Free GARP RAI Exam Questions

Question 1

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 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

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 4

In cost complexity pruning, a bank uses a parameter, α, to balance the residual sum of squares (RSS) and the number of terminal nodes. What is the primary role of this parameter?

A : Maximize the training accuracy

B : Minimize the test set error

C : Control the trade-off between tree size and fit

D : Increase the complexity of the tree

Answer: C

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