AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 23

An ML engineer is training a simple neural network model. The ML engineer tracks the performance of the model over time on a validation dataset. The model's performance improves substantially at first and then degrades after a specific number of epochs.
Which solutions will mitigate this problem? (Choose two.)

Answer options

• A. Enable early stopping on the model.
• B. Increase dropout in the layers.
• C. Increase the number of layers.
• D. Increase the number of neurons.
• E. Investigate and reduce the sources of model bias.

Correct answer: A, B

Explanation

Enabling early stopping allows the training process to halt when performance on the validation set starts to decline, preventing overfitting. Increasing dropout helps reduce overfitting by randomly dropping units during training, which can improve generalization. The other options, such as adding layers or neurons, may worsen overfitting, while addressing model bias is important but not directly related to the immediate issue of performance degradation.