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 26, 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

In cost complexity pruning, a bank uses a parameter, α, to balance the residual sum of squares (RSS) and the number of terminal nodes. What is the primary role of this parameter?

A : Maximize the training accuracy

B : Minimize the test set error

C : Control the trade-off between tree size and fit

D : Increase the complexity of the tree

Answer: C

Question 4

A critical infrastructure provider in the U.S. wants to ensure responsible AI deployment to enhance service reliability. How should the provider proceed under guidance from the AI Safety and Security Board (AISSB)?

A : Only focus on AI deployment in non-critical areas to minimize risk.

B : Deploy AI solutions that solely increase service efficiency without security considerations.

C : Limit AI usage to what is already industry-standard to avoid compliance issues.

D : Implement recommendations from the AISSB to address AI-related disruptions to critical services, protecting national security and public health.

Answer: D

Question 5

An insurance company uses two different AI models for policy approval, one for Group A and another for Group B. The confusion matrices for each group are as follows:Group A Confusion Matrix:True Positives: 50False Positives: 10False Negatives: 40True Negatives: 90Group B Confusion Matrix:True Positives: 30False Positives: 20False Negatives: 70True Negatives: 80What fairness technique should the company use to balance the performance between the two groups?

A : Equalize the number of true positives for both groups.

B : Adjust thresholds to achieve similar false positive rates for both groups.

C : Apply a higher penalty for false negatives in Group A.

D : Implement demographic parity by ensuring equal acceptance rates for both groups.

Answer: D

Page: 1 / 66

Total 330 Questions | Updated On: Mar 26, 2026

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