Question 1

A hedge fund uses a multi-arm bandit approach to decide between various trading strategies daily, aiming to maximize returns. Why is the multi-arm bandit model suitable for this application?

Options :

A : Each trading strategy-s performance is independent from day to day.

B : The model can predict future stock prices accurately.

C : It allows the fund to always choose the best strategy immediately.

D : It considers the interdependence of trading strategies over time.

Answer: A

Question 2

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?

Options :

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 3

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: 50

False Positives: 10

False Negatives: 40

True Negatives: 90

Group B Confusion Matrix:

True Positives: 30

False Positives: 20

False Negatives: 70

True Negatives: 80

What fairness technique should the company use to balance the performance between the two groups?

Options :

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 4

You are given the following feature vector and statistical parameters:

Feature Vector: [1, 3.6, 9, 10]

Mean: 5

Variance: 2.89

What is the standardized feature vector?

Options :

A : [−2.35,−0.82, 2.35, 2.94]

B : [−2.34,−0.81, 1.81, 1.97]

C : [−1.81,−0.81, 2.34, 2.76]

D : [−2.34,−0.81, 2.34, 3.76]

Answer: A

Question 5

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