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: Nov 28, 2024

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Question 1

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 2

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

Question 3

What is a primary reason to remove duplicate observations during data cleaning?

A : To improve data storage efficiency

B : To avoid biases in analysis

C : To handle missing data effectively

D : To address inconsistent recording

Answer: B

Question 4

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

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: Nov 28, 2024

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