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Question 1

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 2

What is a key benefit of integrating business stakeholders into the AI development process?

Options :

A : Delegating technical decisions solely to the development team

B : Ensuring alignment with business objectives and requirements

C : Limiting stakeholder input to avoid project delays

D : Minimizing discussions to focus on technical challenges

Answer: B

Question 3

What is the primary objective of a Denoising Autoencoder?

Options :

A : To reconstruct clean data from noisy inputs

B : To add noise to the input for robust learning

C : To compress data

D : To handle missing values

Answer: A

Question 4

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 5

Which evaluation metric is most suitable for imbalanced datasets?

Options :

A : F1 Score

B : Precision

C : Mean Squared Error

D : Accuracy

Answer: A