AI Project Cycle Class 10 Questions and Answers

These Class 10 AI Important Questions Chapter 2 AI Project Cycle Class 10 Important Questions and Answers NCERT Solutions Pdf help in building a strong foundation in artificial intelligence.

AI Project Cycle Class 10 Important Questions

Class 10 AI Project Cycle Important Questions

Important Questions of AI Project Cycle Class 10 – Class 10 AI Project Cycle Important Questions

AI Project Cycle Class 10 Subjective Questions

Question 1.
Who are the stakeholders?
Answer:
Stakeholders are the individuals, groups, or organizations directly or indirectly affected by the problem or its solution. They can include customers, employees, suppliers, shareholders, regulators, and the community.

Question 2.
Write about ‘What’ block of 4 Ws problem.
Answer:
The “What” block of the 4 W problem canvas addresses the core issue or problem at hand. It requires a concise and clear articulation of what the problem is.

Question 3.
What is the context/situation in which the stakeholders experience the problem?
Answer:
The context/situation in which stakeholders experience the problem refers to the environment, circumstances, or setting where the problem occurs and affects those involved. It helps to understand the specific conditions and factors contributing to the problem’s manifestation.

Question 4.
Are you aware of visual representations of data? List them.
Answer:
Yes, I’m aware of visual representations of data. Some common ones include:

1. Bar charts
2. Line graphs
3. Pie charts
4. Scatter plots
5. Histograms
6. Heatmaps
7. Bubble charts
8. Box plots
9. Sankey diagrams
10. Tres: maps

Question 5.
Write the Al models’ classification”?
Answer:
AI models can be classified into two inlain categories:
1. Learning-Based Models These mocilels learn patterns and relationships from data through techniques such as machine learning and deep learning. Examples include neural networks, support ventor machines, and decision trees.

2. Rule-Based Models These models ope rate on predefined rules and logic programmed by human experts. They use if-then statements or logical rules to make decisions or perform tasks. Exper t systems and knowledge-based systems fall into this category.

Question 6.
Write the features of Neural Network?
Answer:
Refer from Text on Page no: 92 (Features of ‘Neural Network).

AI Project Cycle Class 10 Very Short Answer Type Questions

Question 1.
It helps us to summarise all the key points into one single Template so that in future, whenever there is a need to look back at the basis of the problem, we can take a look at this and understand the key elements of it.
Answer:
Problem Statement Template

Question 2.
It is a type of supervised learning model where the goal is to predict a continuous value output based on the input features.
Answer:
Regression

Question 3.
In this learning model, the data set which is fed to the machine is labelled. Name the model.
Answer:The learning model where the dataset fed to the machine is labeled is called “Supervised Learning”.

Question 4.
It refers to the unsupervised learning algorithm which can cluster the unknown data according to the patterns or trends identified out of it.
Answer:
Clustering

Question 5.
What precaution to be taken while acquiring data for developing an AI project?
Answer:
Data should be collected from an authentic source and should be accurate. The redundant and irrelevant data should not be part of prediction.

Question 6.
What is the formula to calculate F1 score?
Answer:
2 × \(\frac{\text { Precision } \times \text { Recall }}{\text { Precision }+ \text { Recall }}\)

Question 7.
What is the role of hidden layer in neural network?
Answer:
Hidden layers transform the input data into a format that helps the network capture complex patterns and relationships.

Question 8.
Priya went to her hometown. Her hometown does not have all the facilities that are available in the urban area. She basically is working on selecting a problem
which she will solve. In the project cycle, which step is required for selecting a problem to solve?
Answer:
Problem scoping

Question 9.
Mention any four sources of data sources.
Answer:
The sources of data are – Surveys & questionnaire, web scrapping, sensors and API.

Question 10.
Mention the types of learning based approaches for Al modeling.
Answer:
Supervised, unsupervised and Reinforced.

AI Project Cycle Class 10 Short Answer Type Questions

Question 1.
Sirisha and Divisha want to make a model which will organise the unlabeled input data into groups based on features. Which learning model should they use and why?
Answer:
Clustering model/Unsupervised learning is used to organise the unlabeled input data into groups based on features.
Clustering is an unsupervised learning algorithm which can cluster unknown data according to the patterns or trends identified out of it. The patterns observed might be the ones which are known to the developer or it might even come up with some unique patterns out of it.

Question 2.
Suhana works for a company wherein she was assigned the task of developing a project using AI project cycle. She knew that the first stage was scoping the problem. Help her list the remaining stages that she must go through to develop the project.
Answer:
List the remaining stages of AI project cycle that she must go through to develop the project

1. Data Acquisition
2. Data Exploration
3. Modelling
4. Evaluation

Question 3.
Differentiate between Classification and Regression.
Answer:

Question 4.
What is F1 Score in Evaluation?
Answer:
F1 score can be defined as the measure of balance between precision and recall.

F1 Score = 2 × \(\frac{\text { Precision } \times \text { Recall }}{\text { Precision }+ \text { Recall }}\)

Question 5.
What do you mean by Evaluation of an Al model ? Also explain the concept of overfitting with respect to Al model Evaluation.
Answer:
Evaluation of an AI model refers to the process of assessing its performance and effectiveness in solving a particular fask or problem.
One critical concept in the evaluation of AI models is overfitting. Overfitting occurs when a model learns the training data too well, capturing noise or random fluctuations in the data rather than the underlying patterns.

Question 6.
For a healthcare organisation’s objective of predicting disease outbreaks and efficiently allocating resources through the analysis of medical records, would you recommend using supervised learning or unsupervised learning as the preferred machine learning approach? Explain your choice briefly.
Answer:
I would recommend using supervised learning as the preferred machine learning approach.
Supervised learning is suitable for this task because it involves training a model on labeled data, where the input data (medical records) is associated with corresponding output labels (e.g., disease outbreak occurrences). This allows the model to learn the patterns and relationships between medical records and disease outbreaks, enabling it to make predictions on unseen data.

Question 7.
What is Dimensionality Reduction?
Answer:
Dimensionality reduction is the process of reducing the number of features (or dimensions) in a dataset while retaining as much information as possible. This can be done for a variety of reasons, such as to reduce the complexity of a model, to improve the performance of a learning algorithm, or to make it easier to visualize the data.

AI Project Cycle Class 10 Long Answer Type Questions

Question 1.
Akhil wants to learn how to scope the problem for an AI Project. Explain him the following:
(a) 4Ws Problem Canvas
(b) Problem Statement Template
Answer:
(a) The 4 Ws Problem canvas helps in identifying the key elements related to the problem. The 4 Ws are

• The “Who” block helps in analysing the people getting affected directly or indirectly due to the problem.
• The “What” block helps us to determine the nature of the problem.
• The “Where” block helps us to look into the situation in which the problem arises, the context of it, and the locations where it is prominent.
• The “Why” block suggests to us the benefits which the stakeholders would get from the solution and how it will benefit them as well as to the society.

(b) Problem Statement Template

Question 2.
Identify and explain the types of the learning-based approaches in the figures given below.

Answer:
The learning-based approaches shown in the given figures are Supervised learning and Unsupervised learning.

Figure 1 In a supervised learning model, the dataset which is fed to the machine is labelled. In other words, we can say that the dataset is known to the person who is training the machine only then he/she is able to label the data. A label is some information which can be used as a tag for data.

Here, labelled images of dog and cat are fed into the model and trained. The model correctly identifies the given input as dog.

Figure 2 An unsupervised learning model works on unlabelled dataset. This means that the data which is fed to the machine is random and there is a possibility that the person who is training the model does not have any information regarding it.

The unsupervised learning models are used to identify relationships, patterns and trends out of the data which is fed into it. It helps the user in understanding what the data is about and what are the major features identified by the machine in it. Here, images of a set of animals are fed into the AI model and the model clusters them based on similar features.

Question 3.
Neural networks are said to be modelled the way how neurons in the human brain behave. A similar system is mimicked by the Al machine to perform certain tasks. Explain how neural networks work in an Al model and mention any three features of Neural Networks.
Answer:
Neural networks are loosely modelled after how neurons in the human brain behave.
The features of a neurel network are :

1. They are able to extract data features automatically without needing the input of the programmer.
2. A neural network is essentially a system of organizing machine learning algorithms to perform certain tasks.
3. It is a fast and efficient way to solve problems for which the dataset is very large, such as in images.

Question 4.
What are Neural networks? Briefly explain all the layers of a neural network.
Answer:
Refer to text on page No. 91 and 92 (Neural Networks).

Question 5.
Consider the following graphs (Figure 1 and Figure 2) that demonstrate the two types of Supervised Learning Models of Artificial Intelligence. Identify and explain each model giving suitable examples of.

Answer:
Figure 1 refers to classification and figure 2 refers to regression.
Classification In classification, the goal is to predict which category or class the input data belongs to. The output variable is categorical. Examples include:

• Binary Classification The output variable has two classes. For instance, predicting whether an email is spam or not spam.
• Multi-class Classification The output variable has more than two classes. For example, classifying images of animals into categories such as dog, cat, or bird.

Regression In regression, the goal is to predict a continuous numerical value based on input features. The output variable is numerical. Examples include:

• Linear Regression Predicting house prices based on features such as size, number of bedrooms, and location.
• Polynomial Regression Predicting the temperature based on historical weather data.

Question 6.
What is the significance of Al project cycle? Also explain in detail about how Data Acquisition is different from data exploration.
Answer:
The AI (Artificial Intelligence) project cycle is a structured approach to developing and deploying AI solutions. It encompasses various stages from problem identification and data collection to model development, deployment, and monitoring. The significance of the AI project cycle lies in its ability to guide teams through a systematic process, ensuring that AI projects are well-planned, executed efficiently, and yield meaningful results.
Data Acquisition differs from Data Exploration as

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