CBSE Class 12 AI Question Paper 2023 with Solutions

Students must start practicing the questions from Class 12 AI Important Questions and CBSE Class 12 AI Question Paper 2023 are designed as per the revised syllabus.

CBSE Class 12 AI Exam Question Paper 2023 with Solutions

Question 1.
Answer any 4 out of the given 6 questions on Employability Skills (4 × 1 = 4)

I. Narcissistic personality disorder is characterized by which of the following conditions(s):
i. People have an inflated sense of their own importance.
ii. A deep need for excessive attention
iii. Admiration and lack of empathy
iv. Introvert
(a) Only I
(b)Both i and ii
(C) i,ii, and iii
(d) i,ii,iii and iv
Answer:
(d) i,ii,iii and iv

II. “S” in acronym SMART in goal setting stands for:
(a) Strong
(b) Segment
(c) Specific
(d) Special
Answer:
(c) Specific

III. _____ is an economic process, where an idea is generated or an opportunity is created, refined, developed and implemented, while being exposed to uncertainity, to realize a profit by effective utilization of resources.
(a) Entrepreneurs
(b) Entrepreneurship development
(c) Entrepreneurship
(d) Cluster intervention
Answer:
(c) Entrepreneurship

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IV. In a spreadsheet software, an arrangement of cells in a vertical manner is known as:
(a) Worksheets
(b) Workbooks
(C) Rows
(d) Columns
Answer:
(d) Columns

V. Which of the following is not an example of a spreadsheet?
(a) Microsoft excel
(b) LibreOffice calc
(c) OpenOffice impress
(d) Google sheets
Answer:
(c) OpenOffice impress

VI. Which entrepreneur, out of the following, is essentially a manufacturer, who identifies the needs of customers and creates products or services to serve them?
(a) Service entrepreneur
(b) Industrial entrepreneur
(c) Agricultural entrepreneur
(d) Technical entrepreneur
Answer:
(b) Industrial entrepreneur

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Question 2.
Answer any 5 out of the given 6 questions (5 × 1 = 5)

I. A training set is a set of _______ -data in which the outcomes are already known.
(a) Current
(b) Instant
(c) Historical
(d) Output
Answer:
(c) Historical

II. _____ involves a combination of three key elements, that help to explain the audience what’s happening in the data in an engaging and entertaining manner.
(a) Data analysis
(b) Data visualization
(c) Data storytelling
(d) Data narrative
Answer:
(c) Data storytelling

III. Match the following step of AI project given in column ‘A’ with its exact sequence given in column ‘B’

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IV. In AI development which of the following framework is used?
(a) Python
(b) Tensor flow
(c) Visual Basic
(d) C++
Answer:
(b) Tensor flow

V. All the algorithms in machine learning rely on minimizing or maximizing a function, called as_______.
(a) Objective function
(b) Focal loss
(c) Loss function
(d) Gradient descent
Answer:
(a) Objective function

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VI. Which of the following is incorrect?
(a) The testing phase is essent
(b) The first fundamental step
(C) In scoping phase, it’s crucial to precisely define the strategic business objectives and desired outcomes of the project.
(d) Test data should include all relevant subsets of training data.
Answer:
(d) Test data should include all relevant subsets of training data.

Question 3.
Answer any 5 out of the given 6 questions. (5 × 1 = 5)

I. When visuals are applied to data, they can _______ the audience to the insights that they wouldn’t perceive without the charts or graphs.
(a) Engage
(b) Explain
(c) Enlighten
(d) Change
Answer:
(a) Engage

II. It is believed that if there is a _______ in the data, then AI development techniques may be employed.
(a) Pattern
(b) Program
(c) Language
(d) Logic
Answer:
(a) Pattern

III. Assertion (A) : It’s crucial to precisely define the strategic business objectives and desired outcomes of the project, align all the different stakeholders’ expectations, anticipate the key resources and steps, and define the success metrics.
Reason (R) : Selecting the AI or machine learning use, cases and being able to evaluate the return or investment (ROI) is critical to the success of any data project.
(a) Both (A) and (R) are true and (R) is the correct explanation of (A)
(b) Both (A) and (R) are true and ® is not the correct explanation of (A)
(C) (A) is true, but (B) is false.
(d) (A) is false, but (R) is true.
Answer:
(a) Both (A) and (R) are true and (R) is the correct explanation of (A)

IV. Which of the following shows the audience where to look and what not to miss and also keeps the audience engaged?
(a) data
(b) narrative
(c)charts
(d) story
Answer:
(d) story

V. Once the relevant projects have been selected and properly scoped, which of the following phase will be the next step of the machine learning lifecycle?
(a) Problem scoping
(b) Deployment
(c) Design
(d) Testing
Answer:
(b) Deployment

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VI. Which of the following is the first stage of an AI model life cycle?
(a) Build
(b) Scoping
(c) Design
(d) Testing
Answer:
(b) Scoping

Question 4.
Answer any 5 out of the given 6 questions (5 × 1 = 5)

I. Which of the following is the most preferred language for building an AI model?
(a) Python
(b) VB
(c) Java
(d) C++
Answer:
(a) Python

II. First four steps of writing Python code to find out RMSE values of the model are given here. Arrange them in proper order-
1. Splitting the data into training and test.
2. Reading the data.
3. Fitting simple linear regression to the training set.
4. Important required libraries
(a) 2-4-1-3
(b) 4-1-2-1
(c) 1-2-3-4
(d) 4-2-1-3
Answer:
(c) 1-2-3-4

III. Which of the following is NOT true for testing?
(a) The volume of test data should be very small
(b) Data validation is important
(C) Your testing team should test the AI and ML algorithms keeping model validation
(d) Your team must create test suites that help you validate your ML models.
Answer:
(a) The volume of test data should be very small

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IV. Data storytelling is a _______ approach for communicating insights drawn from data.
(a) Iterative
(b) Procedural
(c) Sequential
(d) Structural
Answer:
(c) Sequential

V. Which of the following is NOT true for train-test split evaluation?
(a) The procedure involves taking a dataset and dividing it into two subsets.
(b) The train-test procedure is appropriate when there is a larger dataset.
(c) The objective is to estimate the performance of the user.
(d) It can be used for classification or regression problems.
Answer:
(c) The objective is to estimate the performance of the user.

Question 5.
Answer any 5 out of the given 6 questions.

I. Stories create ______ experiences that transport the audience to another space and time.
(a) Visualizations
(b) Engaging
(c) Testing
(d) Necessary
Answer:
(b) Engaging

II. Which of the following is incorrect?
i. A capstone project is a project where students must research a topic independently to find a deep understanding of the subject matter.
ii. The initial part of the project of academic program is known as capstone project.
iii. The final project of an academic program, integrating all of the learning from the program is called capstone project.
(a) i and iii only
(b) ii and iii only
(c) ii only
(d) i, ii and iii
Answer:
(b) ii and iii only

III. Which of the following is an example of a development tool used in building an AI model?
(a) C+++
(b) Anaconda
(c) Linux
(d) Scratch
Answer:
(b) Anaconda

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IV. Identify the correct statements from the following:
i. Data modeling focuses on developing modes that are either descriptive or predictive.
ii. A predictive model tries to yield yes/no, or stop/go type outcomes.
iii. The data scientist will use a training set for descriptive modeling.
iv. Data modeling focuses on testing the project.
(a) i, ii, and iii
(b) ii, iii and iv
(c) i and ii
(d) i and iv
Answer:
(a) i, ii, and iii

V. _______ is a design methodology that provides a solution-based approach to solving problems.
(a) Classification
(b) Design thinking
(c) Recommendation
(d) Computational
Answer:
(b) Design thinking

VI. Which of the following is a key element of a data story?
(a) Audience
(b) Visuals
(c) Engage
(d) Information
Answer:
(a) Audience

Section-B

Subjective Type Questions:

Answer any 3 out of the given 5 questions on Employability Skills. Answer each question in 2030 words:

Question 6.
With reference to ‘Five Factor Model’, mention any four parameters that describe an individual’s personality.
Answer:
The Five Factor Model, also known as the Big Five personality traits, includes the following parameters:

1. Openness to Experience: This reflects the degree of intellectual curiosity, creativity, and preference for novelty and variety.
2. Conscientiousness: This measures the degree of organization, responsibility, dependability, and goal-oriented behavior.
3. Extraversion: This indicates the level of sociability, assertiveness, talkativeness, and the tendency to seek out social stimulation.
4. Agreeableness: This assesses the individual’s degree of kindness, cooperativeness, and the tendency to be compassionate and cooperative rather than suspicious and antagonistic.
5. Neuroticism: This measures emotional stability and the tendency to experience negative emotions, such as anxiety, anger, and depression.

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Question 7.
Define any two of the following personality disorders: (2)
(a) Avoidant
(b) Dependent
(C) Histrionic
Answer:
(a) Avoidant Personality Disorder:

• Individuals with Avoidant Personality Disorder (APD) tend to have a pervasive pattern of social inhibition, feelings of inadequacy, and hypersensitivity to negative evaluation.
• They are often reluctant to engage in social activities and fear criticism or rejection.
• People with APD may avoid new activities or meeting new people due to a fear of embarrassment or being socially inept.

(b) Dependent Personality Disorder:

• Dependent Personality Disorder (DPD) is characterized by a pervasive and excessive need to be taken care of.
• Individuals with this disorder often have difficulty making everyday decisions without reassurance and advice from others.
• They may feel helpless when alone and are preoccupied with fears of being abandoned.
• This dependency on others can lead to submissive and clingy behavior in relationships.

(c) Histrionic Personality Disorder:

• Histrionic Personality Disorder (HPD) is characterized by a pattern of excessive attentionseeking and emotionality.
• Individuals with HPD may be uncomfortable when they are not the center of attention and may use their physical appearance to draw attention to themselves.
• They often display rapidly shifting and shallow emotions and may be easily influenced by others.
• Relationships may be characterized by a need for constant approval and may be perceived as more intimate than they actually are.

Question 8.
Differentiate between a row and a cell of a spreadsheet. (2)
Answer:
In a spreadsheet:
Row:

• A row is a horizontal line of cells in a spreadsheet.
• It is identified by numbers along the left side of the spreadsheet, usually starting from 1 and increasing sequentially ( 1,2,3, … ).
• Each row in a spreadsheet is labeled with a row number to help identify its position.

Cell:

• A cell is a single unit or intersection point within a spreadsheet where a column and a row meet.
• It is identified by a combination of its column letter and row number. For example, cell A1 is in the first column and first row.
• Each cell can hold data, such as text, numbers, formulas, or functions.

In summary, a row is a horizontal arrangement of cells, and a cell is a single unit within the spreadsheet where data is entered or calculated. The combination of rows and columns creates a grid, and each cell in the grid has a unique address based on its column and row position.

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Question 9.
Write any four qualities that motivate an entrepreneur. (2)
Answer:
Motivation plays a crucial role in driving entrepreneurs to pursue their goals and overcome challenges. Here are four qualities that often motivate entrepreneurs:
Passion: Entrepreneurs are often motivated by a deep passion for their business idea or industry. Passion fuels their commitment, perseverance, and enthusiasm, helping them stay focused on their goals.

Autonomy: The desire for autonomy and independence is a strong motivator for entrepreneurs. They seek the freedom to make their own decisions, control their destiny, and build something of their own.

Challenge and Achievement: Entrepreneurs are motivated by challenges and the opportunity to overcome obstacles. The sense of accomplishment derived from successfully navigating difficulties and achieving milestones serves as a powerful motivator.

Innovation and Creativity: The opportunity to innovate and bring creative ideas to life is a motivating factor for many entrepreneurs. The prospect of developing new solutions, disrupting industries, and contributing to positive change can drive their entrepreneurial spirit.

It’s important to note that motivation can vary among entrepreneurs, and individual motivations may encompass a combination of these and other factors.

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Question 10.
Briefly define the term competency. Mention any two common Entrepreneurial Competencies.
Answer:
Competency: Competency refers to the combination of knowledge, skills, abilities, and behaviors that an individual possesses and demonstrates in order to perform effectively in a particular role or field. In the context of entrepreneurship, entrepreneurial competencies are the key qualities and capabilities that enable individuals to identify opportunities, create and manage ventures, and navigate the challenges of running a business.

Two Common, Entrepreneurial Competencies:
Opportunity Recognition:
Entrepreneurs need the competency to identify and evaluate business opportunities in the market. This involves the ability to spot gaps, unmet needs, or trends that can be turned into viable business ventures. Successful entrepreneurs possess a keen sense of observation, market understanding, and the ability to assess the feasibility of potential opportunities.

Risk Management:
Entrepreneurial ventures often involve uncertainty and risk. Competency in risk management is crucial for entrepreneurs to assess, mitigate, and take calculated risks. Entrepreneurs need to be able to make informed decisions in the face of uncertainty, manage challenges, and adapt to changing circumstances. This competency involves a combination of analytical skills, strategic thinking, and the ability to weigh potential rewards against potential risks.

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Answer any 4 out of the given 6 questions in 20-30 words each (4 × 2 = 8)

Question 11.
Mention the names of any four stages of Design Thinking.
Answer:
Design Thinking typically involves a series of stages that guide the problem-solving and innovation process. While the specific number of stages can vary, a commonly used framework consists of the following four stages:

Empathize: In this stage, designers seek to understand and empathize with the needs, experiences, and perspectives of the people they are designing for. This involves engaging with users, conducting interviews, and observing behaviors to gain deep insights into their needs and challenges.

Define: The Define stage involves synthesizing the information gathered during the Empathize stage to define the core problems and challenges. Designers work to articulate the problem statement clearly and frame it in a way that guides the rest of the design process.

Ideate: Ideation is the stage where designers generate a wide range of creative ideas and potential solutions to address the defined problems. It encourages brainstorming and free-thinking, allowing for the exploration of diverse possibilities without immediate judgment.

Prototype: Prototyping involves creating tangible representations or mock-ups of potential solutions. These prototypes can be low-fidelity or high-fidelity, depending on the stage of the design process. Prototyping allows designers to test and iterate on their ideas, getting feedback and refining the solutions.

These stages are often iterative and may be revisited as designers gain more insights and refine their understanding of the problem and potential solutions.

Question 12.
Explain the term time series decomposition. (2)
Answer:
Time series decomposition is a statistical technique used to break down a time series into its underlying components. The primary goal is to separate the observed data into different systematic and non-systematic components, making it easier to understand the patterns, trends, and seasonality present in the time series data. The decomposition typically involves three main components:
Trend: The trend component represents the long-term direction or movement in the time series. It identifies the underlying pattern that may indicate whether the values are increasing, decreasing, or staying relatively constant over time.

Seasonal: The seasonal component captures repeating patterns or fluctuations that occur at regular intervals within a specific time period, such as daily, monthly, or yearly. Seasonal patterns often reflect recurring events or cycles.

Residual (or Error): The residual, also known as the error or remainder, accounts for the variability in the data that cannot be attributed to the trend or seasonal components. It represents the random or irregular fluctuations in the time series that are not part of any discernible pattern.

The time series decomposition helps analysts and data scientists to better understand the underlying structure of the data, making it easier to analyze and model. Various methods, such as additive or multiplicative decomposition, can be employed based on the nature of the time series data. Decomposition is particularly useful in time series forecasting and trend analysis, allowing for more accurate predictions and insights into the factors influencing the data over time.

Question 13.
Give two points of difference between Cross-Validation and Train-Test split.
Answer:
Cross-Validation and Train-Test Split are techniques used to assess the performance of machine learning models, but they differ in their approach to splitting the dataset for training and evaluation. Here are two points of difference between Cross-Validation and Train-Test Split:
Data Splitting: Train-Test Split: In this method, the dataset is divided into two subsets: a training set and a test set. The model is trained on the training set, and its performance is evaluated on the separate test set. The split is typically done with a fixed ratio, such as 80 % for training and 20% for testing.

Cross-Validation: Cross-Validation involves dividing the dataset into multiple folds or subsets. The model is trained on some of the folds and tested on the remaining fold. This process is repeated multiple times, with each fold serving as the test set exactly once. The results are averaged to provide a more robust evaluation.

Usage of Data: Train-Test Split: The data is split into a training set and a test set, and the model is trained exclusively on the training set. The test set is reserved for assessing the model’s performance after training.

Cross-Validation: The entire dataset is used for both training and testing across multiple iterations. Each data point serves as part of the training set and part of the test set at different stages of the cross-validation process. This ensures that every data point is used for validation at least once.

In summary, Train-Test Split involves a fixed separation of the dataset into training and test sets, while Cross-Validation systematically uses different subsets of the data for training and testing in multiple iterations, providing a more comprehensive evaluation of the model’s performance.

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Question 14.
Selecting the right analytical approach depends on the questions being asked.
In the light of the above given statement, which type of questions can be asked for (2)
(A) Classification approach
(B) Descriptive approach
Answer:
(A) Classification Approach:

Questions for Classification Approach:

• Is the given observation a part of a specific category or class?
• Can we predict the class or category of a new observation based on its features?
• What factors contribute the most to the classification of observations into different classes?
• How well can the model distinguish between different classes?

Example Scenario:

• Predicting whether an email is spam or not based on its content.
• Classifying customers into high, medium, or low risk for a credit application.

Descriptive Approach:
Questions for Descriptive Approach:

• What are the key characteristics and trends in the dataset?
• How are variables distributed, and what is the central tendency?
• Can we identify patterns or anomalies in the data?
• What are the relationships between different variables?

Example Scenario:

• Analyzing the average sales per month over the past year.
• Describing the distribution of customer ages in a given dataset.

In summary, the choice between a classification approach and a descriptive approach depends on whether the primary goal is to predict categories or understand and describe patterns and trends in the data.

Question 15.
Name the main stages of AI project life cycle. (2)
Answer:
The AI project life cycle typically involves several stages, each crucial for the successful development and deployment of artificial intelligence solutions. The main stages of an AI project life cycle include:

• Problem Definition: Clearly define the problem or objective that the AI project aims to address. Identify the scope, goals, and constraints of the project.
• Data Collection: Gather relevant data that will be used for training and testing the AI model. Ensure data quality, completeness, and relevance to the problem at hand.
• Data Preparation: Clean, preprocess, and transform the collected data into a format suitable for training machine learning models. This may involve handling missing values, encoding categorical variables, and scaling features.
• Modeling: Select and apply appropriate machine learning algorithms to build predictive models’ This stage involves training the model on the prepared data and fine-tuning its parameters.
• Evaluation: Assess the performance of the trained model using evaluation metrics and validation techniques. This helps ensure that the model generalizes well to new, unseen data.
• Deployment: Integrate the trained model into the target environment, making it accessible for making predictions or decisions in real-world scenarios.
• Monitoring and Maintenance: Continuously monitor the performance of the deployed model in real-world conditions. Implement updates and maintenance as needed to address changing data patterns and ensure ongoing effectiveness.
• Feedback Loop: Establish a feedback loop to gather information from the model’s performance in the real world. Use this feedback to improve the model, update training data, and enhance overall system performance.
• Documentation: Document the entire AI project life cycle, including problem statements, data sources, modeling techniques, evaluation results, and deployment details. This documentation aids in knowledge transfer and future enhancements.

These stages are iterative, and the AI project life cycle often involves revisiting and refining certain steps as the project progresses and additional insights are gained.

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Question 16.
Give two reasons why storytelling is so powerful? (2)
Answer:
Storytelling is a powerful communication tool that has been used throughout history. Two key reasons why storytelling is so powerful are:
1. Emotional Connection:
Stories have the ability to evoke emotions and create a personal connection with the audience. When information is presented in a narrative form, it engages the audience on an emotional level. Emotional connections are more memorable and impactful, leading to a deeper understanding of the message. By incorporating relatable characters, challenges, and resolutions, storytelling can resonate with the audience’s own experiences, making the message more meaningful and memorable.

2. Enhanced Understanding and Retention:
Stories provide a context and structure that help people better understand complex information. Instead of presenting facts and data in isolation, storytelling weaves information into a narrative that is easier to follow and comprehend. The narrative structure of a story, with a clear beginning, middle, and end, helps organize information in a way that the human brain naturally processes and remembers.

As a result, storytelling enhances the retention of information, making it more likely that the audience will remember and internalize the key messages conveyed in the story.

In summary, the emotional connection created through storytelling and the enhanced understanding and retention of information contribute to the power of storytelling as a communication tool. Stories have the capacity to engage, inspire, and leave a lasting impact on the audience.

Answer any 3 out of the given 5 questions in 50-8o words each. (3 × 4 = 12)

Question 17.
Give steps to break down a problem into smaller units before coding.
Answer:
Breaking down a problem into smaller units, often referred to as decomposition, is a crucial step in the software development process. It helps in managing complexity, promoting modularity, and facilitating a more systematic approach to problem-solving. Here are steps to break down a problem before coding:

• Understand the Problem: Begin by thoroughly understanding the problem statement or requirements. Identify the key objectives, constraints, and expected outcomes.
• Identify Subtasks: Break down the main problem into smaller, manageable subtasks or components. Each subtask should represent a specific aspect of the problem that can be addressed independently.
• Define Inputs and Outputs: For each subtask, clearly define the inputs required and the expected outputs. Understand how data will flow between different components and what transformations or operations are needed.
• Prioritize and Order Subtasks: Prioritize the subtasks based on their importance and dependencies. Identify any prerequisites or sequential relationships between subtasks.
• Use Modular Design: Aim for a modular design by encapsulating related functionality into modules or functions. This promotes code reusability, maintainability, and a clearer organization of the codebase.
• Identify Data Structures and Algorithms: Determine the appropriate data structures and algorithms for each subtask. Consider the efficiency and scalability of the chosen solutions.
• Pseudocode or Floweharts: Before diving into actual code, consider creating pseudocode or flowcharts for each subtask. This provides a high-level overview of the logic and helps in refining the algorithm.
• Review and Refine: Review the decomposition and design with a focus on clarity, efficiency, and adherence to the problem requirements. Refine the decomposition as needed to improve the overall structure.
• Test Each Subtask Independently: Develop and test each subtask independently to ensure that it functions correctly. This allows for early detection and resolution of issues before integrating the components.
• Integrate and Test the Whole System: Once individual subtasks are tested and verified, integrate them into the complete system. Conduct comprehensive testing to ensure that the integrated solution meets the overall requirements.

By following these steps, developers can break down a complex problem into manageable units, facilitating a more systematic and organized approach to coding. This decomposition process promotes a clear understanding of the problem and improves the overall efficiency of the development process.

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Question 18.
The following is the diagram depicting the foundational methodology for data science.
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The diagram is marked with A, B, C, D. Identify these four steps and briefly explain the significance of steps marked as ‘A’ and ‘B’.
Answer:

• A-depicts-Business understanding
• B-depicts data collection
• C-depicts modeling
• D-depicts deployment

A. Business understanding means is a critical and foundational phase in the data science process. Its significance lies in several key aspects:

• Alignment with Business Goals:
• Problem Definition and Refinement:
• Identification of Stakeholder Needs:
• Return on Investment (ROI):

B. Data collection- is required for the following:

• Data collection helps in finding raw material for data analysis.
• High quality data helps support in decision making.
• Data collection enables the identification of patterns, trends, and correlations within the dataset

Question 19.
Explain AI project cycle and project scoping in detail.
Answer:
The AI project cycle outlines the various stages involved in developing and deploying an artificial intelligence solution. While specific approaches may vary, a general AI project cycle typically includes the following stages:

• Project Scoping: Define the project’s goals, objectives, and scope. Identify the problem to be addressed and the expected outcomes. Set clear boundaries and constraints for the project.
• Data Collection and Preparation: Gather relevant data required for training and testing the AI model. Clean, preprocess, and format the data to ensure its quality and suitability for machine learning algorithms.
• Modeling: Select appropriate machine learning algorithms based on the nature of the problem. Train the model using the prepared data and fine-tune its parameters for optimal performance.
• Evaluation: Assess the performance of the trained model using evaluation metrics and validation techniques. Evaluate how well the model generalizes to new, unseen data.
• Deplayment: Integrate the trained model into the target environment or application. Deploy the AI solution for real-world use, making predictions or decisions based on new data.
• Monitoring and Maintenance: Continuously monitor the performance of the deployed model in the real-world context. Implement updates and maintenance as needed to adapt to changing data patterns and ensure ongoing effectiveness.
• Feedback Loop: Establish a feedback loop to gather information from the model’s performance in the real world. Use this feedback to improve the model, update training data, and enhance overall system performance.

Project Scoping:

• Project scoping is a critical initial phase in the AI project cycle. It involves defining the boundaries and parameters of the project to ensure clarity and alignment with organizational objectives. Here are key steps in project scoping:
• Define Objectives: Clearly articulate the goals and objectives of the AI project. Understand what the organization aims to achieve through the implementation of AI.
• Identify Stakeholders: Identify and involve key stakeholders, including project sponsors, end-users, and other relevant parties. Understand their expectations and requirements.
• Set Boundaries and Constraints: Define the scope of the project by setting boundaries and constraints. Clearly specify what is included and excluded from the project to manage expectations.
• Understand Resources: Assess the resources available for the project, including budget, time, and personnel. Determine the feasibility of the project within the given constraints.
• Risk Assessment: Identify potential risks and challenges associated with the project. Conduct a risk assessment to understand and mitigate potential issues that may arise during the project.
• Feasibility Study: Evaluate the technical and economic feasibility of the AI project. Assess whether the proposed solution aligns with the organization’s capabilities and financial resources.
• Define Deliverables: Clearly define the deliverables and milestones expected at various stages of the project. This includes specific outcomes, reports, and any other tangible results.
• Create a Project Plan: Develop a detailed project plan that outlines tasks, timelines, dependencies, and responsibilities. This plan serves as a roadmap for project execution.
• Communication Plan: Establish a communication plan that defines how project progress will be communicated to stakeholders. Ensure transparent and regular communication channels.
• Approval and Sign-Off: Present the project scope, objectives, and plan to stakeholders for approval and sign-off. This formalizes commitment and ensures alignment before proceeding to subsequent stages.
• By effectively scoping an AI project, organizations can set the foundation for a successful implementation, mitigate risks, and align the project with overarching business objectives.

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Question 20.
(a) Mention the steps that can assist in finding compelling stories in the data sets.
Answer:
Some of the simple steps that assist in compelling data stories are :

• The story context must be a relevant one.
• It must include authentic data.
• Should be presented in compelling visuals.
• There should be a relationship between the data.

(b) Which of following (Option-A or Option-B) is a better data story? Give reasons.

Option-A

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Option-B

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In 2021, the total forest and tree cover in India is 80.9 million hectares, which is 24.62 % of the geographical area of the country.
Answer:
Option-B is a better data story figure because

• The textual context within the figure is more appropriate.
• The numbers represents the exactness of the data and depicts a clear picture of what the text conveys.
• It also presents geographical data with certain regions and areas.

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