Question 1

How might AI exacerbate global inequality at a societal level?

Options :

A : By allowing less industrialized countries to adopt AI at a faster rate than industrialized countries

B : By increasing job opportunities equally across all countries

C : By benefiting wealthier, more industrialized countries more than others

D : By promoting open access to AI resources for all nations

Answer: C

Question 2

A risk management team is evaluating the QRM results of a newly developed credit risk model. The model output shows a low risk estimate, and team members are confident in their decision to proceed with increased exposure. However, they fail to consider that the model does not account for current market volatility. What critical factor did they overlook in interpreting the results?

Options :

A : Point estimates provide accurate assessments.

B : Risk model assumptions can be ignored.

C : Quantitative data is more reliable than qualitative.

D : Model limitations should be considered, especially unmodeled risks.

Answer: D

Question 3

A bank is implementing a quantitative risk model (QRM) and needs to ensure that the model is designed in a way that can be accurately translated into code. Which implementation task should the bank prioritize first?

Options :

A : Model design

B : Core implementation

C : System implementation

D : Post-implementation review

Answer: A

Question 4

A portfolio manager is using a multi-arm bandit model to decide daily between several stocks for short-term investments. Each stock has a different historical return profile. In this scenario, what would be the "reward" in MAB terminology?

Options :

A : The action taken to select a stock

B : The expected return from each stock

C : The choice of policy

D : The agent’s portfolio composition

Answer: B

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?

Options :

A : Model-based approach

B : Policy gradient

C : Value-based approach

D : Supervised learning

Answer: C

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