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D. Very short answer type question.

1. What does the “Who” block in the 4 W’s canvas help identify?
2. How is evidence gathered in the “What” block of the 4 W’s canvas?
3. What is the goal of stakeholders in the Mess scenario?
4. What are the factors affecting the Mess scenario problem statement?

5. What are the key data features for the Mess scenario problem?
6. Which Python package is fundamental for scientific computing?

7. What are the two main structures in Pandas?
8. What types of graphs can be drawn using Matplotlib for data visualization?

E. Short answer type question.
1. What is the purpose of the “Who” block in the 4 W’s canvas?
2. Why is it important to gather evidence in the “What” block?

3. How does the “Where” block help in scoping the problem?
4. What benefits do stakeholders aim to achieve in the Mess scenario?
5. What are the essential data features for the Mess scenario problem?
6. Which Python package is crucial for data manipulation and analysis?

7. What are the main structures in Pandas for handling data?
8. Why is data visualization important in the AI project life cycle?

F. Long answer type question.
1. In what ways can AI project teams address the challenges of data privacy and security?

2. How does the ‘Prototyping’ approach facilitate rapid development and testing of AI solutions?
3. What role does ‘Experiential Learning’ play in enhancing AI team skills and capabilities?
4. Explain the concept of ‘Model Validation’ and its significance in ensuring model accuracy.

5. How can AI project managers effectively communicate technical details to non-technical stakeholders?
6. What ethical considerations should AI project teams keep in mind during the deployment phase of a
machine learning model?

G. Application based question.
1. If you were tasked with preparing a dataset for training an AI system to predict traffic patterns in a city,
what steps would you take during the data exploration phase? Explain in simple terms.
2. In a smart home scenario, how could the principles of user-centered design be applied to ensure that the
AI system understands and meets the needs of the residents? Provide practical examples.

H. Assertion and reason-based questions.
1. Assertion: Data exploration is a necessary step before training an AI system.
Reason: It helps in understanding and preparing the dataset.

a. Both Assertion and Reason are true, and Reason is the correct explanation of Assertion.
b. Both Assertion and Reason are true, but Reason is not the correct explanation of Assertion.
c. Assertion is true, but Reason is false.

d. Assertion is false, but Reason is true.
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