MLS-C01 Modeling Practice Question

A team is training a neural network for image classification using Amazon SageMaker. The training loss decreases rapidly but the validation loss starts increasing after a few epochs. Which action should the team take?

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Implement early stopping based on validation loss

Early stopping halts training when the validation loss stops improving (or starts increasing), preventing overfitting. Option A is incorrect because reducing batch size does not directly address overfitting; it may add noise to gradients. Option B is incorrect because adding more convolutional layers increases model complexity, likely worsening overfitting. Option C is incorrect because increasing the learning rate can cause the model to diverge or overshoot minima, not reduce overfitting.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Reduce the batch size
Why it's wrong here
Reducing batch size does not prevent overfitting.
✗
Add more convolutional layers
Why it's wrong here
Adding layers increases complexity and may worsen overfitting.
✗
Increase the learning rate
Why it's wrong here
Increasing learning rate may cause divergence.
✓
Implement early stopping based on validation loss
Why this is correct
Early stopping prevents overfitting.

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MLS-C01 Modeling Practice Question

Why each option matters

Option-by-option breakdown

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