Free GARP RAI Exam Questions

Question 1

A financial institution is developing a credit risk model using a machine learning algorithm. The data science team decides to split their dataset into three parts: training, validation, and test sets. What is the primary purpose of the validation set in this setup?

A : To finalize the model by evaluating it on unseen data

B : To select the best performing model among multiple algorithms

C : To adjust model parameters to fit the training data

D : To test the model’s performance under different scenarios

Answer: B

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

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 4

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 5

A financial institution is developing a credit risk model using a machine learning algorithm. The data science team decides to split their dataset into three parts: training, validation, and test sets. What is the primary purpose of the validation set in this setup?

A : To finalize the model by evaluating it on unseen data

B : To select the best performing model among multiple algorithms

C : To adjust model parameters to fit the training data

D : To test the model’s performance under different scenarios

Answer: B