example. [CBSE TextBook]
Answer:
To understand whether a machine/application is AI based or not, we need to check if it learns with data and whether it’s able to decide/predict.

AI for Everyone Introduction Class 11 Short Answer Type Questions

Question 1.
Why artificial intelligence is the need of the hour?
Answer:
These days AI is the inevitable part of everyone’s life as:

• It can solve many real life problems i.e. health related issues, transport issues.
• Various virtual assistances made with the help of AI such as Cortona, Google assistant, Siri can help challenged people also.
• AI can work in an environment that is very difficult or dangerous for human beings.
• Through AI India is able to land on the Moon.

Question 2.
What type of data is used in making machines Artificially Intelligent?
Answer:
Artificial intelligence (AI) systems use various types of data to make machines artificially intelligent. The choice of data depends on the specific AI task and the domain it operates in. Here are some common types of data used in AI are:

• Information
• Graphs
• Facts
• Numbers
• Figures
• Quantities etc.

Question 3.
What is Machine Learning?
Answer:
A branch of artificial intelligence called machine learning (ML) focuses on creating statistical models and algorithms that let computers learn from data and experience to perform better on a given task without needing to be explicitly programmed.

Question 4.
Is there any difference between machine learning and artificial intelligence?
Answer:
Machine learning is considered as a tool for achieving artificial intelligence. If AI is a technology to create intelligence machine then machine learning provides machines the ability to learn forecast and progress on their own without any specific program.

Question 5.
Write some examples of Machine learning algorithm in our daily-life.
Answer:
In practice, machine learning algorithms are used to predict stock prices, identify spam emails, and project class attendance. These outcomes can be attained by utilising previously gathered spam messages, historical price records, or five or more years’ worth of class attendance data.

Question 6.
What are the jobs you can get with the help of AI?
Answer:
With the knowledge of AI you can get:

• Creative job
• Management job
• Tech job like data detective
• Man-machine teaming manager
• AI business development manager
• AI assisted medical professional (this I am sure, will appear before 2030)
• AI tutor

Question 7.
What ML cannot do?
Answer:
Although ML has made many advancement in the recent years still it has some issues:

• It takes time to solve social causes related problems
• According to text analytics, Ambiguity and Variability problem is associated with ML,there are many challenges that the same word can have many meanings. Second there are many ways to say the same word also.
• Also ML cannot solve ethical issues for instances a person’s death is caused by a self driving car on the road then it is difficult to find out whose fault is it?

Question 8.
What is deep learning, and how does it differ from traditional machine learning?
Ans.
Deep learning, a branch of machine learning that relies heavily on high-end computing equipment and can tackle end-to-end issues, is built solely on artificial neural networks.

Question 9.
What do you mean by Reinforcement Learning? Write any two applications of Reinforcement Learning at School.
Answer:
Through interaction with the environment and feedback in the form of rewards or penalties, an agent can learn to make decisions through a sort of machine learning called reinforcement learning. Adaptive learning systems, which customise content, and educational games and simulations, which include students in interactive learning, are two examples of applications used in schools.

AI for Everyone Introduction Class 11 Long Answer Type Questions

Question 1.
Briefly explain all categories of Machine learning algorithms.
Answer:
Supervised Learning:

• In supervised learning, the algorithm is trained on a labeled dataset, means each input data point is paired with the corresponding correct output or target.
• The goal is to learn a mapping from inputs to outputs, allowing the algorithm to make predictions or classifications on new, unseen data.

Unsupervised Learning:

• Unsupervised learning deals with unlabeled data, means the algorithm tries to find hidden patterns, structures, or clusters within the data.
• Mostly used for tasks like clustering (grouping similar data points together) and dimensionality reduction (reducing the number of features while preserving important information).

Reinforcement Learning:

• Reinforcement learning is concerned with training agents to make a sequence of decisions in an environment to maximize a cumulative reward.
• The agent interacts with the environment and learns by receiving feedback in the form of rewards or penalties for its actions.
• Applications of reinforcement learning include game playing (e.g., AlphaGo).

Question 2.
What’s the difference between traditional programming approach and Machine learning approach?
Answer:

Question 3.
How Machine learning approach is different from traditional approach, explain with an example.
Answer:
Traditional approach steps to convert Celcius scale to Fahrenheit scale
Step -1: Take input deg_celcius
Step-2: Apply the conversion formula: deg_Fahrenheit = deg_Celcius * 1.8+32
Step -3: Print the Output deg_fahrenheit
Note: we are telling the computer what to do on the input data i.e. multiply deg_Celcius with 1.8
and then add 32 to obtain the value in deg_Fahrenheit.
Machine Learning approach to convert deg celcius to deg_fahrenheit
Step 1: Feed or input many values in deg_Celcius (i.e. -40,-10,0,8,15,22,38 )
Step -2: Feed or input corresponding deg_Fahrenheit values (i.e. -40,14,32,46,59,72,10 Q )
Step -3: Pass these 2 sets of values to Machine Learning (ML) algorithm
Step- 4: Now you ask the ML program to predict (convert) any other celcius value to Fahrenheit, and program will tell you the answer.
For example, ask the computer to predict (convert) 200 Celcius to Fahrenheit, and you will get the answer as 392.

Note: in the ML approach, nowhere this conversion or calculation step (F=C^* 1.8+32) has been mentioned. Only the code was provided with the input data deg_celcius and corresponding output data (deg fahrenheit) and the model (i.e. ML code) will automatically generates the relationship between Celsius and Fahrenheit.

Question 4.
What is Deep Learning? How it is related to Neural networks?
Answer:
A branch of machine learning known as “deep learning” is centred on multi-layered artificial neural networks, or “deep neural networks.” It is a branch of machine learning that has seen great success and attention in a number of areas, such as speech recognition, computer vision, and natural language processing. Deep learning is therefore also a form of brain mimicking. We don’t have to explicitly programme anything in deep learning.

Competency-Based Questions

Question 1.
Read the following Information and answer the questions based on the information.
For instance, suppose the machine is given an image having both dogs and cats which it has not seen before. Logically the machine has no idea about the physical characteristics of dogs and cats and therefore it cannot categorize the animals. But it can surely categorize them according to their similarities, patterns, and differences i.e. we can easily categorize this above picture into two parts. First category may contain all pictures having dogs in it and second category may contain all pictures having cats in it. Here you didn’t learn anything before, means no training data or examples were provided for prior training.

Here in the above paragraph, we are discussing about:
a) Supervised Learning
b) Unsupervised Learning
c) Reinforcement Learning
d) Deep Learning
Answer:
(b) Unsupervised Learning

Question 2.
Read the following example and answer the question below it.
Suppose there is an Al agent presert within a maze environment, and his goal is to find the diamond. The agent interacts with the environment by performing some actions, and based on those actions, the state of the agent gets changed, and it also receives a reward or penalty as feedback.
The agent continues doing these three things (take action, change state/remain in the same state, and get feedback) and by doing these actions, he learns and explores the environment.

a) Neural Network
c)Deep Learning
b) Reinforcement Learning
d) Unsupervised Learning
Answer:
b) Reinforcement Learning

Question 3.
A hospital implemented an AI system to assist doctors in diagnosing diseases based on medical images such as X-rays and MRI scAnswer: However, some patients expressed concerns about the accuracy and reliability of the AI diagnoses. How can the hospital address these concerns?
Answer:
By carrying out extensive validation studies to evaluate the precision and dependability of the AI system in comparison to human diagnosis, the hospital can allay these worries. Additionally, they can guarantee transparency by giving thorough explanations of the AI system’s operation and how it enhances the knowledge of medical professionals.

Question 4
Rahul is an architect. He has designed and built a beautiful home for his client in Pune. He has installed these systems/appliances/gadgets at the newly constructed home. Identify which of these are AI systems and which of these are not AI systems. Solar water heater, Smart TV, Security cameras, rainwater harvesting system, cleaning robots, smart lighting, automatic door, Siri, automatic washing machine. After separating the AI systems, mention some parameters on which you choose these appliances/systems as AI systems.
Answer:
Siri, smart lights, cleaning robots, and smart TVs are examples of AI systems. The following systems do not use artificial intelligence: automatic doors, solar water heaters, security cameras, rainwater harvesting systems, and automatic washing machines. The criteria are: decision-making, problem-solving, suggestion-making, situational adaptation, and experiencebased learning. You can add any additional AI feature.

Question 5
If you were designing a robot to sort recyclable items like glass, plastic, and paper, which type of learning would be used to help the robot.
Answer:
Supervised learning

Question 6.
Can you think of a scenario where you have a bunch of different fruits mixed together and you want the computer to organize them into groups based on similarities?
Answer:
Unsupervised learning

Question 7.
Mr. Shankar owns a company that deals with services to customers related to financial investments. Lately, he has been using AI technology in his company due to which his employees are facing less job responsibility, customers are feeling insecure about their data. What is this scenario known as?
Answer:
Potential impact of AI on society

Question 8.
Jatin is a student who has just enrolled in a course in AI. He attended a few introductory classes and learned that systems can learn from the data using algorithms to perform a task without explicitly programming it. In some situations, the system mimics the human brain’s learning process. Identify the concepts in this scenario. [CBSE TextBook]
Answer:
Machine learning and Deep learning

Scenario Based Question Answer

Scenario 1: Understanding Neural Networks

Scenario: Alok is taking a course on artificial intelligence and is learning about neural networks. During a class discussion, the professor asks Alex to describe how neural networks function.

Question:
How should Alok describe a neural network in terms of its structure and function?
a) A linear sequence of commands
b) A single computational unit
c) A network of interconnected neurons
d) A random collection of algorithms
Answer:
c) A network of interconnected neurons

Scenario 2: Identifying AI Systems

Scenario: Priya is a software developer tasked with evaluating new software to determine if it incorporates artificial intelligence. She is looking for specific characteristics that indicate AI capabilities.

Question:
What characteristic should Priya look for to determine if the software is AI-based?
a) Ability to perform repetitive tasks quickly
b) Ability to perform tasks that typically require human-like intelligence
c) Ability to store large amounts of data d) Ability to connect to the internet
Answer:
b) Ability to perform tasks that typically require human-like intelligence

Scenario 3: Cognitive Tasks and AI

Scenario: During a technology seminar, a speaker explains how AI can transform industries by performing tasks that usually require human intelligence.

Question:
According to the speaker, what types of tasks does AI enable machines to carry out?
a) Physical tasks typically performed by robots
b) Cognitive tasks typically performed by humans
c) Mechanical tasks typically performed by machines
d) Emotional tasks typically performed by therapists
Answer:
b) Cognitive tasks typically performed by humans

Scenario 4: Categories of Machine Learning

Scenario: Maria is studying machine learning and comes across a section that categorizes different learning techniques used in AI applications.

Question:
What are the three categories of learning techniques Maria learns about in her studies?
a) Supervised, unsupervised, and collaborative learning
b) Supervised, unsupervised, and reinforcement learning
c) Supervised, unsupervised, and competitive learning
d) Supervised, unsupervised, and associative learning
Answer:
b) Supervised, unsupervised, and reinforcement learning

Scenario 5: Deep Learning and AI
Scenario: A tech startup is focusing on implementing cutting-edge AI technologies. The CTO mentions a specific subset of AI that relies heavily on artificial neural networks to achieve its advanced capabilities.

Question:
What subset of AI is the CTO referring to?
a) Supervised Learning
b) Deep Learning
c) Data Mining
d) Natural Language Processing
Answer:
b) Deep Learning

Scenario 6: Machine Learning for Cybersecurity

Scenario: An online retail company is enhancing its cybersecurity measures by using advanced technologies to detect and prevent fraud.

Question:
Which technology is the company using to make cyberspace more secure and detect online fraud?
a) Blockchain
b) Machine Learning
c) Cloud Computing
d) Internet of Things (IoT)
Answer:
b) Machine Learning

AI for Everyone Introduction Class 11 Notes

Artificial Intelligence (AI) Definition:

AI refers to the ability of a machine to learn patterns and make predictions. It combines computer science and robust datasets to enable problem-solving without replacing human decisions but adding value to human judgment.

Examples of AI Applications:

• Understand Language: Virtual assistants like Siri or Alexa.
• Recognize Images: Identifying animals in photos.
• Make Predictions: Predicting the weather or movie recommendations.
• Play Games: AI playing chess or video games.
• Drive Cars: Autonomous vehicles sensing the road and making decisions.

Non-AI Examples:

• Traditional Rule-Based Systems: Follow set rules without learning from data.
• Simple Automation Tools: Timers or calculators that do not think or learn.
• Mechanical Devices: Pulleys or gears that do not learn or think.
• Fixed-Function Hardware: Microwave ovens that perform tasks without learning.
• Non-Interactive Systems: Basic electric fans that do not change based on new information.
• Basic Sensors: Collect data but do not analyze or understand it.

Evolution of AI:

• 1950: Alan Turing’s paper “Computing Machinery and Intelligence” proposed the “imitation game” (Turing test).
• 1956: Dartmouth Conference organized by John McCarthy, marking the birth of AI as a field.
• 1960-1970: Development of expert systems, early neural networks, and symbolic reasoning.
• 1980-1990: Breakthroughs in machine learning and neural networks, leading to “AI winter.”
• 21st Century: Advancements in computing power, data availability, and algorithmic innovation leading to transformative AI applications.

Types of AI:

• Narrow AI: Focuses on single tasks (e.g., virtual assistants) and lacks broader understanding.
• Broad AI: Handles a wider range of related tasks, often used in businesses.
• General AI: Performs any intellectual task a human can, though current AI lacks abstract thinking, strategizing, and creativity.

Domains of AI:

• Data Science: Deals with numerical, alphabetical, and alphanumeric data for extracting insights using statistical methods, machine learning, and data visualization.
• Natural Language Processing (NLP): Processes text and speech inputs for tasks like language translation, sentiment analysis, and speech recognition.
• Computer Vision: Interprets visual data inputs (images and videos) for tasks like object detection, image classification, and facial recognition.

Data Types:

• Structured Data: Neatly arranged in rows and columns, easy to analyze (e.g., names, dates).
• Unstructured Data: Lacks specific organization, challenging to analyze (e.g., images, text documents).
• Semi-Structured Data: Uses metadata for some level of organization (e.g., social media videos with hashtags).

Cognitive Computing:

Mimics human brain functions for processing information and making decisions using Machine Learning, Reasoning, NLP, and Computer Vision.

AI Terminologies:

• Machine Learning: Developing algorithms that enable computers to learn from data and make predictions without explicit programming.
• Deep Learning: AI function imitating the human brain’s data processing for creating decision-making patterns. Uses neural networks with multiple layers for feature extraction and recognition.
• Neural Networks (Artificial Neural Networks – ANNs): Core of Machine Learning with node layers (input, hidden, output) for data processing.

Types of Machine Learning:

• Supervised Learning: Learning from labeled data to map input to output.
• Unsupervised Learning: Finding hidden patterns in unlabeled data.
• Reinforcement Learning: Learning to make decisions by interacting with an environment to maximize rewards.

Benefits of AI:

• Increased efficiency and productivity.
• Improved decision-making.
• Enhanced innovation and creativity.
• Progress in science and healthcare.

Limitations of AI:

• Job displacement concerns.
• Ethical considerations like bias and misuse.
• Lack of explainability in complex models.
• Data privacy and security vulnerabilities.

The post AI for Everyone Introduction Class 11 Questions and Answers appeared first on Learn CBSE.

These Class 11 AI Important Questions Chapter 2 Unlocking your Future in AI Class 11 Important Questions and Answers NCERT Solutions Pdf help in building a strong foundation in artificial intelligence.

Unlocking your Future in AI Class 11 Important Questions

Class 11 Unlocking your Future in AI Important Questions

Important Questions of Unlocking your Future in AI Class 11 – Class 11 Unlocking your Future in AI Important Questions

Unlocking your Future in AI Class 11 Very Short Answer Type Questions

Question 1.
Name some common job roles in the field of artificial intelligence (AI).
Answer:
Some common Job roles in AI are Machine Learning Engineer, Data Scientist, Business Intelligence Developer, Robotics Engineer, Software Engineer, NLP Engineer, Computer Vision Engineer, AI ethicist, AI consultant.

Question 2.
How computer vision sees an image?
Answer:
Computer visualizes images in the form of a matrix of 2-or 3’dimensional array(black or colour image).

Question 3.
What are the primary task of a computer vision?
Answer:
The primary task involved in the computer vision are:

• Semantic segmentation or image classification
• Classification + localization
• Object detection
• Instance segmentation

Question 4.
What is semantic segmentation?
Answer:
The technique of associating each pixel in an image with a class name (such as a flower, person, road, sky, or car) is known as semantic segmentation. Segmentation is a technique used to teach computers how to comprehend images at the pixel level.

Question 5.
What is Artificial Intelligence (AI)?
Answer:
AI refers to the simulation of human intelligence in machines that are programmed to think and learn like humans.

Question 6.
How has AI impacted daily life?
Answer:
AI is integrated into daily life through personalized recommendations, autonomous vehicles, and more, enhancing convenience and efficiency.

Question 7.
What are some common AI job roles?
Answer:
Roles include Machine Learning Engineer, Data Scientist, Robotics Engineer, and AI Ethicist, among others.

Question 8.
What skills are essential for a career in AI?
Answer:
Technical skills like machine learning, programming (Python, Java), and familiarity with AI frameworks, along with soft skills such as communication and problem-solving.

Question 9.
Which industries benefit from AI integration?
Answer: Industries like healthcare, finance, retail, and automotive use AI for operations optimization, customer service enhancement, and more.

Question 10.
How can someone prepare for a career in AI?
Answer:
By acquiring technical skills in machine learning and programming, staying updated with AI tools like TensorFlow, and building soft skills like teamwork and problem-solving.

Question 11.
What is one of the key advantages of pursuing a career in AI?
Answer:
The sheer breadth of opportunities it offers.

Question 12.
What role does a Machine Learning Engineer play?
Answer:
They develop scalable data science models capable of handling vast volumes of realtime data.

Question 13.
Which programming languages are essential for a Data Scientist?
Answer:
SQL, Python, and Scala.

Question 14.
What is the primary responsibility of a Business Intelligence Developer?
Answer:
To design, model, and maintain complex data sets to analyze business and market trends.

Question 15.
What expertise is crucial for a Robotics Engineer?
Answer:
Proficiency in programming, robotic engineering, mechanical engineering, and electrical engineering.

Question 16.
What is the role of a Natural Language Processing (NLP) Engineer?
Answer:
They specialize in human language processing, working on voice assistants’, speech recognition, and document processing.

Question 17.
What is the focus of a Computer Vision Engineer?
Answer:
Developing algorithms and systems that enable computers to analyze and interpret visual information from images or videos.

Question 18.
What is the primary concern of an AI Ethicist?
Answer:
Addressing ethical considerations and implications related to the development and deployment of AI technologies.

Question 19.
What does an AI Consultant do?
Answer:
They offer expert guidance and advice to organizations on how to leverage AI technologies to solve business challenges and drive innovation.

Question 20.
Which skill is vital for developing advanced AI applications?
Answer:
Expertise in neural networks, machine learning, and deep learning.

Question 21.
Name a popular programming language used for building and deploying AI solutions.
Answer:
Python.

Question 22.
What is TensorFlow commonly used for in AI?
Answer:
Developing sophisticated AI applications.

Question 23.
Which soft skill is crucial for AI professionals to convey complex technical concepts to non-technical stakeholders?
Answer:
Effective communication skills.

Question 24.
What role does an Autonomous Vehicle Engineer play in the automobile industry?
Answer:
They develop AI algorithms for self-driving cars.

Question 25.
What is the primary job of a Medical Imaging Analyst in the healthcare industry?
Answer:
Analyzing medical images using AI algorithms.

Question 26.
Which industry would benefit from the role of a Precision Agriculture Specialist?
Answer:
Agriculture.

Question 27.
What does a Fraud Detection Analyst do in the finance industry?
Answer:
Identifies fraudulent activities using AI models.

Question 28.
What kind of AI professional is responsible for optimizing inventory levels in the retail industry?
Answer:
Inventory Management Specialist.

Question 29.
What is the main task of a Travel Recommendation Engine Developer in the tourism industry?
Answer:
Providing personalized travel recommendations.

Question 30.
Which platform offers free online micro courses to help gain skills for independent data science projects?
Answer:
Kaggle.

Question 31.
Which online resource provides insights, latest news, and a magazine featuring opinions of top industry leaders in AI?
Answer:
Analytics Insight.

Question 32.
Where can you find a free, two-hour Machine Learning for Everyone course?
Answer:
DataCamp.

Question 33.
What does a Quantitative Analyst in the finance industry do?
Answer:
Analyzes market trends using AI algorithms.

Question 34.
Which programming language is known for its flexibility and object-oriented functions used in Al ?
Answer:
C++.

Question 35.
Name a job role in the beauty and wellness industry that utilizes AI.
Answer:
AI-powered Skincare Assistant.

Unlocking your Future in AI Class 11 Short Answer Type Questions

Question 1.
What are some essential technical skills required for a successful career in AI, and why are they important?
Answer:
Some important technical skills required are expertise in neural networks, machine learning, and deep learning, knowledge of big data technologies and techniques, understanding of frameworks and libraries like TensorFlow, SciPy, and NumPy, familiarity with programming languages such as Python, R, Java, and C++, proficiency in linear algebra, probability, statistics, and signal processing. By acquiring the right combination of technical skills and tools, aspiring AI professionals can position themselves for success in this dynamic and rapidly growing field.

Question 2.
What is the role of AI professionals in healthcare, finance, and retail industry?
In Healthcare:

• Medical Imaging Analyst: Analyzes medical images using AI algorithms.
• Virtual Nurse Assistant: Provides personalized healthcare recommendations.
• Drug Discovery Researcher: Identifies potential drug candidates using AI.

In Finance:

• Quantitative Analyst: Analyzes market trends using AI algorithms.
• Fraud Detection Analyst: Identifies fraudulent activities using AI models.
• Financial Advisor: Offers personalized investment recommendations using AI-driven analytics.

In Retail Industry:

• Inventory Management Specialist: Optimizes inventory levels using AI algorithms.
• Sales Forecasting ‘Analyst: Forecasts sales using AI models.
• Customer Experience Designer: Enhances customer experience using AI-driven insights.

Question 3.
What is computer vision?
Answer:
The field of artificial intelligence known as “computer vision” aims to give computers the ability to see and process visual data in a manner that is comparable to how humans do. Retrieving relevant information from pictures, movies, and other visual inputs is its primary goal.

Question 4.
How AI uses in weather forecasting?
Answer:
Weather forecasting is the process of obtaining satellite data, identifying patterns in the observations, and computing the results to generate accurate weather predictions. Artificial intelligence uses computer-generated mathematical algorithms and computer vision technology to detect trends in order to produce accurate weather forecasts.

Question 5.
List some popular AI tools and programming languages used by AI professionals.
Answer:
Some of the popular AI tools are Tensor Flow, SciPy and NumPy. Programming Languages used by AI professionals are Python, R, Java, C++.

Question 6.
What soft skills do AI professionals need, and how do they help them succeed?
Answer:
Soft skills needed by AI professionals are:

• Effective communication skills
• Strong teamwork and collaboration
• Problem-solving, decision-making, and analytical thinking skills
• Time management and organizational skills
• Business intelligence and critical thinking skills Acquiring soft skills helps professional develop the personality and thus work better

Question 7.
Why is continuous learning crucial in AI, and how do professionals stay updated with the latest advancements?
Answer:
Because artificial intelligence is developing at a rapid speed, it is imperative that practitioners engage in continuous learning. Continuous AI learning is now essential due to technique evolution, new research findings, and addressing ethical and social issues. Professionals use online resources, research and publications, hands-on projects, community involvement, and ongoing education to stay up to date on the most recent advancements.

Unlocking your Future in AI Class 11 Long Answer Type Questions

Question 1.
How AI uses Google’s nowcasting?
Answer:
Google calls this method “Nowcasting,” and it’s intended to predict weather 0-6 hours ahead of time. It targets weather phenomena like thunderstorms, which can change quickly from clear sky to intense rain to strong winds, and vice versa. Convolutional neural networks (CNNs), which are mostly utilised for image analysis, are employed by Google.

Question 2.
AI and how it benefit for the society?
Answer:
AI is undoubtedly transforming the world, but there is also a lot of misinformation and attention surrounding it. Some ways in which AI is benefitting society is as follows.
Transportation: Autonomous vehicles are gradually coming to pass. In addition to saving lives, this will help prevent car accidents..

Healthcare: In the field of healthcare, AI is a blessing. As the pandemic has taught us, diseases are identified more swiftly and correctly, and medicine discovery and creation are sped up and simplified. During the pandemic, virtual nurse assistants were deployed to monitor patients, and big data analysis contributes to a more individualized patient experience.

Education: Textbooks are being digitalized with the aid of AI, early-stage virtual tutors -are assisting human professors, and face analytics are measuring student emotions to identify who is having difficulty or is bored and to better adapt the experience to your particular needs.

Agriculture: Numerous issues, such as the weather, water use, or soil conditions, affect the agriculture sector. Real-time data analysis assists farmers in increasing agricultural yields and, consequently, revenues.

Customer service: Google is developing an AI assistant that can perform human-like phone calls, such as booking reservations at a nearby restaurant. The system comprehends subtitles and context in addition to words.

Question 3.
How does the global demand for AI professionals affect career opportunities in the field?
Answer:
Due to the quickening speed of technical breakthroughs and the growing integration of AI solutions into various industries, there is an unprecedented need for AI talent globally. Organisations are using AI to optimise workflows, streamline operations, and provide customers with better services in a variety of industries, including healthcare, banking, retail, and transportation.

The vast array of opportunities that a career in artificial intelligence offers is one of the biggest benefits. There is a niche in the AI sector that is appropriate for your interests and skill set, regardless of your passions: robots, data analytics, machine learning, or natural language processing. Furthermore, new professions and specialisations are developing as AI technologies advance, opening up opportunities for specialisation and expertise.

Question 4.
What are some common job roles in the field of AI, and how do they contribute to the development and implementation of AI solutions across various industries?
Answer:
Some common job roles include:
Machine Learning Engineer: Machine learning engineers bridge software engineering and data science, utilizing big data tools and programming frameworks to develop scalable data science models capable of handling vast volumes of real-time data.

Data Scientist: Data scientists leverage machine learning and predictive analytics to extract insights from large datasets, informing business decisions.

Business Intelligence Developer: Business intelligence (BI) developers design, model, and maintain complex data sets to analyse business and market trends, enhancing organizational profitability and efficiency.

Robotics Engineer: They design and maintain AI-powered robots, develop mechanical devices capable of performing tasks with human commands.

Software Engineer: AI software engineers build and maintain software products for AI applications, staying updated on the latest artificial intelligence technologies.

Natural Language Processing (NLP) Engineer: NLP engineers specialize in human language processing, working on voice assistants, speech recognition, and document processing.

Computer Vision Engineer: Computer vision engineers specialize in developing algorithms and systems that enable computers to analyse and interpret visual information from images or videos.

AI Ethicist: AI ethicists address ethical considerations and implications related to the development and deployment of AI technologies, ensuring that they are used responsibly and ethically.

AI Consultant: AI consultants offer expert guidance and advice to organizations on how to leverage AI technologies to solve business challenges and drive innovation.

Case-Based Questions:

Question 1.
Which AI application is used in the following cases?
a. Fani, a rare summer cyclone in the Bay of Bengal, hits eastern India on May 03,2019. It is one of the strongest cyclones to have hit India in the last 20 years, according to the Indian government’s meteorological department. Storm surges and powerful winds reaching 125 mph blew off roofs, damaged power lines, and uprooted countless trees.

But the worst-affected state, Odisha, has been successful in keeping the loss of life and numbers of affected people to a minimum. This is the result of a very effective strategy of disaster preparation and effective weather forecasting.

(Source: https://qz.com/india/1618717/indias-handling-of-cyclone-fani-has-a-lesson-for-theus/ )

b. When we speak in our device, the microphones of the device hear our audio and plots the graphs of our sound frequencies. As light-wave has a standard frequency for each colour, so does sound. Every sound (phonetics) has a unique frequency graph.

For example:
You: Alexa, what is the date today?
Alexa: It is the 18-March-2020.
Answer:
a) Computer Vision
b)Natural Language Processing

Question 2.
Sumedha and Neha in conversation with each other talked about the working of AI CCTV camera installed in their school. But they don’t know how it works. Help them in understanding the underlying concept behind it.
Answer:
AI CCTV cameras are advanced devices that does motion detection, face detection and similar features. The main job of the AI here is to make use of advanced analytical programs that have been integrated into their software. So, that all the activities of a human being can be recorded and analysed.

Question 3.
While reading newspaper Kanak, saw that a new startup company has launched an app that helps farmers using the AI technology. But she doesn’t know how AI helps to farmers. Help her in understanding the usage of AI.
Answer:
When it comes to the commodity market, global consumption outweighs local output. For this reason, price forecasting is highly advantageous to farmers. Generally speaking, agricultural products or crops are more vulnerable to weather, demand, and price risk than any other type of product.

As a result, small farmers frequently sell their products for extremely low prices. Crop waste can occasionally result from harsh weather or over harvesting. When crops fail and when there are abundant harvests, farmers are distressed. Farmers will be able to control their price and make educated decisions with the help of a trustworthy price forecasting tool.

Question 4.
In the class teacher explained about ‘Recommendation System’. But Mohan does not grasp the concept and he also can’t differentiate it from ‘Autonomous System’. What do you think are both same? Reason out your answer.
Answer:
Autonomous System: The technology through which machine understands the system and reacts without human intervention. The autonomous systems often use Artificial intelligence. For example, self -driving car, a floor cleaning robot etc. are autonomous system. Even IoT device like a smart home system is another example of an autonomous system. For instance, according to room temperature Acs are ON or not.

Recommendation System: Whereas, a recommendation system recommends or suggests products, services, information to users based on analysis of data that are dependent on a number of factors such as the history, behavior, preference, and interest of the user, etc.

Recommendation System are data driven; they need data for training. For instance, YouTube, suggests based on our past searches or preferences. So, in short it gathers data from the history of your activities and behavior. Similarly, Flipkart recommends the types of products that you normally prefer or buy. All these are classic examples of recommendation, but you must know that Machine Learning (AI) is working behind all this to give you a good user experience.

Question 5.
Medha just finished her Class X exams and dreams of a career in AI for Finance. She’s unsure what subjects to choose in higher studies (Class XI & XII) to prepare for this future goal. What subjects would you recommend for Medha to take in school, and are there any additional courses she can consider to strengthen her profile?
Answer:
Medha can pursue a job in finance while pursuing her goals in AI. Her academic priorities should be in mathematics, computer science, and commerce (business studies, accounting, and economics). Furthermore, online courses that bridge the gap between finance and AI may be found on sites like Kaggle, Udemy, and Data Science programmes. These courses might impart useful technical skills. She will be well-prepared for a prosperous future in AI for Finance with this combo.

Question 6.
Akash, a recent graduate, just joined the IT and AI startup company as a software developer. He’s brilliant at coding but feels a bit nervous about collaborating with his new team. He feels explaining technical concepts to non-technical team members and clearly documenting code could be a challenge. Also, he’s not sure how to effectively collaborate with others and handle situations where there might be disagreements about the best approach. List some soft skills that can help him excel in this environment:
Answer:
Effective Communication: Being able to explain complex technical ideas in clear and concise language, both verbally and in writing, is crucial.

Teamwork and Collaboration: Success in an AI startup often depends on working effectively with others. Actively listen to your colleagues, share ideas openly, and be willing to compromise when necessary.

Problem-solving: AI projects often involve unexpected hurdles. Develop a logical approach to problem-solving, considering different solutions and collaborating with your team to find the best approach.

Question 7.
Saloni, a career counselor, is visiting a Class XII assembly to talk about exciting career opportunities in Artificial Intelligence (AI). After explaining various AI fields, a student named Aditya raises his hand and asks, “This all sounds fascinating, but how is AI being used in healthcare specifically?” How will Saloni address Aditya’s query?
Answer:
In response to Aditya’s question, Saloni demonstrated how artificial intelligence (AI) is transforming the healthcare industry through projects including robotic surgery, medication development, virtual assistants, medical image analysis, risk assessment, and administrative work automation. This demonstrates the enormous potential of employment in artificial intelligence at the nexus of technology and medicine.

Question 8.
Naveen is a student. Help him to differentiate the commonly used AI tools, platforms and programming languages used for the development of AI applications. Python, GW-BASIC, R, MS-Word, IBM Watson, TensorFlow, MS-Paint, SciPy, Perl.
Answer:
Python, R, IBM Watson, TensorFlow and SciPy.

Question 9
Mr. Sourav Shukla is a journalist and owns a media house. He wants to leverage AI in his field. List some employment opportunities and some job roles related to the field of media.
Answer:
Visual effects, content generation, and audience analysis. Job roles are: Visual Effect Artist, Content Creator, Audience or Data Analyst, and targeted advertising.

Scenario Based Question Answer:

1. Scenario: Sarah has recently graduated with a degree in computer science and is interested in pursuing a career in AI. She is particularly passionate about developing algorithms that can interpret visual information from images or videos.

Question:
Which AI job role would be most suitable for Sarah, and what skills should she focus on acquiring to excel in this role?
Answer:
The most suitable job role for Sarah would be a Computer Vision Engineer. To excel in this role, she should focus on acquiring skills in developing algorithms and systems for analyzing and interpreting visual information. This includes proficiency in image processing techniques and programming languages such as Python and C++.

2. Scenario: John works as a business intelligence developer and wants to transition into a role that involves leveraging AI to provide strategic guidance to organizations. He has strong analytical skills and understands business processes well.

Question:
Which AI job role would be a good fit for John, and what additional knowledge or skills might he need?
Answer:
The AI job role that would be a good fit for John is an AI Consultant. In addition to his current skills, John might need to deepen his understanding of AI technologies and their applications in various industries. He should also develop strong communication skills to effectively convey AI strategies and solutions to organizational stakeholders.

3. Scenario: Maria is a data scientist who has been working with machine learning models to extract insights from large datasets. She is now interested in exploring roles where she can work on developing scalable AI models that handle real-time data.

Question:
Which job role should Maria consider, and what technical skills are essential for this position?
Answer:
Maria should consider the role of a Machine Learning Engineer. Essential technical skills for this position include strong mathematical skills, experience in machine learning and deep learning, and proficiency in programming languages like Java, Python, and Scala. She should also be familiar with big data tools and programming frameworks.

4. Scenario: David is a robotics engineer interested in designing AI-powered robots that can perform tasks based on human commands. He is looking for a role that combines his expertise in mechanical and electrical engineering with AI.

Question:
What job role would be most appropriate for David, and what areas should he focus on to succeed?
Answer:
The most appropriate job role for David is a Robotics Engineer. To succeed in this role, he should focus on areas such as programming, robotic engineering, mechanical engineering, and electrical engineering. He should also stay updated with the latest AI technologies used in robotics.

5. Scenario: Emily is passionate about natural language processing (NLP) and wants to work on projects involving voice assistants and speech recognition. She has a background in computational linguistics.

Question:
Which AI job role would align with Emily’s interests, and what should she concentrate on learning or improving?
Answer:
The AI job role that aligns with Emily’s interests is an NLP Engineer. She should concentrate on improving her knowledge of human language processing and developing skills in programming languages relevant to NLP, such as Python. She should also stay updated on the latest advancements in voice assistants and speech recognition technologies.

6. Scenario: Alex is working in the finance industry and wants to transition into an AI role that focuses on fraud detection and risk management. He has strong analytical and statistical skills.

Question:
What AI job role should Alex pursue, and what additional skills or knowledge might he need?
Answer:
Alex should pursue the role of a Fraud Detection Analyst. Additional skills or knowledge he might need include proficiency in machine learning algorithms, experience with big data platforms, and an understanding of AI models used in fraud detection. Enhancing his programming skills in languages like SQL, Python, and Scala would also be beneficial.

7. Scenario: Lisa is interested in working as an AI ethicist. She has a background in philosophy and law and wants to ensure that AI technologies are used responsibly.

Question:
What should Lisa focus on to become a successful AI ethicist, and what key responsibilities would her role include?
Answer:
To become a successful AI ethicist, Lisa should focus on gaining expertise in AI technology and understanding its ethical implications. Key responsibilities in her role would include addressing ethical considerations, providing guidance on ethical frameworks and policies, and promoting fairness, transparency, and accountability in AI systems.

8. Scenario: Kevin is an AI professional who enjoys collaborating with multidisciplinary teams and solving complex problems. He wants to take on a role that involves both technical expertise and strategic decision-making.

Question:
Which’AI job role would suit Kevin’s preferences, and what skills are crucial for this role?
Answer:
The AI job role that would suit Kevin’s preferences is an AI Consultant. Crucial skills for this role include a deep understanding of AI technologies, strong communication and analytical skills, and the ability to assess business needs and develop strategic AI initiatives. Collaboration and problem-solving abilities are also essential.

9. Scenario: Laura has been working as a software engineer and is interested in developing AIpowered applications. She wants to stay updated on the latest AI technologies and integrate them into software products.

Question: What’AI job role should Laura consider, and what areas should she focus on to excel in this role?
Answer:
Laura should consider the role of an AI Software Engineer. To excel in this role, she should focus on staying updated on the latest AI technologies, developing proficiency in software engineering and programming languages, and understanding statistical analysis. She should also enhance her knowledge of AI application development.

10. Scenario: Mike is a marketing professional interested in using AI to optimize marketing campaigns and enhance customer segmentation. He has experience in business studies and mathematics.

Question:
What AI job role would be ideal for Mike, and what additional skills or tools might he need to learn?
Answer:
The ideal AI job role for Mike is a Marketing Campaign Automation Specialist. Additional skills or tools he might need to learn include AI algorithms for campaign automation, customer segmentation techniques using AI, and proficiency in relevant AI tools and platforms. Enhancing his understanding of data analytics and AI-driven marketing strategies would also be beneficial.

Unlocking your Future in AI Class 11 Notes

Common job roles in AI – Machine Learning Engineer, Data Scientist, Business Intelligence Developer, Robotics Engineer, Software Engincer, Natural Language Processing (NLP) Engineer, Computer Vision Engineer, AI Ethics, AI Consultant.

Essential Skills and Tools for prospective AI Carrier-Technical Skills and Soft Skills.

Applications of AI across various Industries- Automobile, Healthcare, Retail, Agriculture, Media, Information Technology, Finance, Beauty \& wellness, Banking and Geospatial.

Popular AI tools and platforms-Python, R, Java, C++, Tensorflow, Scipy and Numpy

Learning Resources for AI:

• News and Blogs: Analytics Insight, Towards Data Science, KDnuggets, Data Science Central, Datanami.
• Free Learning Opportunities: IBM SkillsBuild, Kaggle, Udemy, froeCodeCamp.org, DataCamp, W3Schools, Codecademy.

Educational Pathways:
Professional Courses in AI: Bachelor of Science Degree in Data Science and Applications (IIT Madras), AI and ML courses (AICTE Digital Skilling), B.Tech courses in AI and ML, Data Science, Robotics, and Computer Science with specializations.

The post Unlocking your Future in AI Class 11 Questions and Answers appeared first on Learn CBSE.

These Class 11 AI Important Questions Chapter 3 Python Programming Class 11 Important Questions and Answers NCERT Solutions Pdf help in building a strong foundation in artificial intelligence.

Python Programming Class 11 Important Questions

Class 11 Python Programming Important Questions

Important Questions of Python Programming Class 11 – Class 11 Python Programming Important Questions

Python Programming Class 11 Very Short Answer Type Questions

Question 1.
For B=x: x is a natural number less than 8, write the set.
Answer:
B = 1,2,3,4,5,6,7

Question 2.
What are the types of matrix?
Answer:
Various types of matrix are row matrix, column matrix, square matrix, diagonal matrix, scalar matrix.

Question 3.
Write the types of operations that can be performed on a matrix?
Answer:
The various operations that are performed on a matrix are:
Transpose of a matrix, determinants of matrix. The other two matrices operations are trace and rank.

Question 4.
What is vector?
Answer:
A one dimensional array expressed in bracket is called a column vector or simply vector. For example v = (V1, V2, V3) Where v1 , v2, v3 are scalar values, often real values.

Question 5.
What is matrix?
Answer:
The two dimensional array-expression enclosed in brackets is called a matrix.

Question 6.
What kind of arithmetic operations you can do on a vector.
Answer:
Various arithmetic operations that can be done on a vector are:
Addition, subtraction, multiplication.

Question 7.
What are the operators used in relational algebra.
Answer:
Relational algebra consists of eight operators:
SELECT, PROJECT, JOIN, INTERSECT, UNION, DIFFERENCE, PRODUCT AND DIVIDE.

Question 8.
Calculate the mean of the following set data. S = {6,11,16,21,31}
Answer:
Mean of set S = 6+11+16+21+31 / 5 = 85 / 5 = 60.2

Question 9.
Five students marks in physics are given below :
18,10,13,10,17,11,9
Find the median value:
Answer:
Arranging the marks in order – 9,10,10,11,13,17,18

Question 10.
input() function accepts the value as string only. How can you convert string to int?
Answer:
Using int( ) function together with input(), we can convert string to int.

Question 11.
What are variables? What are the rules of declaring variables in Python?
Answer:
Named labels whose value can be used and processed during program run. Generally, keywords (list given above) are not used as variables. Variable names cannot start with digit and also it can’t contain any special characters except underscore.

Question 12.
What do you mean by type casting?
Answer:
A variable of particular datatype can be converted into another datatype using some functions. The explicit conversion of an operand to a specific type is called type casting.

Question 13.
“Python supports dynamic typing”, True or False. Justify your answer. True.
Answer:
A variable pointing to a value of certain data type can be made to point to a value/object of another data type. This is called Dynamic Typing. Python supports Dynamic Typing.

Question 14.
Name any four features of python language.
Answer:

• High Level language
• Interpreted Language
• Free and Open Source
• Platform Independent (Cross-Platform)

Question 15.
Give examples for keywords.
Answer:
and, as, continue, if, not try, del, pass

Question 16.
Expand CSV.
Answer:
Comma Separated Values

Question 17.
How do you read data from a CSV file into a Pandas DataFrame?
Answer:
import pandas as pd pd.read_csv(“filename.csv”)

Question 18.
What is Jupyter Notebook?
Answer:
Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text.

Question 19.
How do you install Jupyter Notebook?
Answer:
You can install Jupyter Notebook using Anaconda, which includes it by default, or by using pip with the command pip install notebook.

Question 20.
How do you start Jupyter Notebook after installation?
Answer:
Type jupyter notebook in your command prompt or terminal to start Jupyter Notebopk.

Question 21.
How do you create a new notebook in Jupyter Notebook?
Answer:
Click on the “New” button in the Jupyter Dashboard and select a kernel (e.g., Python 3) to create a new notebook.

Question 22.
How do you run code in a Jupyter Notebook?
Answer:
Press Shift + Enter or click the “Run” button to execute code cells in a Jupyter Notebook.

Python Programming Class 11 Short Answer Type Questions

Question 1.
Calculate the dot product of a=(-4,-9) and b=(-1,2) ?
Answer:
Using the formula for the dot product of two-dimensional vectors, a. b = a 1 b1 + a2 b2
we calculate the dot product to be
a.b = (-4)(-1)+(-9)(2) = 4-18 = -14

Question 2.

Answer:
[A]=3 ×(6 × 9-2 X7)-5(4 X9-1 X7)+2(4 X 2-6 X1) = 3(54-14) – 5(36-7) + 2(8-6) = 3(40) – 5(29) + 2(2) = 21

Python Programming Class 11 Long Answer Type Questions

Question 1.
Calculate the mean of the following grouped data.

Answer:

Here n = Σ f = 18
Σ f . x = 112 Mean = Σ f . x / n = 112 / 18 = 6.2

Question 2.
Following distribution table is given as :

Answer:
The median of the table is:

N = 145
Median class is = size of item = (145+1) / 2=73
Median class =20-30
Now l 1=20, cf = 30, f = 30, n = 145, i = 10
Median M = 20 + \(\frac{\left(\frac{145}{2}-30\right)}{30}\) × 10 = 20+((72.5-30) / 30) × 10 = 20 + (42.5 / 30) \frac{\left(\frac{145}{2}-30\right)}{30} 10 = 20 + 14.16 = 34.16

Question 3.
Describe the data types supported by Python, providing relevant examples.
Answer:

Question 4.
Define an operator and provide examples of different operators along with their functions.
Answer:
Operators are symbols or keywords that perform operations on operands to produce a result. Python supports a wide range of operators:

• Arithmetic operators (+, -, *, /, %)
• Relational operators (==, !=, , <=, >=)
• Assignment operators (=,+=, -=)
• Logical operators (and, or, not)
• Bitwise operators (&, |, ∧, <>)
• Identity operators (is, is not)
• Membership operators (in, not in)

Question 5.
Write a Tipper program where the user inputs the total restaurant bill. The program should then display two amounts: 15 percent tip and 20 percent tip.
Answer:

Question 6.
Write a program to check whether the user is eligible for driving license or not.
Answer:

Question 7.
Your father always gives his car for service after 15000 km . Check whether his car needs, service or not. Read the kilometer reading from the user and give the output.
Answer:

Question 8.
Write a program to display the first ten even natural numbers (use for loop).
Answer:

Question 9.
Write a program to accept the Basic salary from the user and calculate the Net Salary.
Net Salary = Basic Salary + HRA + DA -PF
HRA = 30 % of Basic
DA=20 % of Basic
PF=12 % of Basic

Question 10.
Write a program to create series from an array in Python.
Answer:

Question 11.
Consider the following admission.csv and answer the following questions:
Answer:
immmmmmmmmmmmm
a. Create a dataframe from the admission.csy
import pandas as pd
import csv
df=pd.read_csv(“admission.csv”)
print(df)
b. Display first 3 rows of the dataframe
print(df.head(3))
c. Display the details of Ravi
print(df. loc[‘Ravi’])
d. Display the total number of rows and columns in the data frame print(df.shape)
e. Display the column “Gender”
print(df[‘Gender’])

Case-Based Questions

Question 1.
Madhu is confused on uses of the matrices AI help her in understanding the same.
Answer:
In machine learning matrices are used for computing. It help in :

• Sales forecasting and predicting price
• In processing images
• In recommendation systems
• In processing natural language or NLP

Question 2.
Shyama is little bit confused while evaluating transpose and determinants of a matrix. Help her in understanding the concepts with an example.
Answer:
Transpose of a matrix. When a matrix obtained by a interchanging its rows and column then that matrix is called a transpose of a matrix (A) and is denoted by A^T.

Determinants of a matrix : Determinant is a scalar value it may be either a real number of a complex number bht it is a unique number. For example

Question 3.
Jagdish is watching that Shyama was trying to understand the determinants and transpose of a matrix now he want to discuss her about inverse of a matrix. Suggest a way to help them.
Answer:
Inverse of a matrix is the process of finding another matrix, which when multiplied with the original matrix gives an identity matrix. It is denoted by A^-1. For example

Question 4.
Help Priya to differentiate the given information into various datatypes of Python. Name of the student, email id, student id, marks in 5 subjects which can be changed at any point, 3 extra subjects chosen which cannot be changed later.
Answer:
The different datatypes of Python for the given information are:

• Name of the student-String type
• Email id-String type
• Student id-Integer type
• Marks in 5 subjects which can be changed at any point-List type.
• Extra 3 subjects which cannot be changed later-Tuple type.

Question 5.
For any analysis to be done, a huge amount of data needs to be collected and stored in a proper format. Rohan has stored the information in a delimited file that stores tabular data which is separated by comma. Which type of file is Rohan using?
Answer:
CSV file (Comma Separated Values)

Question 6.
Athrav is confused about the different libraries of Python. Help him choose the correct library for the following tasks. a. Data manipulation and aggregation functionalities b . Numerical computing c. Mathematical operations like square root, cosine values d. Machine learning and statistical modeling
Answer:
a. pandas
b. numpy
c. math
d. scikit-learn

Question 7.
Rohit wants to input the data about the runs scored by his 50 classmates. Which is the most appropriate loop to be used in this case?
Answer:
‘for’ loop

Scenario Based Question Answer:

Scenario: 1.

Question:
As a beginner in Python programming, you are interested in using Jupyter Notebook for your data science projects. Explain the steps you would follow to install and use Jupyter Notebook on your computer.
Answer:
To install and use Jupyter Notebook for your Python projects, follow these steps:
Installation:

If you’re using Anaconda, Jupyter Notebook comes pre-installed. Simply launch Anaconda Navigator and click on the Jupyter Notebook icon.
If you prefer using pip, open your command prompt or terminal and type pip install notebook.
Once installed, you can start Jupyter Notebook by typing jupyter notebook in your command prompt or terminal.

Opening Jupyter Notebook:

After starting Jupyter Notebook, a new browser window or tab will open showing the Jupyter Dashboard.
Here, you can navigate to the directory where you want to create or open your Python notebooks.

Creating a New Notebook:

Click on the “New” button on the top right corner and select “Python 3” (or any other kernel you need) to create a new notebook.
This will open a new tab where you can start typing your Python code.

Writing and Running Code:

In the notebook, you can write Python code in cells. To execute a cell, press Shift + Enter or click the “Run” button in the toolbar.
Output will appear below each cell where you’ve written code.

Saving and Closing:

To save your work, click on the “File” menu and select “Save and Checkpoint”, or use Ctrl + S (Cmd +S on macOS).
When you’re done, you can close the notebook by clicking on the “Quit” button in the Jupyter Dashboard or by closing the browser tab.

Python Programming Class 11 Notes

Python Overview:

• Python is a general-purpose, high-level programming language created by Guido van Rossum in 1991, named after Monty Python.
• Features: High-level, interpreted, free, open-source, platform-independent, easy to use and learn, supports ASCII and UNICODE characters.

Python Editors-There are various editors and Integrated Development Environments (IDEs) that you can use to work with Python. Some popular options are PyCharm, Spyder, Jupyter Notebook, IDLE etc.

Tokens in Python:
Tokens are the smallest units recognized by the Python interpreter, including keywords, identifiers, literals, operators, and punctuators.

Control Flow Statements:
Includes selection statements (‘if’, if..else’) and looping statements (‘for’, ‘while’).

Libraries in Python:

• NumPy: Used for numerical computing with arrays and matrices.
• Pandas: Used for data manipulation and analysis with data frames.
• Scikit-learn: Provides tools for machine learning tasks like classification, regression, and clustering.

Machine Learning with Scikit-learn:

Includes loading datasets like Iris, splitting data into training and test sets, using classifiers like K-Nearest Neighbors (KNeighborsClassifier), and evaluating model accuracy.

Identifier-An identifier is a name used to identify a variable, function, class, module or other object. Generally, keywords (list given above) are not used as variables. Identifiers cannot start with digit and also it can’t contain any special characters except underscore.

Literals- Literals are the raw data values that are explicitly specified in a program. Different types of Literals in Python are String Literal, Numeric Literal(Numbers), Boolean Literal(True & False), Special Literal (None) and Literal Collections.

Operators-Operators are symbols or keywords that perform operations on operands to produce a result. Python supports a wide range of operators:

• Arithmetic operators (+, -, *, /, %)
• Relational operators (==, !=, <, >, <=, >=)
• Assignment operators (=,+=,-=)
• Logical operators (and, or, not)
• Bitwise operators ( &, |, ∧, <<, >>)
• Identity operators (is, is not)
• Membership operators (in, not in)

Punctuators: Common punctuators in Python include:
: () [] {}, ; , ‘” ” “/ \ & @ ! ? | ~etc.

Looping Statements-Looping statements in programming languages allow you to execute a block of code repeatedly. In Python, there are mainly two types of looping statements: for loop and while loop.

Series-A Series is a one-dimensional array containing a sequence of values of any data type (int, float, list, string, etc.) which by default have numeric data labels starting from zero. The data label associated with a particular value is called its index.

The post Python Programming Class 11 Questions and Answers appeared first on Learn CBSE.

These Class 11 AI Important Questions Chapter 4 Python Programming Class 11 Important Questions and Answers NCERT Solutions Pdf help in building a strong foundation in artificial intelligence.

Python Programming Class 11 Important Questions

Class 11 Python Programming Important Questions

Important Questions of Python Programming Class 11 – Class 11 Python Programming Important Questions

Python Programming Class 11 Very Short Answer Type Questions

Question 1.
What are the issues and concerns around AI?
Answer:
Artificial intelligence research has led to a number of issues, including job loss, trust issues, safety concerns, and computation issues, among others.

Question 2.
What are the sources of AI bias?
Answer:
There are three sources of AI bias are: Data, Societal Bias and Algorithm.

Question 3.
What is Algorithm bias?
Answer:
Algorithmic bias is the term used to characterise systematic and recurrent mistakes made by computer programmes that lead to “unfair” outcomes, such as “privileging” one category over another in ways that go against the algorithm’s original purpose.

Question 4.
What is AI bias?
Answer:
Machine learning bias, sometimes called algorithm bias or AI bias, is a phenomena that arises when an algorithm produces results that are consistently biassed due to erroneous assumptions established during the machine learning (ML) process.

Question 5.
Give two examples of driverless car-related ethical concerns.
Answer:
Concerns concerning the morality of programming autonomous vehicles to make decisions in potentially hazardous circumstances are also raised. For instance, if a collision is unavoidable, should the vehicle choose to safeguard its occupants or limit property damage?

Question 6.
What skills are developed through an AI capstone project?
Answer:
Teamwork, problem identification, solution brainstorming, AI application selection, ethical data usage, prototype testing, and pitching solutions.

Python Programming Class 11 Short Answer Type Questions

Question 1.
Which occupations are at risk from AI?
Answer:

• Bank clerk
• Retail
• Manufacturing
• Translation using NLP
• Customer care/Telemarketing executive
• Driver
• Food preparation service
• Construction

Question 2.
How can bias in AI be reduced or eliminated?
Answer:
Following are some of the ways through which we can reduce AI bias:

• Create procedures for detecting and reducing bias in AI systems.
• Have fact-based discussions regarding possible biases in human judgement.
• Use an interdisciplinary strategy.
• Invest more in studies on AI bias.

Question 3.
Due to the massive number of patients during the Covid epidemic, robots were used to assist with patient care. Talk about the two benefits of this method.
Answer:

• The frontline staff members were protected by the robots acting as a barrier between the patient and the infection.
• During such an unprecedented time, robots were able to serve at maximum capacity (without rest)

Question 4.
What do you mean by a capstone project.
Answer:
The final project of an academic program, typically integrating all of the learning from the program is called the Capstone Project. A capstone project is a project where students must research a topic, deeply understand the subject matter and integrate all their knowledge to develop a solution to a problem.

Question 5.
Name some the skills which students will acquire by doing capstone project
Answer:
While doing a Capstone project, students will learn many important skills including:

• How to work as a team member
• How to clearly identify an issue and who it is affecting (the user)
• How to brainstorm solutions and select the best one
• How to decide which type of AI may be useful for the proposed solution
• How to ethically gather and use data to train a computer to help solve the issue
• How to test the prototype with users and use their feedback to improve the solution
• How to pitch their solution to people who will be able to help them take action

Question 6.
Give some examples of use case of SDG’s
Answer:
Some use cases of the SDGs are given below

• Renewable energy
• Reporting and communicating
• Combating hunger and poverty
• Protecting resources
• Promoting responsible consumption
• Providing access to education

Question 7.
Students are promoted to develop critical and creative thinking. Why?
Answer:
In order for students to effectively communicate and collaborate, they need to be taught how to think critically and creatively. They could pose important queries in response to issues, compile and evaluate pertinent data, generate concepts, and develop well-thought-out findings and solutions. The ability to think critically and creatively is essential for preparing people to be leaders, innovators, and social change agents.

Question 8.
Define Design Thinking
Answer:
One design methodology that offers a solution-based approach to problem solving is called design thinking. It’s quite helpful for solving complicated, unclear, or ill-defined problems.

Python Programming Class 11 Long Answer Type Questions

Question 1.
What are the issues raise due to AI and explain briefly?
Answer:
With development of artificial intelligence, some problems are arise like Trust related problem, Cyber security, Job loss problem and Computing power:
Trust related problem-As algorithms for artificial intelligence grow more potent, concerns, over whether they can make decisions that are fair and beneficial to humanity also surface.

Cyber Security-A technology’s potential for evil as well as good uses increases as its power increases. Hackers will always try to exploit any holes in the system, whether it be on a private network, cloud server, or social media platform.

Job loss problem-Today, there are a lot of expectations for AI. According to the media, cognitive capabilities of artificial intelligence would displace human workers from their occupations. Therefore, the IT sector must accurately communicate that AI is only a tool that can only be used in conjunction with humans.

Computing Power- Unstructured data is often used in AI. Enterprises find significant value in unstructured data, but many organizations are unable to provide useful information because they lack access to established techniques of analysis. Unstructured data is challenging to process and analyze since it cannot be stored in a relational database management system (RDBMS).

Question 2.
What AI and Bias and explain briefly?
Answer:
Artificial intelligence prejudice is a problem that happens when algorithm results are consistently biased towards a particular gender, language, race, wealth, etc. AI bias produces distorted results. Because algorithms are developed by people with conscious or unconscious preferences, they may have inherent biases that aren’t revealed until the algorithm is deployed in a public setting. There are three mainly sources of AI bias that are: Data, Societal Bias, Algorithm.

Data-Because AI systems are only as good as the data we feed them, biased AI results from the ingestion of biased data. AI systems reflect a wider base and are unable to distinguish between correct and incorrect training data.

Algorithm-Technology is meant to be objective. Unfortunately, this is untrue; parenting and society have a shaping effect on people and cause them to internalize certain preconceptions about the world. AI is identical. These individuals built the algorithms that make up the system, and the system tends to “think” in the manner that was taught to it.

Societal Bias-When an AI behaves in a way that reflects institutional discrimination or social intolerance, this is known as societal AI bias. Although the algorithms and data initially appear to be objective, their conclusions confirm societal biases.

Question 3.
Write down the steps of Problem decomposition
Answer:
Problem decomposition steps
1. Understand the problem and then restate the problem in your own words

Know what the desired inputs and outputs are
Ask questions for clarification (in class these questions might be to your instructor, but most of the time they will be asking either yourself or your collaborators)

2. Break the problem down into a few large pieces.
Write these down, either on paper or as comments in a file.

3. Break complicated pieces down into smaller pieces.

Keep breaking down until all of the pieces are small.

4. Code one small piece at a time.

• Think about how to implement it and write the code/query
• Test it on its own and Fix problems, if any

Question 4.
Explain briefly the different stages of Design thinking
Answer:
There are five stages of Design Thinking. They are: Empathize, Define, Ideate, Prototype, and Test.

i) Empathize: Design thinking begins with empathy. It allows designers to understand, empathize and share the feelings of the users. As a designer of the solution to a challenge, one should always understand the problem from the end-user perspective. This is done by observation, interaction or by imagination. Designers interact with customers / users very frequently to gather detailed facts about the problems and user’s expectations. A detailed analysis of these facts leads to approaching the problem in the best possible way.

ii) Define In the Define stage, information collected during Empathize is used to draw insights and is instrumental in stating the problem that needs to be solved. It’s an opportunity for the design thinker to define the challenge or to write the problem statement in a human cantered manner with a focus on the unmet needs of the users. 5 W 1 H method of asking questions can be implemented here.

iii) Ideate Ideation is the process of generating ideas and solutions through sessions such as sketching, brainstorming etc. At this stage, numerous ideas are generated as a part of the problem-solving exercise. In short, ideation is all about idea generation. Brainstorm, Brain write and Brain dump can be used for ideate stage.

iv) Prototype The prototype stage involves creating a model designed to solve consumers’ problems which is tested in the next stage of the process. Creating a prototype may include a developing simple drawing, poster, group role-playing, homemade “gadget, or a 3 d printed product.” The prototypes must be quick and easy to develop and cheap. Therefore, prototypes are visualized as rudimentary forms of what a final product is expected to look like.

v) Test During testing, the designers receive feedback about the prototype (s), and get another opportunity to interact and empathize with the people they are finding solutions for. Testing focuses on what can be learned about the user and the problem, as well as the potential solution.

Question 5.
Explain the different types of Ideation techniques.
Different ideation techniques are brainstorm, brain dump and brain writing
Answer:
Brainstorm
During a Brainstorming session, students leverage the synergy of the group to generate new innovative ideas by building on others’ ideas. Participants should be able to discuss their ideas freely without fear of criticism. A large number of ideas are collected so that different options are available for solving the challenge.

Brain dump

Brain dump is very similar to Brainstorm; however, it’s done individually. It allows the concerned person to open the mind and let the thoughts be released and captured onto a piece of paper. The participants write down their ideas onto paper or post-it notes and share their ideas later with the larger group.

Brain writing

Brain writing is also very similar to a Brainstorm session and is known as individual brainstorming’. At times only the most confident of team members share their ideas while the introverts keep the ideas to themselves. Brainwriting gives introverted people time to write them down instead of sharing their thoughts out loud with the group.

The participants write down their ideas on paper and, after a few minutes, pass on their own piece of paper to another participant who then elaborates on the first person’s ideas and so forth. In this way all participants pass their papers on to someone else and the process continues. After about 15 minutes, the papers are collected and posted for instant discussion.

Case-Based Questions:

Question 1.
Sita and Mina were discussing about trusting AI machines. According to Sita machines should not be trusted because they may give erroneous output but Mina however is not agreeing with her statement help a way out.
Answer:

• AI systems is so complicated that a user does not how it works. Thus trusting its decision is at stake. So its challenge can be addressed by the following ways.
• Using minimum bias data while training the machine.
• Sharing the algorithm with public or government before putting it into actual application.
• The AI developers should be from diverse backgrounds – gender, religion, skin colour, language and so on.
• On International monitoring body should be there, for monitoring and designing the and AI ethics and algorithm policy.

Question 2.
Mona said ‘Ethics and bias are not the problem of the machine rather it is the humans behind the machine’. Do you agree with her?
Answer:
Absolutely, considering that students involved in the AI system’s development-engineers, scientists, developers, etc.-strive to obtain the best accurate findings possible given the data at hand. without considering the bigger picture. Therefore, rather than blaming the machine, we must attribute the problem to the people who built it.

Question 3.
Rita is confused how these advanced machines are so problematic that it is showing biased results. Help her in understanding the sources of AI biasness.
Answer:
The reason there could be many causes for AI bias, but some of them are pointed below:

• Data-AI system does not have the understanding of whether their training data is correct or incorrect. It also have enough representation from a broader base or not.
• Algorithm-Although algorithm does not incur bias but it can amplify the causes behind the bias data.
• People-AI designers may less focus on the broader context of the usages of the machine.

Question 4.
As you near the completion of your Artificial Intelligence program, you are eager to put your newfound skills to good use. You have always been fascinated by the potential of AI to improve healthcare. Recently, you have learned about the challenges of early detection for certain diseases, leading to poorer prognoses. What do you call the project in which you must research a topic, deeply understand the subject matter, and integrate all their knowledge gathered about a particular subject to develop a solution to a problem?
Answer:
Capstone Project

Question 5.
You live in Darjeeling, a beautiful hill station. Due to heavy rains, landslides have damaged the roads, leaving the town temporarily isolated. Your school is closed, and online learning is the only option available. How do you feel about not being able to attend school in person and facing challenges with remote learning? Draw an empathy map to illustrate your thoughts, feelings, and experiences during this unexpected situation.
Answer:

Question 6.
Mr. Muthu Kumar is facing a serious issue of high electricity bill every month. He is trying a lot to reduce it. Considering his issue is worth solving, draw an empathy map related to Muthu Kumar.
Answer:

Question 7.
A small island nation in the Pacific Ocean, known for its beautiful beaches and colorful coral reefs, is in trouble. The sea level is rising, which is causing the shores to erode and making the land salty where plants used to grow. Most of the buildings and roads are near the coast, so they are at risk from the rising sea and strong storms. Because of these problems, it is harder for people to grow food like they used to, and they might not have enough to eat. Which Sustainable Development Goals (SDGs) can you align the above scenario with?
Answer:

• Zero Hunger (SDG 2)
• Sustainable Cities and Communities (SDG 11)
• Climate Action (SDG 13).

Scenario Base Question Answer:

Scenario: Sarah and James are computer science students preparing for their capstone project in artificial intelligence. They are keen on selecting a project that aligns with their interests and showcases their skills effectively. They have read about different stages and methodologies involved in capstone projects and want to ensure they understand the process thoroughly before proceeding.

Question 1.
What are the key skills Sarah and James will develop through their AI capstone project?
Answer:
Sarah and James will develop several important skills through their AI capstone project:

• Teamwork: They will learn how to collaborate effectively with peers to achieve project goals.
• Problem Identification: They will gain the ability to clearly identify and define complex issues that their AI solution aims to address.
• Solution Brainstorming: They will practice generating multiple solutions and selecting the most feasible one for implementation.
• AI Application: They will understand how to assess which AI techniques are suitable for solving specific problems, such as classification, regression, or clustering.
• Ethical Data Usage: They will learn the ethical considerations involved in gathering and utilizing data to train AI models.
• Prototype Testing: They will develop skills in creating prototypes and testing them with users to gather feedback for iterative improvements.
• Pitching Solutions: They will hone their ability to communicate their AI solution effectively to stakeholders who can support its implementation.

Python Programming Class 11 Notes

Capstone Project:

• A research-based project where students deeply explore a topic, integrate their knowledge, and propose a solution to a real-world problem.
• Opportunity to showcase expertise in Al skills as a final step in learning.

Skills Learned in Capstone Projects:

• Teamwork, problem identification, brainstorming solutions, AI applicability assessment, ethical data usage, prototype testing, and pitching solutions.

Examples of Capstone Projects:

• Stock Prices Predictor, Sentiment Analyzer, Movie Ticket Price Predictor, Students Results Predictor, Human Activity Recognition, and more.

Asking the Right Questions:

Before starting an AI Capstone Project, it’s crucial to determine if the problem exhibits identifiable patterns suitable for AI solutions.

AI Techniques for Problem Solving:

Classification, Regression, Clustering, Anomaly Detection, Recommendation systems are used based on the nature of the problem.

Problem Decomposition:

Breaking down complex problems into manageable parts to analyze and solve effectively.

Critical and Creative Thinking:

• Critical thinking involves analyzing situations based on facts and data.
• Creative thinking involves generating new ideas and solutions.

Design Thinking:

Non-linear, iterative process to understand users, redefine problems, and create innovative solutions through stages like Empathize, Define, Ideate, Prototype, and Test.

Empathy Map:
Tool to understand users’ needs, thoughts, actions, and feelings regarding a problem.

SDGs and Capstone Projects:
Aligning project goals with Sustainable Development Goals (SDGs) to address global challenges sustainably.

Empathy Map-An extremely useful tool for understanding the users’ needs and gaining a deeper insight into the problem at hand is the empathy map. It also helps in deepening that understanding, gaining insight into the user’s behavior.

Ideate -By now the problem is obvious and it is time to brainstorm ways and methods to solve it. At this stage, numerous ideas are generated as a part of the problem-solving exercise. In short, ideation is all about idea generation.

Brainstorm -During a Brainstorming session, students leverage the synergy of the group to generate new innovative ideas by building on others’ ideas. Participants should be able to discuss their ideas freely without fear of criticism. A large number of ideas are collected so that different options are available for solving the challenge.

Brain dump -Brain dump is very similar to Brainstorm; however, it’s done individually. It allows the concerned person to open the mind and let the thoughts be released and captured onto a piece of paper. The participants write down their ideas onto paper or post-it notes and share their ideas later with the larger group.

Brain writing- Brain writing is also very similar to a Brainstorm session and is known as ‘individual brainstorming’. At times only the most confident of team members share their ideas while the introverts keep the ideas to themselves. Brainwriting gives introverted people time to write them down instead of sharing their thoughts out loud with the group.

The post Introduction to Capstone Project Class 11 Questions and Answers appeared first on Learn CBSE.

These Class 11 AI Important Questions Chapter 5 Data Literacy Data Collection to Data Analysis Class 11 Important Questions and Answers NCERT Solutions Pdf help in building a strong foundation in artificial intelligence.

Data Literacy Data Collection to Data Analysis Class 11 Important Questions

Class 11 Data Literacy Data Collection to Data Analysis Important Questions

Important Questions of Data Literacy Data Collection to Data Analysis Class 11 – Class 11 Data Literacy Data Collection to Data Analysis Important Questions

Data Literacy Data Collection to Data Analysis Class 11 Very Short Answer Type Questions

Question 1.
Explain the concept of data literacy and its importance in today’s digital age.
Answer:
Data literacy refers to the ability to find, understand, and use data effectively. In today’s digital age, where data is abundant, data literacy is crucial for making informed decisions, understanding trends, and solving complex problems. It helps individuals and organizations extract valuable insights from data to drive innovation and growth.

Question 2.
What is data preprocessing?
Answer:
Data preprocessing is the process of cleaning and preparing raw data before it is used for analysis or modelling. It involves handling missing data and outliers to ensure the data is accurate and reliable.

Question 3.
How does a line graph differ from a bar graph?
Answer:
A line graph is used to show trends over time with continuous data, while a bar graph is used to compare different categories of data with discrete values.

Question 4.
When would you use a scatter plot?
Answer:
A scatter plot is used to show the relationship between two variables in a set of paired data, helping to identify correlations or trends between the variables.

Question 5.
Name four elements that, when combined, help a tale be interesting?
Answer:

• Involve your audience in the narrative
• Build tension for the conclusion
• Make characters come to life
• The conclusion of the climax is anticipated to be favorable.

Question 6.
What is data literacy?
Answer:
Data literacy refers to the ability to effectively find, understand, evaluate, and use data.

Question 7.
Why is data diversity important in data collection for AI models?
Answer:
Data diversity helps AI models cover more scenarios, improving their reliability and applicability.

Question 8.
What are the levels of measurement in data exploration?
Answer:
The levels of measurement include nominal, ordinal, interval, and ratio scales, each offering different properties for data analysis.

Question 9.
How does statistics contribute to understanding data in AI projects?
Answer:
Statistics, through measures like mean, median, mode, and standard deviation, helps summarize and interpret data for AI decision-making.

Data Literacy Data Collection to Data Analysis Class 11 Short Answer Type Questions

Question 1.
What is data visualization and why is it important?
Answer:
Data visualization is the graphical representation of data to help people understand the significance of data by summarizing and presenting it in a visual form such as charts, graphs, or maps. Data visualization is important because it allows for the exploration and understanding of data patterns, trends, and outliers that may not be apparent in raw data. It helps in making data-driven decisions and communicating information clearly and efficiently.

Question 2.
What is data?
Answer:
Data can be defined as a representation of facts or instructions about some entity (students, school, sports, business, animals etc.) that can be processed or communicated by human or machines.

Question 3.
What do you mean by web scraping?
Answer:
Web scraping is the process of using bots to extract content and data from a website. Web scraping extracts underlying HTML code and, with it, data stored in a database. The scraper can then replicate entire website content elsewhere.

Question 4.
If a matrix has 6 elements, what are the possible orders it can have?
Answer:
4 orders – (1 × 6), (6 × 1), (2 × 3) and (3 × 2)

Question 5.
Construct a 3 × 2 matrix where each element is given by aij = i * j
Answer:
3 × 2 matrix means 6 elements a11 = 1 × 1 a12 = 1 × 2 a 21 = 2 × 1 a22 =2 × 2 a 31 = 3 × 1 a 32 = 3 × 2
Putting all elements in matrix form we get: A = [1 2 2 4 2 3 6]

Question 6.
Find the transpose of the matrix B = [5-1 4 2 3]
Answer:
B = [5 2 -1 3 4 6]

Data Literacy Data Collection to Data Analysis Class 11 Long Answer Type Questions

Question 1.
Discuss the advantages and limitations of using a pie chart in data visualization. Provide examples to illustrate your points.
Answer:
One advantage of using a pie chart is that it can effectively show the proportion of each category in a dataset. For example, a pie chart can be used to visualize the market share of different companies in a specific industry. However, pie charts have limitations, such as difficulty in comparing multiple datasets or showing trends over time. For example, a pie chart would not be suitable for visualizing changes in sales over the course of a year, as it cannot effectively convey this type of information.

Question 2
What is conflict and resolution?
Answer:
Conflict can be defined as a clash between two opposing forces that creates the narrative thread for a story. It is the most exciting and engaging drive in any story.
The following skills can be used to resolve conflict in data storytelling:

• Teamwork
• Problem solving
• Strest management
• Communication
• Emotional flexibility

Question 3.
What do you mean when you talk about a story’s setting and plot?
Answer:
The story’s plot is what takes place. A story’s plot centres on a single event (a pattern break, a turning point) that poses a dramatic question that needs to be resolved as the narrative develops. Location and time are what make up the setting of a story. It might be made up or genuine.

Question 4.
Explain the terms mean, median and mode.
Answer:

Question 5.
Explain the four levels of measurement.
Answer:
Four levels of measurements are Nominal, Ordinal, Interval and Ratio The nominal level of measurement is the simplest or lowest of the four ways to characterize data. Nominal means “in name only”. Colours of eyes, yes or no responses to a survey, gender, smartphone companies, etc all deal with the nominal level of measurement. Ordinal data, is made up of groups and categories which follow a strict order.

For e.g. if you have been asked to rate a meal at a restaurant and the options are: unpalatable, unappetizing, just okay, tasty, and delicious. The options is Ordinal and the data is qualitative,. Like the nominal scale data, ordinal scale data cannot be used in calculations. Data that is measured using the interval scale is similar to ordinal level data because it has a definite ordering but in interval scale data does not have a starting point i.e. zero value.

Temperature scales like Celsius (oC) and Fahrenheit (F) are measured by using the interval scale. Ratio scale data is like interval scale data, but it has a o point and ratios can be calculated. We can add, subtract, divide and multiply the two ratio level variables. Eg: Weight of a person. It has a real zero point. Hence it can be considered as ratio value.

Question 6.
Given the matrices A and B. Calculate A+ B and B – A.

Answer:

Question 7.
The ages of a group of people in a community are: 25,28,30,35,40,45,50,55,60,65 : Write a program to calculate the mean, median, and mode of the ages.
Answer:

Question 8.
A company recorded the daily temperatures (in degrees Celsius) for five consecutive days: 20° C, 22° C, 25° C, 18° C, and 23° C. Determine the variance and standard deviation of the temperatures.
Answer:

Question 9.
Plot a line chart representing the weekly number of customer inquiries received by a customer service center:
Week 1: 150 inquiries
Week 2: 170 inquiries
Week 3: 180 inquiries
Week 4: 200 inquiries

Question 10.
Plot a bar chart representing the number of books sold by different genres in a bookstore:
Fiction: 120 books
Mystery: 90 books
Science Fiction: 80 books
Romance: 110 books
Biography: 70 books

Question 11.
Visualize the distribution of different types of transportation used by commuters in a city using a pie chart:
Car: 40%
Public Transit: 30%
Walking: 20%
Bicycle: 10%

Case-Based Questions

Question 1.
In AI class, Sona heard about the importance of data story in an AI project but she is still having some doubts. Help her in understanding the importance of data story.
Answer:
Like a story, data story also help in engaging the audience across the boundary and culture. The data point holds some information that may be unclear and contextually deficient on its own. Thus it is visualization in the form of statistics, numbers reduces ambiguity, connects data with context and describes a specific interpretation.

Question 2.
Mohan joined a new school as a teacher. He found that the students are weak in some subjects but they are not ready to listen him. So he thought to present their overall performance in the form of graph. Gradually after seeing the graph their performance increased. What do you think what is the reason behind this?

Answer:
The information presented in the graph by Mohan, created a good data story setting, i.e. a strong narrative around the data, making it is easier to understand the pre and post data, existing problem, action taken by Mohan and the resolution of the problem.

Question 3.
Anakha is working on a project involving mood analysis of individuals experiencing depression. She plans to visit hospitals, yoga instructors, mental health care providers, and healthcare institutes. How can Anakha collect data from these organizations and individuals for her project?
Answer:
Surveys, Interviews, Questionnaire

Question 4.
Kalaimathi and her friend are planning to build an admission chatbot for her school. They have collected data from different sources with the help of their friends. Now, they need to arrange it in proper order to analyse it. They plan to classify the data based on the levels of measurement. The data they have collected is as follows: Student Name, Age, Gender, GPA (Grade Point Average), ECA (Extra-Curricular activities), Place, Parent Name, Parent Education level, Distance from school, Fees, Interview rating, No. of years in last school, Admission Test score
Answer:

Question 5.
During a sales analysis, metrics such as sales revenue per month, average sales revenue, and the most popular products sold are examined to comprehend overall performance and aid in decision-making regarding marketing strategies, inventory management, and resource allocation. Which measurements in statistics will facilitate the sales analysis process?
Answer:
Mean, Median Mode

Question 6.
Selvan’s Textiles operates multiple showrooms in a city. Fahad is working on a project to predict the annual sales percentage for the upcoming year. He intends to analyse the trend of sales percentage over time and also the sales percentages in different regions. To facilitate data analysis, he plans to visualize the data using graphs.
a. Which type of graph would be most appropriate for visualizing the trend of sales percentage over time?
b. Which type of graph would be most suitable for comparing sales percentages across different regions?
Answer:
a. Line Chart
b. Pie Chart

Question 7.
Akshith wrote a program to visualize the data analysis of five test and marks got.

The program did not have any errors. But the line graph was not showing up. Could you find the reason why the graph is not shown even though the program has no errors?
Answer:
In order to view the graph we have to use show( ) function in the program. If we write pl.show( ), the graph window will be displayed.

Scenario Based Question Answer:

Scenario: Emily is a data scientist tasked with developing an AI model to predict customer, preferences for a new product launch. She needs to ensure her team understands the fundamentals of data literacy and effective data collection methods. During a team meeting, she decides to clarify key concepts through scenario-based questions.

Question 1.
Question: Emily explains the importance of data literacy in AI projects. She asks her team, “Why is data literacy crucial for leveraging artificial intelligence effectively?”
Answer:
Data literacy is crucial because AI relies on converting large amounts of raw data into actionable insights. Without understanding how to find, evaluate, and use data effectively, AI models cannot generate meaningful outcomes. Data literacy ensures that data is collected ethically, organized correctly, and analyzed accurately to derive insights that support decisionmaking processes in AI applications.

Question 2.
Question: Emily discusses the challenges and considerations in data collection. She asks, “What factors should we consider when deciding how much data to collect for our AI model?”
Answer:
When deciding how much data to collect, several factors should be considered:

• Model Complexity: More complex AI models, such as those used in medical AI, typically require larger volumes of data to ensure accuracy and reliability.
• Data Diversity: Collecting diverse data ensures the model covers various scenarios and improves its robustness.
• Application Scope: Understanding where and how the AI model will be used helps determine the types and amounts of data needed.
• Iterative Process: Data collection is an iterative process that involves understanding the problem, defining data requirements, identifying data sources (primary and secondary), and continuously refining the dataset based on model performance.

Data Literacy Data Collection to Data Analysis Class 11 Notes

Data Literacy:

• Definition: Data literacy refers to the ability to effectively find, understand, evaluate, and use data.
• Skills: Includes collecting, organizing, checking data quality, analyzing, interpreting results, and using data ethically.
• Importance: Essential for leveraging AI and making informed decisions based on actionable insights from data.

Data Collection:

• Definition: Gathering data from various sources for analysis.
• Methods: Includes primary (surveys, interviews, observations, experiments) and secondary (social media, web scraping, satellite data) sources.
• Considerations: Importance of data diversity and quantity, depending on the complexity and application of AI models.

Exploring Data:

• Purpose: Understanding data characteristics (typical, unusual, spread) to identify and correct issues that could affect analysis outcomes.
• Levels of Measurement: Classifies data into nominal, ordinal, interval, and ratio scales based on properties like order and mathematical operations.

Statistical Analysis of Data:

• Central Tendency: Measures (mean, median, mode) used to summarize the central value of a dataset.
• Python Libraries: Utilizes statistical functions like mean, median, mode, variance, and standard deviation for analysis.

The post Data Literacy Data Collection to Data Analysis Class 11 Questions and Answers appeared first on Learn CBSE.

These Class 11 AI Important Questions Chapter 6 Machine Learning Algorithms Class 11 Important Questions and Answers NCERT Solutions Pdf help in building a strong foundation in artificial intelligence.

Machine Learning Algorithms Class 11 Important Questions

Class 11 Machine Learning Algorithms Important Questions

Important Questions of Machine Learning Algorithms Class 11 – Class 11 Machine Learning Algorithms Important Questions

Machine Learning Algorithms Class 11 Very Short Answer Type Questions

Question 1.
Name the various stages of design thinking?
Answer:
Empathize, Define, Ideate, Prototype and Test

Question 2.
What are the questions method in 5 W 1 H ?
Answer:
Who, What, Where, When, Why and How

Question 3.
What is the primary difference between classification and regression?
Answer:
Classification predicts discrete values, while regression predicts continuous values

Question 4.
Provide examples of classification problems in real-life scenarios.
Answer:
Examples of classification problems include email spam detection, handwritten character. recognition, and sentiment analysis in social media posts.

Question 5.
What are some common applications of clustering techniques?
Answer:
Common applications of clustering techniques include market segmentation, statistical data analysis, social network analysis, image segmentation, and anomaly detection.

Question 6.
List the types of clustering methods.
Answer:
Types of chastering methods include partitioning clustering, density-based clustering, distribution model-based clustering, hierarchical clustering, and fuzzy clustering.

Question 7.
How do recommendation systems suggest products to users?
Answer:
By analyzing browsing history and identifying purchase patterns.

Question 8.
What technology does a social media platform use to tag people in photos?
Answer:
Facial recognition.

Question 9.
How do chatbots understand and respond to human language?
Answer:
Through natural language processing (NLP) and machine learning.

Question 10.
What is one benefit of using machine learning in medical diagnosis?
Answer:
Early detection of diseases.

Question 11.
What is a challenge of using machine learning in medical diagnosis?
Answer:
Model interpretability.

Question 12.
How does supervised learning detect spam emails?
Answer:
By training on a labeled dataset to recognize spam patterns.

Question 13.
What does regression analysis use to forecast future sales?
Answer:
Past sales data, marketing spend, and seasonal trends.

Machine Learning Algorithms Class 11 Short Answer Type Questions

Question 1.
What is design thinking?
Answer:
It is possible to fix difficulties by using design thinking. It first addresses the needs of the consumer. Design thinking is therefore “people-oriented,” which means that it relies on actual consumer (or human) interactions with a product or service rather than how businesses anticipate handling it.

Question 2.
What is Ideate?
Answer:
Idea generation is a creative method that designers use in meetings to develop ideas (such as brainstorming). The design thinking process has a third step, which is this.

Question 3.
What is Machine learning? Name the three methods of machine learning.
Answer:
Machine learning (ML) is a type of artificial intelligence (AI) focused on building computer systems that learn from data. It uses algorithms that learn from data to make predictions. The predictions can be generated through three methods known as supervised learning, unsupervised learning and reinforcement learning. In supervised learning algorithms learn patterns from existing data, in unsupervised learning, they discover general patterns in data and reinforcement learning where they learn through reward and punishment methods.

Question 4.
How are correlation measures used in AI applications?
Answer:
Feature selection: Identify features highly correlated with the target variable, potentially indicating relevance for prediction. Exploratory data analysis: Understand relationships between variables and identify potential or anomalies. Recommender systems: Recommend items based on past user behaviour and correlations between items purchased together.

Question 5.
Name some examples of regression algorithms?
Answer:
Examples of regression algorithms include Linear Regression, Polynomial Regression, Ridge Regression, Lasso Regression, Elastic Net Regression, Support Vector Regression (SVR), Decision Tree Regression, Random Forest Regression, and Gradient Boosting Regression. These algorithms are used to predict continuous numerical values are widely applied in various fields such as finance, economics and engineering.

Question 6.
What are regression algorithms used for?
Answer:
Regression algorithms are used for predicting continuous numerical values based on input features. They are widely applied in various fields such as finance for stock price forecasting, economics for predicting economic indicators, healthcare for disease progression estimation, and engineering for predicting product performance. Regression analysis helps uncover relationships between variables and make informed predictions for future data points.

Question 7.
What is Linear regression? Give two applications of regression in machine learning?
Answer:
Linear Regression is a supervised learning algorithm. It makes use of one independent variable X to predict the outcome of a second dependent variable Y . In machine learning, regression is used to predict outputs and forecast trends.

Question 8.
How can outliers impact regression analysis?
Answer:
A data point that deviates noticeably from other observations is called an outlier. An outlier could be the consequence of experimental error, measurement fluctuation, or an indication of potentially obtained data. They can greatly alter the regression line and impair prediction accuracy, which will bias the findings of regression analysis.

Machine Learning Algorithms Class 11 Long Answer Type Questions

Question 1.
What is design thinking and explain about the stages of design thinking?
Answer:
A method for finding innovative solutions to issues is design thinking. It does this by prioritising the demands of the consumer. Therefore, design thinking is “people-oriented,” which implies that it relies on data about how users (consumers) interact with a good or’service rather than how businesses anticipate handling it.
This process consists of five different stages as shown beloe:

Empathize- Observe customers in the first stage to better understand how they engage with or are impacted by the product or issue:

• Define-To define the problem that has to be solved, you gather the observations from the first stage in the second stage.
• Ideate-The next stage is to start gathering solutions to the issue you’ve identified.
• Prototype-This is the stage where an idea becomes a real solution.
• Test-Once a prototype solution is made available to customers, it is important to watch how they use it.

Question 2.
What is right questioning and what are they?
Answer:
Designers must frequently engage with clients when creating solutions utilising the design thinking framework in order to gather precise information about the issues and user expectations.

To gather relevant facts and information from customers, a simple and efficient method of questioning called the 5W1H method can be used.

WHO-

• Which users are they?
• What are their characteristics?

WHAT-

• What action do users take?
• What tasks are they capable of completing?

WHERE-

• Where should the assignment be completed?
• Where can the task be better accomplished (change of location)?

WHEN-

• When do the users carry out these actions?
• What is the frequency? … Everyday, occasionally?

WHY-

• Why must the users complete these tasks?
• Why are they unable to alter how they carry out the task?

HOW-

• How to perform the task?
• Can this approach be applied in other circumstances?

Question 3.
What is Ideate and what are the techniques are used?
Answer:
Idea generation is a creative method that designers use in meetings to develop ideas (such as brainstorming). The design thinking process has a third step, which is this. Participants gather with an open mind and present as many solutions as they can in order to address issues in a comfortable and judgment-free setting. Ideation will help you:

• Ask the right questions and innovate.
• Combine team members’ perspectives and advantages.
• Get rid of the obvious solutions in your mind and let your team surpass them.

Ideation techniques:
Below are the techniques are used to generate various ideas.
Brainstorm: It is very popular with design teams because they can expand in all directions. Bran dump: Although it is done individually, brain dump is extremely similar to brainstorm. It enables the participants to open up, express their feelings, and write their ideas down.

Brain writing: Brain writing is also referred to as “Individual brainstorming” and is quite similar to brainstorming. In this situation, only the team members who are the most selfassured speak up, whilst introverts prefer to keep their opinions to themselves.

Question 4.
How does classification model work?
Answer:

• Classes or Categories: Data is organized into different groups, such as “positive” and “negative,” representing distinct outcomes.
• Features or Attributes: Each data instance is described by specific characteristics or attributes, providing information about the instance.
• Training Data: The classification model learns from a dataset containing labeled examples, associating each instance with a class label.
• Classification Model: An algorithm or technique is applied to the training data to build a model that can predict the class labels of new instances.
• Prediction or Inference: Once trained, the model is used to classify new data instances based on the patterns learned during training.

Question 5.
Explain the types of clustering.
Answer:

• Partitioning Clustering: This method divides data into non-hierarchical groups using a centroid-based approach, where data points are grouped into k clusters based on the proximity to cluster centroids. Eg: K-Means Clustering algorithm.
• Density-Based Clustering: This technique identifies clusters by connecting 168 highly dense areas in the dataset, allowing for arbitrarily shaped clusters to form as long as dense regions are connected. Eg: DBSCAN algorithm.
• Distribution Model-Based Clustering: Here, data is clustered based on the probability of belonging to a particular distribution, often assuming Gaussian distributions. Eg: ExpectationMaximization Clustering algorithm, using Gaussian Mixture Models (GMM).
• Hierarchical Clustering: This approach creates a tree-like structure, or dendrogram, to cluster data without requiring the pre-specification of the number of clusters. Eg: Agglomerative Hierarchical algorithm.

Question 6.
Write any two advantages and disadvantages of linear regression.
Answer:
Advantages of Linear regression:

• Simple technique and easy to implement
• Efficient to train the machine on this model Disadvantages of Linear regression
• Sensitivity to outliers, which can significantly impact the analysis.
• Limited to linear relationships between variables.

Question 7.
What are the steps involved in k-NN algorithm?
Answer:

• Select the number K of the neighbors
• Calculate the Euclidean distance of K number of neighbors
• Take the K nearest neighbors as per the calculated Euclidean distance.
• Among these k neighbors, count the number of the data points in each category.
• Assign the new data points to that category for which the number of the neighbor is maximum.
• Our model is ready

Question 8.
What are the steps involved in k-means clustering?
Answer:

• Select the number K to decide the number of clusters.
• Select random K points or centroids. (It can be other from the input dataset).
• Assign each data point to their closest centroid, which will form the predefined K clusters.
• Calculate the variance and place a new centroid of each cluster.
• Repeat the third steps, which means reassign each datapoint to the new closest centroid of each cluster.
• If any reassignment occurs, then go to step-4 else go to FINISH.
• The model is ready.

Case-Based Questions:

Question 1.
Rohan has been offered a job that he wants, but he doesn’t have the facility to reach the office premises and also doesn’t have enough money to buy a car.” What do you think is Rohan’s main problem?
Answer:
Lack of money and commuting problem.

Question 2.
Neha is unable to understand the following terms help her in understanding the same.
Answer:

• Brain storm-During a brain storm session participants are allowed to discuss their ideas freely without fear or criticism. A large number of ideas are collected for solving the problem.
• Brain dump-Brain dump is done individually. It allows only the concerned person to open the mind and thoughts are captured onto a piece of paper and share the ideas with the large group.
• Brain writing-It is also called “individual brainstorming” only the confident the members, share their ideas and other members of the team write down their ideas on paper. So that other members can elaborate the idea.

Question 3.
Asmita is developing an AI-driven recommendation system for a retail e-commerce platform. What type of machine learning method might she have used to:
a. Train the model with details of past purchases, user interactions, and product ratings?
b. Identify groups of similar users or products based on their browsing behavior?
Answer:
a. Supervised Learning b. Unsupervised Learning

Question 4.
Suppose you are a sales manager tasked with forecasting sales for the upcoming quarter. Describe how you would use linear regression in this scenario, including the data you would collect and the.steps involved in the analysis.
Answer:
In sales forecasting, linear regression can be used to predict future sales based on historical sales data, marketing spends, seasonality, and other factors. The sales manager would collect historical sales data along with relevant variables such as advertising expenditures, promotional activities, and economic indicators. By analysing this data using linear regression, the sales manager can forecast future sales trends and adjust strategies accordingly.

Question 5.
Observe the scatter plot showing the amount of sleep needed per day by age.

What type of correlation is shown here?
Answer:
As age increases (moving along the x-axis toward greater numbers), the amount of sleep needed decreases (y-values decreasing). This is a negative correlation. This indicates that as individuals grow older, they generally require less sleep.

Question 6.
Ramesh is working on an assignment where he needs to categorize real-world applications of Artificial Intelligence (AI) into two groups: Classification and Clustering. While his initial attempt seems partially correct, his teacher identified a mistake.

1. Classification –  Medical Diagnosis Sentiment Analysis Fraud Detection
2. Clustering – E-mail Spam Detection Identifying high risk patient groups Anomaly detection in network traffic

Identify the mistake
Answer:
E-mail Spam Detection is categorized under Clustering, but it should be under Classification.

Question 7.
Researchers are developing a new blood test to detect cancer early. The test analyzes various biomarkers (indicators) in a patient’s blood sample. The test results need to be categorized accurately. A positive result should indicate the presence of cancer cells, while a negative result should indicate no.cancer. Which type of classification algorithm would be most suitable for this new cancer detection blood test?
Answer:
Binary Classification

Scenario Based Question Answer:

Scenario 1: Online Shopping Recommendations

Question:
You were browsing an online shopping app and looked at a pair of shoes. A few moments later, you received recommendations to purchase socks. Explain how machine learning is used in this scenario.
Answer:
Machine learning is used in this scenario through recommendation systems. These systems analyze your browsing history and identify patterns in the data to make suggestions. When you look at shoes, the system recognizes a common pattern that people who buy shoes also often buy socks. By learning from historical data and patterns, the system recommends socks to enhance your shopping experience and increase the likelihood of a purchase.

Scenario 2: Facial Recognition on Social Media

Question:
You uploaded a photo to a social media platform, and the platform automatically identified and tagged you and your friends in the photo. Describe how machine learning contributes to this feature.
Answer:
Machine learning contributes to this feature through facial recognition technology. The platform uses neural networks, a type of ML algorithm, to analyze the features of faces in photos. By training the model on a large dataset of labeled images (photos with identified people), the system learns to recognize and distinguish between different faces. When you upload a new photo, the trained model can identify and tag people by matching their facial features with those in its database.

Scenario 3: Chatbot Conversations

Question:
You had a conversation with a chatbot that seemed very natural and responsive. How does machine learning enable chatbots to engage in such conversations?
Answer:
Machine learning enables chatbots to engage in natural conversations through natural language processing (NLP) and machine learning algorithms. NLP helps the chatbot understand and interpret human language, while machine learning allows the chatbot to learn from previous interactions.

By training on vast amounts of conversation data, the chatbot can identify patterns, understand context, and generate appropriate responses, making the conversation feel natural and responsive.

Scenario 4: Medical Diagnosis

Question:
A hospital uses a machine learning model to assist doctors in diagnosing diseases. What are the key benefits and challenges of using machine learning in medical diagnosis?
Answer:
Benefits:

• Accuracy and Speed: Machine learning models can quickly analyze vast amounts of medical data to identify patterns and make accurate diagnoses.
• Early Detection: ML can detect subtle signs of diseases that may be missed by human doctors, leading to earlier and more effective treatment.
• Personalized Medicine: ML can tailor diagnoses and treatment plans based on individual patient data.

Challenges:

• Overfitting: Models might perform well on training data but poorly on new data, leading to incorrect diagnoses.
• Bias: If the training data is biased, the model’s predictions will also be biased, potentially leading to unfair or inaccurate diagnoses.
• Interpretability: Some ML models, like neural networks, act as “black boxes,” making it difficult to understand how they arrive at their conclusions, which can be problematic in critical medical decisions.

Scenario 5: Supervised Learning in Email Spam Detection

Question:
Explain how supervised learning is applied in detecting spam emails in your inbox.
Answer:
Supervised learning is applied in email spam detection by training a model on a labeled dataset of emails. Each email in the dataset is labeled as “spam” or “not spam.” The model learns to recognize patterns and features associated with spam emails (e.g., certain keywords, sender information). After training, the model can analyze new emails and predict whether they are spam or not based on the learned patterns. This allows your email service to filter out spam emails before they reach your inbox.

Scenario 6: Regression Analysis in Sales Forecasting

Question:
A company uses regression analysis to forecast future sales based on past sales data, marketing spend, and seasonal trends. Explain how this is done.
Answer:
Regression analysis is used to model the relationship between the dependent variable (future sales) and independent variables (past sales data, marketing spend, and seasonal trends). By analyzing historical data, the regression model identifies patterns and correlations between these variables.

The model then uses these relationships to predict future sales. For example, it might find that higher marketing spend and certain seasonal periods lead to increased sales. Using the regression equation, the company can input expected marketing spend and seasonal factors to forecast future sales accurately.

Machine Learning Algorithms Class 11 Notes

Daily Life Examples of Machine Learning:

• Online shopping apps recommend related items (e.g., socks after looking at shoes).
• Entertainment platforms suggest movies based on previously watched ones.
• Facial recognition technology on social media.
• Chatbots that engage in natural conversations.

Machine Learning (ML) in a Nutshell:

• Definition: ML is a subset of AI focused on teaching computers to learn from data and make decisions without explicit programming.
• Data Types: ML algorithms learn from various data types, including images, text, sensor readings, and historical records.
• Common Algorithms: Examples include decision trees, neural networks, and support vector machines.
• Applications: ML is used in recommendation systems, speech recognition, medical diagnosis, autonomous vehicles, chatbots, personalized ads, and fraud detection.

Challenges in ML:

• Overfitting: Models become too specialized on training data, leading to poor performance on new data.
• Bias: Training data biases can result in biased predictions.
• Interpretability: Some models act as “black boxes,” making them hard to interpret.

Types of Machine Learning:

Supervised Learning:

• Definition: The model learns from labeled data (input data with correct output).
• Examples: Linear regression, logistic regression, decision trees, support vector machines, neural,networks.

Unsupervised Learning:

• Definition: The model works with unlabeled data to find hidden patterns or structures.
• Examples: K-means clustering, hierarchical clustering, principal component analysis, autoencoders.

Reinforcement Learning:

• Definition: An agent learns to make decisions by interacting with an environment to maximize cumulative rewards.
• Examples: Q-learning, deep Q-networks, policy gradients, actor-critic methods.

Regression:

• Definition: A statistical technique to model the relationship between a dependent variable and one or more independent variables.
• Purpose: To understand and predict the value of the dependent variable based on the independent variables.

Linear Regression:

• Simple Linear Regression: Uses a single independent variable.
• Multiple Linear Regression: Uses more than one independent variable.

Correlation Types:

• Positive Correlation: Both variables move in the same direction.
• Negative Correlation: Variables move in opposite directions.
• Zero Correlation: No apparent relationship between variables.
• Pearson’sr: Measures the strength and direction of the linear relationship between two continuous variables.

Classification:

• Definition: Categorizing data into predefined classes or categories.

Types of Classification:

• Binary Classification: Two class labels.
• Multi-Class Classification: More than two class labels.
• Multi-Label Classification: Each example may belong to multiple class labels.
• Imbalanced Classification: Unequally distributed class labels.

K-Nearest Neighbors (KNN):

Definition: A non-parametric supervised learning technique for classification and regression.

Steps:

• Select the number K of neighbors.
• Calculate the Euclidean distance of K neighbors.
• Take the K nearest neighbors.
• Assign the new data point to the category with the maximum neighbors.
• Applications: Image recognition, recommendation systems, healthcare diagnostics, text mining, anomaly detection.
• Advantages: Easy to implement, no explicit training phase, suitable for both classification and regression.
• Limitations: Computationally expensive, sensitive to distance metric and number of neighbors, not suitable for high-dimensional data.

The post Machine Learning Algorithms Class 11 Questions and Answers appeared first on Learn CBSE.

These Class 11 AI Important Questions Chapter 7 Leveraging Linguistics and Computer Science Class 11 Important Questions and Answers NCERT Solutions Pdf help in building a strong foundation in artificial intelligence.

Leveraging Linguistics and Computer Science Class 11 Important Questions

Class 11 Leveraging Linguistics and Computer Science Important Questions

Important Questions of Leveraging Linguistics and Computer Science Class 11 – Class 11 Leveraging Linguistics and Computer Science Important Questions

Leveraging Linguistics and Computer Science Class 11 Very Short Answer Type Questions

Question 1.
What is data?
Answer:
Data are specific facts, figures, or information units that are obtained through observation and are primarily numerical in nature. A set of values for qualitative or quantitative characteristics pertaining to one or more individuals or objects is also referred to as data.

Question 2.
What do you mean by skewness?
Answer:
A distribution’s skewness serves as a gauge of its symmetry. A distribution’s maximum point is called its mode. The distribution is skewed, or asymmetrical, if the tail on one side of the mode is longer or fatter than the tail on the other.

Question 3.
What are the two types of skewed distributions?
Answer:

• Left-skewed distribution: A left-skewed distribution is an asymmetrical distribution with a negative skew in which the left tail is longer than the right.
• Right-skewed distribution: A distribution that is positively skewed and has a longer tail on the right side than the left is referred to as being right-skewed.

Question 4.
List the steps of NLP Processing.
Answer:
The steps of Natural Language Processing (NLP) processing include: Lexical Analysis, Syntactic Analysis, Semantic Analysis, Discourse Integration, Pragmatic Analysis.

Question 5.
What does pragmatics study in linguistics?
Answer:
Pragmatics studies how context influences the interpretation of meaning in communication.

Question 6.
What are the components of a linguistic sign?
Answer:
The components are the signifier (form) and the signified (meaning).

Question 7.
What is the focus of sociolinguistics?
Answer:
Sociolinguistics focuses on how language varies and changes in social groups.

Question 8.
Define syntax.
Answer:
Syntax is the set of rules that governs the structure of sentences.

Question 9.
What is the primary goal of Natural Language Processing (NLP)?
Answer:
The primary goal of NLP is to enable computers to understand, interpret, and respond to human language.

Question 10.
Give an example of a phoneme.
Answer:
An example of a phoneme is the sound /p/ in “pat” and /b/ in “bat.”

Question 11.
What is semantics concerned with?
Answer:
Semantics is concerned with the meaning of words and sentences.

Question 12.
What does morphology study in linguistics?
Answer:
Morphology studies the structure and form of words, including affixes, roots, and stems.

Question 13.
What is the significance of a lexicon in linguistics?
Answer:
A lexicon is a vocabulary of a language, including its words and expressions.

Question 14.
What is an example of a pragmatic inference?
Answer:
An example is understanding “Can you pass the salt?” as a request rather than a question about ability.

Leveraging Linguistics and Computer Science Class 11 Short Answer Type Questions

Question 1.
Identify in which category of data type should the following be stored? (Categorical or numerical)
A-Pin number
B-Language
C-Location
D-Marks
E-Date of birth
Answer:
A-Pin number -Categorical data
B-Language-Categorical
C-Location -Categorical
D-Marks-Numerical
E-Date of birth-Numerical

Question 2.
How does NLP help in email filtering? Give a real-life example.
Answer:
NLP is essential for determining if an incoming email is “important” or “spam,” which – helps with email sorting and improves user experience. It makes precise filtering decisions by analysing email content, spotting patterns, and applying language processing tools. Actual For instance: Situation: Imagine a professional receiving a barrage of emails, including promotional material, work-related correspondence, and possibly spam. Email text can be scanned by NLP algorithms, which check for important factors such as sender reputation, email structure, and keywords. Important work emails are sorted and distinguished from less important or spam messages by using natural language processing (NLP) for email filtering.

Question 3.
Explain categorical data with an example.
Answer:
A data type with two or more categories is called categorical data. For instance, a pizza restaurant may categorize your orders according on the kind of pizza you choose-chicken, pepperoni, onion capsicum, etc. The categorical data in this instance is the kind of pizza that was ordered.

Question 4.
Distinguish between the mean, median, and mode.
Answer:

Question 5.
Briefly explain the two types of chatbots
Answer:
1. Rule-based Chatbots:

• They work on predefined rules and decision trees to provide consistent and accurate answers to specific questions.

Advantages:

• Easy to develop and maintain, Provide 24/7 availability for immediate and consistent support.

Limitations:

• Struggle with understanding complex language
• High development costs and resource requirements.

2. AI-powered Chatbots :

Utitize natural language processing (NLP) and machine learning algorithms to respond based on learned patterns and interactions, providing personalized responses.

Advantages:

• Offer personalized interactions based on user preferences and history.
• Improve efficiency and cost savings by automating tasks and reducing service costs.

Limitations:

• Prone to biases from training data and lack of transparency in decision-making
• Ethical considerations regarding privacy. manipulation, and responsible use.

Question 6.
Briefly explain the classification problem. Give at least two examples.
Answer:
Classification is a type of machine learning problem where the goal is to predict the categorical class labels of new observations based on past data. In classification, the output variable is a category or class label. Two examples of classification problems are: Email Spam Detection: Given a set of emails labeled as spam or not spam, the task is to classify new email messages as either spam or not spam. Handwritten Digit Recognition: Classifying images of handwritten digits (0-9) into their corresponding numeric values.

Question 7.
Define the following:
(a) Intent
(b)Entity
(c) Dialog
Answer:
(a) In the context of the provided document summaries, the term “intent” primarily refers to the purpose or objective behind a user’s contact with chatbots or the desired outcome of a particular communication.

(b) An entity is a noun: a person, place, or thing.
Eg: If a user asks, “What are the hours for the Bangalore office?”, then providing business hours is the intent and Bangalore is the entity. 187

(c) A dialog is a flowchart-an IF / THEN tree structure that illustrates how a machine will respond to user intents. A dialog is what the machine replies after a human asks a question.

Leveraging Linguistics and Computer Science Class 11 Long Answer Type Questions

Question 1.
The following data shows the test marks obtained by a group of students. Draw a frequency table for the data.

Answer:
The frequency table for the data is:

Question 2.
If a positively skewed distribution has a median of 50 , which of the following statement is true?
A) Mean is greater than 50
B) Mean is less than 50
C) Mode is less than 50
D) Mode is greater than 50
E) Both A and C
F) Both B and D
Answer:
In a positively skewed distribution, the mean is typically greater than the median, and the mode is less than the median. Therefore, the correct is E) Both A and C. That is, the mean is greater than 50 and the mode is less than 50.

Question 3.
Explain the structure of a chatbot.
Answer:
A chatbot has a “frontend” and a “backend”.
The frontend of a chatbot serves as the messaging channel through which users interact, providing a user-friendly interface. The one limitation of the frontend is that it may lack contextual understanding, meaning it might struggle to grasp the full meaning or context of user messages beyond the immediate input.

The backend of a chatbot is where the hard work takes place. The backend operates application logic and has enough memory to remember earlier parts of a conversation as dialog continues.

Chatbots understand a question by breaking it into parts and relating those parts to things in its memory. A chatbot’s goal is to identify entities and intents, then use what it is found to trigger a dialog. ‘

Question 4.
“A syntax tree is created as part of the procedure to visually represent semantic links.” Identify the phase of NLP processing?
Answer:
The phase of NLP processing that involves the creation of a syntax tree to visually represent semantic links is the Syntactical Analysis phase. In syntactical analysis, the aim is to check the grammar, word layouts, and word relationships in a given text. One of the key tasks in this phase is to create a syntax tree, also known as a parse tree, which represents the grammatical structure of the sentence and visually displays the relationships between words. This helps in understanding the syntactical constructs and semantic relationships within the text, thereby aiding in the overall comprehension and analysis of the language.

Case-Based Questions

Question 1.
Seema wants to depict her data in the form of a graph that show changes overtime. Which of the following graph would you suggest her?
A-Line graph
B-Bar graph
C-Pie graph
Answer:
A and B

Question 2.
A sample data is collected from two hundred students of class XII. Is students major quiz score and lab assignment score is recorded. Find out the variables and the case in this sample.
Answer:

• Cases- As cases are the collection of the objects here all the data sets about the sample is considered as cases. Here the Students are the cases
• Variable-Variable is a characteristics measure and whose value keep on changing during the program. Here students major quiz score and lab assignment score are variables.

Question 3.
Help Isha in understanding the label of measurement for the following data. Answer:
A- Nationality (Indian or Foreigner)
B-Working status (Employed or non-employed)
C- Today’s temperature
D- Preferred subject (Physics, chemistry, math, artificial intelligence)
E- Product rating (1-5 * STAR)-
F- Your weight (In kilogram)
Answer:
A- Nationality (Indian/Foreigner)-Nominal
B-Working status (Employed/non-employed)-Nominal
C- Today’s temperature-Interval
D- Preferred subject (Physics, chemistry, math, artificial intelligence)-Ordinal
E- Product rating (1-5 * STAR)-Ratio
F- Your weight (In kilogram)-Ratio

Question 4.
Imagine you are a customer service manager at a global e-commerce company facing increasing customer inquiries across multiple channels. To alleviate the strain on your support team and enhance customer satisfaction, you decide to implement a chatbot solution. Develop a case study outlining the challenges you faced, the criteria you used to select a chatbot platform.
Answer:

• High Volume of Customer Inquiries: Managing the increasing number of customer inquiries across various channels, including email, live chat, and social media, was overwhelming for the support team.
• Response Time and Availability: Ensuring timely responses and 2.4./7 availability for customer queries without round-the-clock human support.
• Scalability: The need for a solution that could scale with the company’s growth and handle a diverse range of customer queries effectively.
• Consistency in Responses: Maintaining consistency in responses across different channels and ensuring accurate information delivery to customers.

Criteria for Selecting a Chatbot Platform:

• Natural Language Processing (NLP) Capabilities: A chatbot platform with advanced NLP capabilities to understand and respond effectively to natural language queries.
• Integration with Multiple Channels: Ability to seamlessly integrate with various communication channels like website chat, social media etc.
• Customization and Personalization: Platform that allows customization to reflect the brand tone and offers personalized experiences to customers.
• Analytics and Reporting: Provision for detailed analytics on chatbot interactions, customer satisfaction metrics etc.
• Scalability and Flexibility: Platform that can scale with the company’s growth and adapt to evolving customer needs.
• Training and Support: Adequate training resources and customer support from the chatbot platform provider to ensure smooth implementation and ongoing maintenance.

Question 5.
Imagine you are a customer experience lead at a telecommunications company, currently relying on a rule-based chatbot to handle customer queries. However, due to limitations in scalability and adaptability, you are considering a transition to an Albased chatbot solution. Detail the challenges faced during the transition, and the observed impact on customer service efficiency and satisfaction.
Answer:
Challenges Faced During Transition:

• Knowledge Base Transition
• Training Data Quality
• Technical Integration
• Training and Skill Development
• Regulatory Compliance

Observed Impact on Customer Service Efficiency and Satisfaction:

• Enhanced’Scalability
• Adaptability to Varied Queries
• Personalized Customer Interactions
• Reduced Response Time
• Analytics and Insights

Question 6.
Reena’s e-commerce website struggles with a high volume of customer inquiries. They’re considering a chatbot to help. Should it be rule-based or AI-powered?
Here’s how Reena can decide: Rule-based chatbot:
Pros: Faster development, lower cost, perfect for answering simple, repetitive questions (e.g., order tracking, delivery status).
Cons: Limited understanding of complex questions, can’t adapt to new inquiries, frustrating for users if it can’t answer their specific needs. AI-powered chatbot:
Pros: Handles complex questions and learns over time, improves customer experience with more natural conversations.
Cons: Higher development cost, requires training data and ongoing maintenance, may not be perfect initially and could misunderstand complex inquiries.
What factors Reena must evaluate to decide which chatbot best suits their needs and budget.
Answer:
Factors to consider:

• Types of customer inquiries: If most questions are repetitive (tracking, returns), a rule-based chatbot might suffice.
• Complex inquiries (product recommendations, troubleshooting) would benefit from AI.
• Budget: AI chatbots require more investment.
• Technical expertise: Implementing and maintaining AI chatbots might require additional resources.

Question 7.
You’re a compliance officer for a bank launching a new chatbot for customer service. The chatbot can answer basic questions about accounts, transfers, and suspicious activity. However, some customers might ask for specific account details like balances or transaction history. How can you ensure the chatbot complies with data protection regulations and protects sensitive financial information, while still being helpful to customers?
Answer:
To address this, you can implement a two-pronged approach:

1. Security: Require multi-factor authentication (PIN, password, etc.) before revealing sensitive information. The chatbot can also deflect requests for specific details by offering alternative solutions (e.g., suggesting the mobile app for checking balances).
2. Transparency: Inform users upfront that the chatbot can’t share sensitive data without authentication. Additionally, provide an easy option to connect with a live customer service representative for complex issues requiring such information disclosure.

Question 8.
Rahul’s marketing team developed an app that understands text input. He wants to use the app to analyse customer reviews and improve the clarity of their promotional messages. How can Rahul leverage discourse integration techniques based on customer conversations to craft more effective marketing messages?
Answer:
Rahul is able to examine linkages and references in reviews to determine what aspects of a product users fipd appealing (like a long battery life) and how they express their wants (like “ideal for travel”). Rahul may develop concise and pertinent communications that speak to client priorities by utilising strategies like connectives (“This phone boasts a powerful battery, therefore keeping you connected all day”) and references (“We heard you need a longlasting battery…”).

Question 9.
Jyoti is tasked with improving a healthcare chatbot for a hospital. Patients often ask the chatbot repetitive questions about symptoms, appointment scheduling, and basic medical information. How can Jyoti leverage NLP techniques to make the chatbot more helpful and engaging for patients?
Answer:
Jyoti can comprehend the user’s objective (making an appointment, for example) and particulars (such as the type of doctor) by using methods like intent recognition and entity recognition. The chatbot can accurately respond to simple medical queries by utilising information retrieval from a medical knowledge base.

Question 10.
Ashmita’s cinema chain is launching a new mobile app with a chatbot for ticket purchases and movie recommendations. Moviegoers often ask repetitive questions about showtimes and struggle to discover new movies they might enjoy. How can Ashmita leverage the chatbot functionalities to address these issues and improve customer experience?
Answer:
Ashmita can concentrate on features that incorporate a recommendation engine and provide straightforward answers to frequently asked questions (e.g., showtimes, ticket costs). By analysing user data (previous purchases), the chatbot can recommend films or forthcoming releases that fit the user’s tastes. This individualised approach can make watching films more interesting and fulfilling by answering customers’ most frequent queries and assisting them in finding new favourites.

Scenario Based Question Answer:

Scenario 1.: Understanding the Application of Linguistics

Question:
Imagine you are working as a marketing strategist for a global company launching a new product. How would your understanding of linguistics help in crafting effective marketing messages for different cultural regions?
Answer:
Understanding linguistics would help in several ways:

• Tailoring Messages: By knowing the structure, semantics, and pragmatics of different languages, I can craft messages that resonate with specific cultural nuances and expressions.
• Cultural Sensitivity: Sociolinguistics knowledge ensures that marketing messages are culturally appropriate and avoid misunderstandings or offensive content.
• Improved Communication: Effective use of language can influence behavior and enhance the persuasiveness of marketing campaigns, ensuring that the product appeals to the target audience in various regions.

Scenario 2.:,Implementing NLP in Customer Service

Question:
You are tasked with improving customer service for an e-commerce platform. How would you implement Natural Language Processing (NLP) to enhance the customer experience?
Answer:
Implementing NLP can enhance customer service in the following ways:

• Virtual Assistants: Integrating NLP-powered virtual assistants like chatbots to handle common queries, order tracking, and troubleshooting, providing instant responses to customers.
• Sentiment Analysis: Using sentiment analysis to gauge customer satisfaction from feedback and reviews, enabling proactive improvements in service.
• Email Filtering: Automating email filtering to categorize and prioritize customer emails, ensuring urgent issues are addressed promptly and spam is minimized.
• Document Summarization: Summarizing lengthy customer inquiries and support tickets for quicker resolution by support staff.

Scenario 3.: Addressing Ambiguity in Language with AI

Question:
You are developing an AI system for a translation service. How would you handle the classification problem posed by ambiguous phrases in human language?=
Answer:
Handling ambiguous phrases involves several strategies:

• Contextual Analysis: Using NLP techniques to analyze the context in which words are used to determine their correct meaning. For example, understanding that “run” in “run a business” differs from “run” in “run a marathon.”
• Training Data: Providing the AI with a large dataset containing examples of ambiguous phrases and their correct classifications based on context.
• Confidence Levels: Implementing a system where the AI provides a confidence value for its classification, allowing human oversight for low-confidence interpretations.
• Feedback Loop: Continuously improving the AI’s understanding through user feedback and real-time learning to handle new ambiguous phrases.

Scenario 4.: Designing a Chatbot for a Restaurant Chain

Question:
You are creating a chatbot for a restaurant chain to handle customer inquiries. What are the key components and steps involved in developing this chatbot?
Answer:
Key components and steps include:

• Identifying Intents: Determining the various reasons customers might contact the restaurant, such as inquiring about operating hours, menu items, reservations, and special events.
• Defining Entities: Listing all the entities related to the intents, such as specific restaurant locations, menu items, and time slots.
• Creating Dialog Flow: Designing a dialog flowchart to map out how the chatbot will respond to different customer inputs and intents. Each possible question or request should have a corresponding response.
• Training the Chatbot: Using NLP and machine learning algorithms to train the chatbot on understanding and responding accurately to various customer inputs.
• Testing, and Refining: Testing the chatbot with real users to identify any gaps or issues in its responses, then refining its logic and responses to improve accuracy and user satisfaction.

Scenario 5: Utilizing NLP for Sentiment Analysis in Social Media

Question:
Your company wants to monitor brand reputation on social media. How can NLP be used for sentiment analysis to achieve this goal?
Answer:
Using NLP for sentiment analysis involves:

• Data Collection: Gathering social media posts, comments, and reviews related to the
  brand.
• Text Processing: Applying lexical analysis to clean and preprocess the text data, including tokenization, stemming, and lemmatization.
• Sentiment Classification: Training an NLP model to classify the sentiment of each post as positive, negative, or neutral based on predefined criteria.
• Insight Extraction: Analyzing the classified sentiments to identify trends, common issues, and areas of customer satisfaction or dissatisfaction.
• Reporting: Generating reports and visualizations to present the sentiment analysis findings to stakeholders, enabling data-driven decisions to enhance brand reputation.

Leveraging Linguistics and Computer Science Class 11 Notes

Understanding Human Language Complexity:

Linguistics:

• The strategic application of linguistic principles and practices to meet specific objectives or goals.
• Utilized in marketing, advertising, communications, education, natural language processing, etc.

Natural Language Processing (NLP):

• A branch of Artificial Intelligence (AI) that enables computers to understand, create, and manipulate human language.
• Involves querying data with natural-language text or voice.
• Examples: Virtual assistants (Siri, Alexa), web search, email spam filtering, autotranslate, document summarization, sentiment analysis, grammar/spell checking.

Entities and Relationships:

• Entity: A noun representing a person, place, or thing (e.g., “elephant,” “pajamas”).
• Relationship: A group of two or more entities that have a strong connection (e.g., “I + elephant,” “elephant + pajamas”).

Introduction to Natural Language Processing
Sentence Segmentation:

• Breaking text into sentences to manage unstructured information.

Tokenization:

• ‘Splitting text into smaller parts, called tokens, for easier classification and understanding.

Concepts:

• Implied meanings or ideas not explicitly stated in the text.

Emotion Detection and Sentiment Analysis

Emotion Detection:

• Identifies distinct human emotion types (e.g., anger, happiness).

Sentiment Analysis:

• Measures the strength of an emotion, determining if data is positive, negative, or neutral.
• Chatbots

Chatbots:

• Software applications designed to simulate conversation with human users using AI, NLP, and machine learning.
• Serve purposes like customer support, information retrieval, task automation, and entertainment.

Types of Chatbots:

• Rule-based Chatbots: Operate on predefined rules and decision trees.
• AI-powered Chatbots: Utilize NLP and machine learning for more advanced interactions.

Components of a Chatbot:

• Frontend: Messaging channel for user interaction.
• Backend: Operates application logic and memory to manage dialog continuity.

NLP Processing Steps

Lexical Analysis:

• Examines the structure of words and reduces them to their base forms using techniques like stemming and lemmatization.

Syntactical Analysis:

• Checks grammar, word layouts, and word relationships.

Semantic Analysis:

• Understands the various meanings conveyed by a sentence in context.

Discourse Integration:

• Interprets references and context from preceding sentences or words.

Pragmatic Analysis:

• Studies meanings in particular language contexts and extracts insights from text.

Applications of NLP

Sentiment Analysis:

• Evaluates consumer comments, social media posts, and product reviews to determine sentiment.

Voice Assistants:

• Use voice recognition, natural language interpretation, and NLP to understand and respond to human commands.

Email Filtering:

• Classifies incoming emails as important or spam.

Document Analysis:

• Organizes, stores, and searches large volumes of data.

Automatic Summarization:

• Summarizes information efficiently and accurately, identifying emotional meanings within data.

The post Leveraging Linguistics and Computer Science Class 11 Questions and Answers appeared first on Learn CBSE.

These Class 11 AI Important Questions Chapter 8 AI Ethics and Values Class 11 Important Questions and Answers NCERT Solutions Pdf help in building a strong foundation in artificial intelligence.

AI Ethics and Values Class 11 Important Questions

Class 11 AI Ethics and Values Important Questions

Important Questions of AI Ethics and Values Class 11 – Class 11 AI Ethics and Values Important Questions

AI Ethics and Values Class 11 Very Short Answer Type Questions

Question 1.
What is algorithmic bias, and can you give an example?
Answer:
Algorithmic bias occurs when AI systems display prejudice in their output, such as facial recognition software misidentifying individuals from certain ethnic groups more frequently than others.

Question 2.
How can bias in AI systems be reduced?
Answer:
Implementing regular audits of AI algorithms and training data can identify and correct biases, ensuring the AI’s decisions are fair and equitable across all user groups.

Question 3.
Define AI ethics.
Answer:
AI ethics involves the principles and values guiding the development, deployment, and use of artificial intelligence technologies to ensure they benefit society while minimizing harm and respecting human rights.

Question 4.
Why is transparency important in AI systems?
Ans.
Transparency in AI systems is crucial for users to understand and trust AI decisionmaking processes, ensuring accountability and enabling scrutiny for potential biases or errors.

Question 5.
How can AI impact privacy and data protection?
Answer:
AI technologies can infringe on privacy by collecting, analyzing, and sharing personal data without adequate consent, highlighting the need for robust data protection measures.

Question 6.
What strategies can mitigate bias in AI ?
Answer:
Mitigating bias in AI involves diverse data collection, algorithmic transparency, fairness audits, and continuous monitoring to identify and correct biases.

Question 7.
Why is bias awareness important in AI ?
Answer:
Bias awareness in AI is crucial because it helps identify and mitigate unfair preferences or discriminatory outcomes based on factors like race, gender, or socioeconomic status, ensuring more equitable and just AI systems.

Question 8.
What are the pillars of AI ethics?
Answer:
The pillars of AI ethics include explainability, fairness, robustness, transparency, and privacy. These principles guide the responsible development and use of AI technologies to uphold ethical standards and societal values.

AI Ethics and Values Class 11 Short Answer Type Questions

Question 1.
Why we are finding pearson correlation coefficient?
Answer:
The pearson correlation coefficient is used to measure the strength of a linear relation between two variables. If r=1 means a perfect positive correlation and if r=-1 a perfect negative correlation. Basically it attempts to draw a line of best fit through the data of two variables.

Question 2.
What is crosstab?
Answer:
A crosstab helps us to establish a relationship between two variables and the relationship is presented in a tabular form.

Question 3.
What happens if a correlation is negative?
Answer:
Correlation is a measure of the strength of a linear relationship between two quantitative variables (e.g. price, sales). When correlation is negative when one value decreases as the other values increases.

Question 4.
What role does fairness play in AI systems?
Answer:
Fairness in AI systems aims to ensure equitable treatment and outcomes for all individuals, preventing discrimination based on race, gender, or other characteristics.

Question 5.
How can ethical dilemmas in AI be addressed?
Answer:
Ethical dilemmas in AI can be addressed through the application of ethical frameworks, stakeholder engagement, and the development of policies that prioritize human welfare.

Question 6.
What challenges do policymakers face in regulating AI?
Answer:
Policymakers face challenges in regulating AI, including keeping up with rapid technological adyancements, balancing innovation with ethical considerations, and addressing global disparities in AI governance.

AI Ethics and Values Class 11 Long Answer Type Questions

Question 1.
Explain the following words and provide instances from everyday life:
a. Positive Correlation
b. Causation
c. Negative Correlation
d. Outlier
Answer:
a. Positive Correlation- When two variables have a linear relationship with one another, this is known as positive correlation. The second variable increases or decreases in tandem with the changes in the first. For instance, the cost of airline tickets rises in tandem with the price of fuel.

b. Causation- A causal relationship between two occurrences exists when one occurs as a direct result of the other. This is known as causation. Another name for this is cause and effect. For instance, an accident is caused by a speeding vehicle. Causation is what led to the accident.

c. Negative Correlation- A negative correlation occurs when two variables have a relationship in which one variable grows while the other drops and vice versa. For instance, increasing exercise causes one’s body weight to drop.

d. Outlier- Data points that considerably deviate from other observations are known as outliers in statistics. Outliers can be the result of measurement irregularities or experimental errors, the latter of which are occasionally removed from the data set. In statistical analysis, outliers can lead to significant issues.

Question 2.
In the example below of 6 people with different age and different weight, let us try calculating the value of the Pearson r.

Answer:
For the calculation of the Pearson correlation coefficient, we will first calculate the following values:

Here the total number of people is n=6
The formula for pearson coefficient is

Putting all the values in the above formula

Case-Based Questions

Question 1.
Mohan doesn’t understand why it is necessary to know different Assumptions to analyze Pearsons co-relation co-efficient. Explain the assumptions to help him in understanding.
Answer:
Assumption #1: The two variables should be measured at the continuous level.
Assumption #2: It is necessary that there should be a linear relationship between the two variables. To check whether a Pearson’s correlation exists or not, a scatterplot using Stata must be created.
Assumption #3: There should not be any outliers. As Outliers are simple single data points inside the data that do not follow the usual pattern.
Assumption #4: Your variables should be normally distributed.

Question 2.
If the regression equation for variables x and y are 7 x-3 y-18=0 and 4 x-y-11=0.
a)What is the A M for x and y ?
b)Find the correlation coefficient between x and y.
Answer:
(i) The intersection of two lines has the same intersection point and that is [x^–, y^–]. Therefore, we replace, x
and y with x^–and y^–
7x – 3y = 18
4x – y = 11

Hence, on solving these two equations we get x^–=3 and y^–=1.

(ii) We know,
r2 = 7 / 12
Therefore,
r = 712–√ (r is positive as both the coefficients are positive)
= 0.7638

Question 3.
“Correlation is ñot causation”. Explain this statement with the help of an example.
Answer:
The phrase “correlation is not causation” means that just because two variables appear to be related to each other, it doesn’t mean that one is the cause of another.
For example:
In a city, it is observed that as the sales of ice cream increase, the rate of drownings also increases. This is a correlation. But does this mean buying of more ice cream causes drownings? No. This is where the phrase “correlation is not causation” comes into play.
In this case, the hidden factor is the weather.
both are caused by a third factor: hot weather.
So, it’s important to remember that correlation (the relationship between two variables) does not imply causation (one variable causing a change in another). It’s possible that there may be an unseen factor influencing both variables, or that the correlation is purely coincidental.

Question 4.
A technology company has been contracted to develop a sophisticated facial recognition system intended for law enforcement applications. This system is designed to assist in identifying suspects by matching their facial features against a database. Initially celebrated for its high level of accuracy and efficiency in processing and identifying faces, the system, however, has come under scrutiny after reports emerged that it disproportionately misidentifies people of color. Investigations revealed that the misidentification rate for individuals of color was significantly higher than for white individuals, leading to a higher incidence of wrongful detentions and arrests among these communities. Further analysis indicated that the training data used to develop the algorithm lacked sufficient diversity, predominantly featuring faces of white individuals. This oversight has not only raised questions about racial bias inherent in AI technologies but also about the ethical implications of deploying such systems in sensitive areas like law enforcement without thorough vetting for fairness and impartiality.

Question 1.
What ethical problems are evident in this scenario?
Answer:
The scenario highlights ethical concerns including racial bias in technological applications, the risk of wrongful arrests due to biased AI, and the potential violation of rights for people of color.

Question 2.
What is one ethical approach to mitigate the bias found in the facial recognition system without compromising its accuracy?
Answer:
An ethical solution involves enhancing the diversity of the dataset used to train the facial recognition system, ensuring it accurately represents all racial groups. This would reduce bias while preserving the system’s overall accuracy.

Question 3.
How does the lack of diversity in training data contribute to the problem of algorithmic bias in this scenario?
Answer:
The lack of diversity in the training data has directly contributed to the algorithmic bias by failing to accurately represent the variety of human facial features across different races, leading to the system’s inability to correctly identify individuals of color.

Question 4.
What measures can be taken to ensure that the deployment of AI systems in law enforcement is both ethical and effective?
Answer:
To ensure the ethical and effective deployment of AI systems in law enforcement, comprehensive measures such as implementing strict regulatory standards, conducting regular bias audits, and involving diverse groups in the system’s development and evaluation process are crucial.

Question 5.
Discuss the potential long-term impacts on public trust and societal fairness if biases in AI systems like facial recognition are not addressed.
Answer:
If biases in AI systems remain unaddressed, it can lead to a significant erosion of public trust, particularly among marginalized communities disproportionately affected by such biases. Over time, this may exacerbate social inequalities and injustice, undermining the legitimacy of law enforcement agencies and the broader justice system.

Question 6.
Imagine you are a data scientist working on training an AI model for facial recognition. During the development phase, you discover that the AI system shows a significant bias against individuals with darker skin tones, leading to higher error rates in identifying them accurately. As an ethical data scientist, how would you approach this issue to ensure fairness and minimize discrimination in the facial recognition AI model?”
Answer:
Train the AI model with a diverse dataset including samples from various skin tones to address bias.

Question 7.
You’re developing an AI for self-driving cars. In an emergency, the car must decide between hitting pedestrians or swerving and risking passenger safety. What factors would you consider when programming this ethical dilemma?
Answer:
When programming an AI system for autonomous vehicles to make split-second ethical decisions, several factors need consideration to prioritize between the safety of passengers and pedestrians: Safety Metrics and Risk Assessment, Ethical Principles and Guidelines, Contextual Awareness, Legal and Regulatory Compliance.

Question 8.
A company develops an AI algorithm for automated hiring processes. Concerns are raised about the lack of transparency in how the algorithm makes hiring decisions, leading to potential biases and discrimination. As an AI ethics expert, how would you advocate for greater transparency in AI algorithms used for hiring? What measures would you recommend to ensure fairness and accountability in automated hiring systems?
Answer:
Here’s how we can push for fairer AI hiring:

• Show, don’t tell: Let candidates see the factors the AI considers during selection. Explain why they were chosen or not (when possible).
• Measure for fairness: Track how the AI treats different groups of applicants. Regularly check for hidden biases.
• Many minds are better: Include HR professionals, ethics experts, and even job seekers in developing and testing the AI. This ensures it’s fair and accountable.

Question 9.
As a product manager for a gaming company, you are tasked with designing an AI powered chatbot to engage users with interactive stories and personalized gaming experiences. How would you approach the design process to ensure the chatbot provides engaging and personalized experiences? Additionally, what strategies would you implement to address potential biases in the chatbot’s decision-making algorithms, particularly concerning fairness and inclusivity?” .
Answer:
Designing an AI-powered chatbot for a gaming company would involve creating interactive stories, personalized gaming experiences, and engaging user interactions. This could include features such as character customization, in-game assistance and tips, progress tracking, and social integration for multiplayer experiences. To address potential biases in decisionmaking algorithms, the chatbot’s training data would be curated carefully to avoid stereotypes or discriminatory patterns. Regular testing and user feedback would also be essential to refine the chatbot’s performance and ensure an immersive gaming experience for users.

Question 10.
Scenario: You are presented with a scenario on the Moral Machine where an autonomous vehicle must choose between swerving to avoid hitting a group of elderly pedestrians or staying the course and potentially harming younger pedestriAnswer:

Question:
How would you approach making such a decision in the context of programming AI systems for autonomous vehicles? What ethical principles would you prioritize?
Answer:
Consider factors like age, vulnerability, and potential impact of injuries to make a balanced decision.

Scenario Based Question Answer:

Scenario: Imagine a large healthcare provider in a diverse urban area that uses AI-powered algorithms to allocate medical resources and predict patient outcomes. Recently, it was discovered that the AI system consistently assigned lower risk scores to patients from minority communities compared to others, leading to disparities in healthcare access.

Question:
What ethical considerations are highlighted by this scenario, and how can bias in AI algorithms impact healthcare outcomes?
Answer:
This scenario underscores significant ethical concerns regarding fairness and equity in healthcare AI. The bias in the algorithm, which favored certain demographic groups over others, resulted in disadvantaged communities receiving suboptimal medical care. Such biases can perpetuate existing inequalities and lead to unequal health outcomes, ultimately undermining the goal of providing equitable healthcare services to all individuals regardless of their background. Addressing bias in AI algorithms is crucial to ensuring fair and just healtheare practices that prioritize patient well-being and uphold ethical standards.

AI Ethics and Values Class 11 Notes

Artificial Intelligence (AD)

• Definition: AI refers to the simulation of human intelligence in machines designed to think and act like humans. These systems can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.
• Significance: AI has the potential to revolutionize various aspects of human society by automating complex processes, improving efficiency, and enabling new capabilities.

Ethics

• Definition: Ethics refers to the moral principles that govern human behavior and decision-making. It encompasses concepts such as right and wrong, fairness, justice, and accountability.
• Significance: Ethical considerations guide individuals and organizations in making responsible choices that align with societal values and norms.

AI Ethics

Definition: AI ethics refers to the ethical principles and guidelines that govern the design, deyelopment, and deployment of AI technologies. It aims to ensure that AI systems are developed and used in ways that are fair, transparent, accountable, and aligned with human values.

Significance: AI ethics are crucial for ensuring that AI technologies are developed and deployed in a manner that upholds ethical principles and respects human rights, addressing issues such as bias, transparency, accountability, privacy, and societal impact.

The Five Pillars of AI Ethics

• Explainability: Transparency and interpretability of AI systems, allowing users to understand how algorithms make decisions and predictions.
• Fairness: Removing bias and discrimination from algorithms and decision-making .models, ensuring fair treatment for all individuals.
• Robustness: Ensuring AI systems consistently provide accurate and reliable results regardless of conditions.
• Transparency: Openness and disclosure about the design, operation, and implications of AI systems.
• Privacy: The right of individuals to control their personal information and to be free from unwanted intrusion.

Bias in AI

• Definition: Bias in AI refers to the tendency of AI systems to make unfair or inaccurate decisions due to flawed data or built-in assumptions, leading to unfair outcomes for certain groups of people.

Types of Bias:

• Training Data Bias: Bias resulting from over- or under-representation of certain groups in the training data.
• Algorithmic Bias: Bias stemming from programming errors or unfair weighting factors in decision-making processes.
• Cognitive Bias: Bias introduced by the designers’ own experiences and preferences during the development of AI systems.
• Significance: Addressing bias in AI is essential for ensuring fair and equitable outcomes, maintaining public trust in AI technologies, and upholding ethical principles.

Examples of AI Bias

• Healthcare: AI algorithms can return lower accuracy results for underrepresented groups.
• Online Advertising: Algorithms can reinforce job role gender bias.
• Image Generation: AI art generation applications can reflect gender biases.
• Facial Recognition Technology: Bias against certain demographic groups, leading to misidentification and wrongful arrests.
• Predictive Policing Algorithms: Over-policing of minority neighborhoods.
• Algorithmic Hiring Systems: Perpetuation of gender and racial biases in hiring processes.
• Credit Scoring Systems: Disadvantage certain demographic groups in assessing creditworthiness.

Mitigating Bias in AI Systems

Strategies:

• Using Diverse Data: Incorporate diverse datasets to minimize bias.
• Detecting Bias: Employ methods to find and measure bias in AI systems before deployment.
• Fair Algorithms: Develop algorithms designed to ensure fairness.
• Being Transparent: Provide clear explanations of how AI systems make decisions.
• Inclusive Teams: Involve diverse teams in AI development to spot and address biases.

AI Policies

• Importance: Ensuring responsible, safe, and ethical use of AI technologies while promoting innovation and public trust.
• Components: Ethical Principles: Developing ethical guidelines and standards.
• Stakeholder Engagement: Involving various stakeholders in the policymaking process.
• Risk Assessment: Identifying and mitigating potential risks before AI deployment.

Ethical Dilemmas in AI

• Definition: Situations where there is no clear “right” or “wrong” decision, often involving conflicting moral principles.
• Examples: Decision-making scenarios in autonomous vehicles, such as prioritizing passenger safety over pedestrian safety.

Moral Machine Game

• Purpose: An online platform exploring ethical dilemmas in AI through interactive decision-making scenarios.
• Significance: Promotes dialogue and ethical awareness regarding the implications of AI technologies.

Survival of the Best Fit Game

• Purpose: An educational game about hiring bias in AI, explaining how misuse of AI can perpetuate human biases.
• Significance: Helps students understand the concept of bias in hiring processes and the importance of mitigating bias in AI systems.

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