Free GARP RAI Exam Questions

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 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 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 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 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