Free GARP RAI Exam Questions

Become GARP Certified with updated RAI exam questions and correct answers

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Total 330 Questions | Updated On: Mar 13, 2026

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Question 1

Which of the following scenarios represents a binary classification problem?

A : Predicting the probability of different stock prices

B : Forecasting next month’s sales in dollars

C : Determining if a transaction is fraudulent or legitimate

D : Grouping customers into three income tiers

Answer: C

Question 2

A tech firm is developing a model to predict credit risk and wants to reduce testing bias while still evaluating performance. Which approach should it use?

A : Conduct black-box testing to assess functionality without internal bias.

B : Focus solely on white-box testing for thorough code analysis.

C : Rely on artificial test cases for full test coverage.

D : Avoid testing until after model deployment to prevent interruptions.

Answer: A

Question 3

A Board of Directors at a global tech firm is considering developing a data-collection AI that could enhance user experience but involves intrusive tracking. According to rule consequentialism, how should they decide?

A : Approve the AI if it increases company revenue and shareholder value

B : Approve the AI if it adheres to rules that generally maximize trust and benefit users

C : Reject the AI if it does not comply with all regional data privacy laws

D : Reject the AI as it could potentially reduce the number of future data projects

Answer: B

Question 4

A tech firm is developing a model to predict credit risk and wants to reduce testing bias while still evaluating performance. Which approach should it use?

A : Conduct black-box testing to assess functionality without internal bias.

B : Focus solely on white-box testing for thorough code analysis.

C : Rely on artificial test cases for full test coverage.

D : Avoid testing until after model deployment to prevent interruptions.

Answer: A

Question 5

A company wants to apply reinforcement learning to manage its warehouse inventory, learning when to restock based on demand fluctuations. What is a potential drawback of using reinforcement learning in this scenario?

A : It cannot adapt to new demand patterns over time.

B : It requires high amounts of labeled data for training.

C : It may initially perform poorly as it learns from trial-and-error.

D : It only works for supervised learning tasks.

Answer: C

Page: 1 / 66

Total 330 Questions | Updated On: Mar 13, 2026

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