Question 1

In the context of explainability, what is a common limitation of using global feature importance methods?

Options :

A : They require complex mathematical calculations

B : They increase model training time significantly

C : They only work with linear models

D : They miss feature interaction effects

Answer: D

Question 2

Which strategy is effective in aligning AI projects with stakeholder expectations?

Options :

A : Focusing on internal team preferences over stakeholder requirements

B : Limiting stakeholder input to avoid conflicting opinions

C : Regularly engaging stakeholders and incorporating their feedback into project planning

D : Making decisions based solely on technical feasibility without stakeholder input

Answer: C

Question 3

What role does the decision boundary play in linear classification algorithms?

Options :

A : It defines the hyperplane that maximizes the margin.

B : It determines the probability thresholds for classification.

C : It separates the classes based on the feature space.

D : It adjusts the learning rate to optimize performance.

Answer: C

Question 4

What is the main advantage of using Generative Adversarial Networks (GANs) over traditional generative models?

Options :

A : Enhanced interpretability of generated samples

B : Capability to generate highly realistic samples

C : Ability to handle sequential data

D : Reduced computational complexity

Answer: B

Question 5

In reinforcement learning, what is the purpose of the reward signal?

Options :

A : It penalizes actions

B : It optimizes actions

C : It updates policies

D : It guides exploration

Answer: B