MLA-C01 ML Model Development Practice Question

A machine learning engineer is using Amazon SageMaker Debugger to monitor a training job for a deep neural network. They receive a rule alert indicating 'exploding gradients'. Which action should they take to address this issue?

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

✓

Reduce the learning rate

Exploding gradients occur when gradients become too large, causing instability. Reducing the learning rate mitigates this. Increasing batch size can also help by smoothing gradients, but reducing learning rate is a direct solution.

Answer analysis

Option-by-option breakdown

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

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Use a smaller batch size
Why it's wrong here
Smaller batch sizes often lead to noisier gradients, which can lead to larger gradient magnitudes, potentially worsening exploding gradients.
✓
Reduce the learning rate
Why this is correct
Reducing the learning rate decreases the size of weight updates, helping to prevent gradients from exploding.
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Increase the number of layers to absorb gradients
Why it's wrong here
Adding layers typically increases model capacity but does not directly address exploding gradients; it may even exacerbate the problem.
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Increase the learning rate
Why it's wrong here
Increasing the learning rate may worsen exploding gradients because it makes weight updates larger.

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MLA-C01 ML Model Development Practice Question

Why each option matters

Option-by-option breakdown

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