100% Free GARP RAI Practice Test Questions

Question 1

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 2

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 3

A trading firm uses reinforcement learning to determine the best time to buy or sell an asset. They have partial information about the state transitions and aim to maximize long-term profits by identifying the best action in each state. Which reinforcement learning approach is most suitable for this scenario?

A : Model-based approach

B : Policy gradient

C : Value-based approach

D : Supervised learning

Answer: C

Question 4

A risk manager is evaluating an NLP-based sentiment analysis model that uses dictionary approaches across multiple datasets. He notices inconsistent results when analyzing different document types. What could explain this inconsistency?

A : The dictionary approach is ineffective for NLP applications

B : The dictionary approach requires separate datasets for each document

C : The dictionary's effectiveness is limited by the document type and language style

D : The dictionary approach is too complex for real-world use

Answer: C

Question 5

A trading firm uses reinforcement learning to determine the best time to buy or sell an asset. They have partial information about the state transitions and aim to maximize long-term profits by identifying the best action in each state. Which reinforcement learning approach is most suitable for this scenario?

A : Model-based approach

B : Policy gradient

C : Value-based approach

D : Supervised learning

Answer: C

Free Practice GARP RAI Exam Questions 2025