Question 1

Which approach is most effective for feature selection in large-scale data?

Options :

A : LASSO

B : Random Forest

C : Recursive Feature Elimination

D : PCA

Answer: A

Question 2

How does the concept of "Residual Connections" in ResNet architectures improve deep network training?

Options :

A : Residual connections apply normalization to the input of each layer, which speeds up convergence and enhances model stability.

B : Residual connections allow gradients to flow through skip connections, mitigating the vanishing gradient problem and enabling the training of deeper networks.

C : Residual connections incorporate dropout to prevent overfitting by randomly omitting network units during training.

D : Residual connections use data augmentation to increase the diversity of training data and improve model robustness.

Answer: B

Question 3

What is a potential ethical concern regarding the deployment of AI in decision-making systems?

Options :

A : Applying AI algorithms uniformly across different domains without adaptation

B : Increasing the model-s complexity to handle more diverse data

C : Reducing the number of parameters in the AI model to improve performance

D : Using AI solely for enhancing operational efficiency without assessing fairness

Answer: D

Question 4

Which type of neural network architecture is primarily used for sequence prediction tasks, such as language modeling and time-series forecasting?

Options :

A : Convolutional Neural Network

B : Feedforward Neural Network

C : Recurrent Neural Network

D : Transformer Network

Answer: C

Question 5

Which approach best manages the risk of privacy breaches in machine learning systems dealing with sensitive data?

Options :

A : Using more frequent data updates

B : Increasing model complexity

C : Implementing differential privacy mechanisms

D : Employing larger training datasets

Answer: C

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