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: Feb 18, 2026

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Question 1

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

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

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 financial institution is creating a model inventory. Which of the following should be included to ensure comprehensive governance?

A : Only the model’s purpose and validation report.

B : Model owner, but not developer information, for clarity.

C : A description of model assumptions but not its risk classification.

D : Model owner, purpose, methodology, assumptions, limitations, and risk tier.

Answer: D

Question 5

A data scientist is tuning the ridge regression model’s hyperparameter, λ, to control the trade-off between model fit and complexity. If λ is set too high, what effect is most likely?

A : The model will overfit the data

B : The model will be underfit, as coefficients are heavily penalized

C : The model will become equivalent to OLS regression

D : The model will ignore the penalty term entirely

Answer: B

Page: 1 / 66

Total 330 Questions | Updated On: Feb 18, 2026

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