AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 184

An ML engineer is training a text generation model on Amazon SageMaker AI. After several epochs, the loss function does not converge, and the model’s accuracy on the validation dataset starts to show oscillating results.

The ML engineer needs to ensure that the model achieves generalization.

Which solution will meet this requirement?

Answer options

• A. Increase the learning rate and decrease the mini-batch size.
• B. Increase the learning rate as the number of epochs increases.
• C. Decrease the learning rate and increase the mini-batch size.
• D. Decrease the learning rate and decrease the mini-batch size.

Correct answer: C

Explanation

The correct answer is C because decreasing the learning rate helps stabilize the training process by allowing finer adjustments to the model weights, while increasing the mini-batch size helps provide a more accurate estimate of the gradient, leading to better generalization. The other options either increase the learning rate, which can cause instability, or do not help the model generalize effectively.