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22. Study the given Regression Line and answer the questions given below: (2)

a. If a student studied 5 hours, what is the predicted test score according to the regression line?

Ans. As per graph, around 78-80 marks

b. If another student’s actual score is higher than the predicted score, where will their point lie on the
graph — above or below the regression line?

Ans. Above the regression line
23. How is regression analysis useful in real life? (2)
Ans. Regression analysis helps businesses and researchers forecast outcomes like profits, rainfall, or exam
results. For instance, schools can predict student performance based on study hours. It helps make
informed decisions using data-driven predictions and trend analysis.
24. Explain the importance of data modelling. (2)

Ans. Data modelling acts like a blueprint for organising and storing data. It helps in designing databases
that are accurate, consistent, and easy to update. By planning how data is linked, organisations reduce
duplication and improve data quality. This planning step ensures better data analysis and system
performance in AI applications.
25. If you were to design a student database, which model would you use and why? (2)

Ans. A relational model would be best because student information like names, classes, and marks can be
stored in tables. These tables can be linked using a common ID, making the database organized and
easy to update.

Question 4
26. What are the four key principles of AI ethics? (2)
Ans. The main principles are Fairness, Transparency, Accountability, and Privacy. Fairness ensures no
discrimination, Transparency makes AI explainable, Accountability ensures responsibility, and Privacy
protects user data. Together, they make AI systems trustworthy.
27. Explain the concept of bias in AI with an example. (2)
Ans. AI bias occurs when systems show unfair preferences due to faulty data. For example, a hiring algorithm
may favour male applicants if trained mostly on male employee data. Balanced, diverse datasets and
regular testing help reduce such bias.
28. Why are ethics important in Artificial Intelligence? [2]

Ans. Ethics ensure that AI systems are fair, transparent, and beneficial to society. Without ethical rules, AI
may cause harm, violate privacy, or create bias. Ethical AI supports trust, accountability, and fairness.
29. Explain the various stages of the AI Project Cycle with examples. (4)
Ans. The AI Project Cycle involves five stages: Problem Scoping (define the goal), Data Acquisition (collect
data), Data Exploration (analyse patterns), Modelling (build AI models), and Evaluation (test results).

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