Chapter Exam

Chapter-7 Computational Thinking & AI Project Lifecycle — Online MCQ Test

Q: What is the first step in the AI project lifecycle?

Answer: B. Problem scoping — Problem scoping is the initial phase where we define the problem, objectives, and constraints before proceeding with any other steps in the AI project lifecycle.

Q: Which term refers to the process of creating and configuring a machine learning model?

Answer: C. AI modelling — AI modelling is the step where we select, design, and configure machine learning algorithms and models suitable for solving the identified problem.

Q: What is training data in the context of AI?

Answer: B. Data used to teach and build the AI model — Training data is the dataset used to teach and build the AI model by adjusting its parameters and weights to minimize prediction errors.

Q: Which of the following is NOT a part of the AI project lifecycle?

Answer: B. Computational thinking — While computational thinking is a methodology used in problem-solving, it is not a specific phase/step in the AI project lifecycle. The lifecycle includes problem scoping, data acquisition, exploration, modelling, and evaluation.

Q: During which phase of the AI lifecycle would you identify if the model is overfitting or underfitting?

Answer: C. Evaluation — The evaluation phase involves testing the trained model on test data to assess its performance, identify overfitting, underfitting, and other issues.

INFORMATION TECHNOLOGY · Grade 8 · CBSE(NCERT)

Practice Chapter-7 Computational Thinking & AI Project Lifecycle with a free chapter-wise online MCQ test for CBSE(NCERT) Grade 8 INFORMATION TECHNOLOGY. This chapter covers: problem scoping - data acquisition - data exploration - AI modelling - evaluation - algorithm efficiency - training data - bias. AI-generated questions from basic to board-exam level, with instant results and explanations.

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Chapter-7 Computational Thinking & AI Project Lifecycle — Important Questions & Answers (FAQ)

Frequently asked questions from CBSE(NCERT) Grade 8 INFORMATION TECHNOLOGY — Chapter-7 Computational Thinking & AI Project Lifecycle, with answers and explanations. These are sample questions; the exam has its own separate question set.

What is the first step in the AI project lifecycle?

A. Data acquisition
B. Problem scoping ✓
C. AI modelling
D. Evaluation

Answer: B. Problem scoping
Problem scoping is the initial phase where we define the problem, objectives, and constraints before proceeding with any other steps in the AI project lifecycle.

Which of the following best defines 'data acquisition'?

A. Analyzing data patterns and relationships
B. Collecting and gathering data from various sources ✓
C. Training the AI model with existing data
D. Evaluating model performance metrics

Answer: B. Collecting and gathering data from various sources
Data acquisition is the process of collecting and gathering data from various sources like databases, sensors, APIs, and files for use in the AI project.

What is the purpose of data exploration in the AI project lifecycle?

A. To deploy the AI model to production
B. To understand the characteristics, patterns, and quality of the data ✓
C. To train the neural network
D. To identify new data sources

Answer: B. To understand the characteristics, patterns, and quality of the data
Data exploration involves analyzing and understanding the data's structure, distribution, patterns, missing values, and quality to prepare it for modeling.

Which term refers to the process of creating and configuring a machine learning model?

A. Algorithm efficiency
B. Data preprocessing
C. AI modelling ✓
D. Bias detection

Answer: C. AI modelling
AI modelling is the step where we select, design, and configure machine learning algorithms and models suitable for solving the identified problem.

What is training data in the context of AI?

A. Data used to test the final model
B. Data used to teach and build the AI model ✓
C. Data that contains errors and outliers
D. Data collected after model deployment

Answer: B. Data used to teach and build the AI model
Training data is the dataset used to teach and build the AI model by adjusting its parameters and weights to minimize prediction errors.

Which of the following is NOT a part of the AI project lifecycle?

A. Problem scoping
B. Computational thinking ✓
C. Data exploration
D. Evaluation

Answer: B. Computational thinking
While computational thinking is a methodology used in problem-solving, it is not a specific phase/step in the AI project lifecycle. The lifecycle includes problem scoping, data acquisition, exploration, modelling, and evaluation.

During which phase of the AI lifecycle would you identify if the model is overfitting or underfitting?

A. Problem scoping
B. Data acquisition
C. Evaluation ✓
D. Algorithm efficiency

Answer: C. Evaluation
The evaluation phase involves testing the trained model on test data to assess its performance, identify overfitting, underfitting, and other issues.

Why is problem scoping considered crucial in the AI project lifecycle?

A. It ensures the data is clean and formatted
B. It defines clear objectives and constraints, preventing wasted resources and misaligned solutions ✓
C. It automatically selects the best algorithm
D. It eliminates all bias from the dataset

Answer: B. It defines clear objectives and constraints, preventing wasted resources and misaligned solutions
Problem scoping establishes clear goals, constraints, and success metrics upfront, ensuring that the entire AI project is aligned with actual business or research needs.

Consider a facial recognition system trained primarily on faces from one ethnic group. What would be the likely outcome?

A. Perfect accuracy for all populations
B. High accuracy for the trained group but poor accuracy for other groups due to training data bias ✓
C. Equal performance across all populations
D. The model would refuse to work on different populations

Answer: B. High accuracy for the trained group but poor accuracy for other groups due to training data bias
Biased training data (homogeneous in ethnicity) causes the model to perform well only on the represented group and poorly on underrepresented or absent groups, a classic example of algorithmic bias.

An AI project for loan approval shows that the model is biased against applicants from certain neighborhoods. At what stages could this bias have originated, and how should it be addressed?

A. Only during modelling; fix it by changing the algorithm
B. Only from historical training data; impossible to fix once deployed
C. Could originate during data acquisition (biased historical data) or exploration; address by auditing data, rebalancing, or using fairness-aware algorithms ✓
D. Only during evaluation; reject the entire project

Answer: C. Could originate during data acquisition (biased historical data) or exploration; address by auditing data, rebalancing, or using fairness-aware algorithms
Bias can enter at multiple stages: biased data collection, imbalanced representation, or historical discrimination in data. It should be addressed through data audit, balancing techniques, and fairness constraints throughout the lifecycle.

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