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: Jan 05, 2026

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Question 1

A retail bank uses self-training to classify loan applicants as high or low risk, but it finds that updating the model after each new labeled data point is computationally intensive. Which approach can the bank use to reduce this burden?

A : Select multiple best-predicted instances in each round

B : Use an unsupervised learning model instead

C : Increase the complexity of the initial model

D : Use transductive learning instead

Answer: A

Question 2

A problem-solving AI model is being developed to solve the Tower of Hanoi puzzle by systematically planning moves to achieve the final goal. The model needs to evaluate intermediate steps to clear disks strategically for future moves. Which approach would best help the model to complete this task?

A : Tactical lookahead, as it considers each immediate move without long-term planning.

B : Strategic planning, by identifying intermediate moves that contribute to the end goal.

C : Reinforcement learning, by rewarding moves that lead to progress in the puzzle.

D : Randomized search, by exploring different moves without a predefined plan.

Answer: B

Question 3

You are given the following feature vector and statistical parameters:Feature Vector: [1, 3.6, 9, 10]Mean: 5Variance: 2.89What is the standardized feature vector?

A : [−2.35,−0.82, 2.35, 2.94]

B : [−2.34,−0.81, 1.81, 1.97]

C : [−1.81,−0.81, 2.34, 2.76]

D : [−2.34,−0.81, 2.34, 3.76]

Answer: A

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

While using Naïve Bayes for text classification, a bank wants to classify customer feedback as “Good” or “Bad.” How does the model handle the probability of classifying feedback with words it hasn’t encountered?

A : It assigns zero probability to the class if a new word is present.

B : It ignores the new word in classification.

C : It calculates probabilities based on prior data alone.

D : It smooths probabilities to avoid zero probabilities.

Answer: D

Page: 1 / 66

Total 330 Questions | Updated On: Jan 05, 2026

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