Question 1

Which method is recommended for managing risks related to data privacy when deploying ML models in production environments?

Options :

A : Reducing training data size

B : Increasing model complexity

C : Hyperparameter tuning

D : Data anonymization and encryption

Answer: D

Question 2

Which type of neural network is best suited for handling spatial data, such as images, by leveraging convolutional layers to detect patterns?

Options :

A : Convolutional Neural Network

B : Generative Adversarial Network

C : Feedforward Neural Network

D : Recurrent Neural Network

Answer: A

Question 3

In the context of ML risk management, what is a key strategy for handling adversarial attacks on deployed models?

Options :

A : Increasing the model-s size and complexity

B : Reducing the number of input features

C : Implementing robust defenses and monitoring

D : Limiting the model-s exposure to external inputs

Answer: C

Question 4

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 5

Why might one choose a linear kernel over a non-linear kernel in SVMs?

Options :

A : Linear kernels provide better scalability in high dimensions.

B : Linear kernels are more efficient for large, sparse datasets.

C : Non-linear kernels require more support vectors to perform well.

D : Non-linear kernels tend to overfit small training datasets.

Answer: B