MLA-C01 ML Model Development Practice Question
A company is using SageMaker Debugger to monitor a training job for a deep learning model. They want to detect when gradients become extremely large, which may cause training instability. Which built-in rule should they use?
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
✓
ExplodingGradients
The ExplodingGradients rule monitors gradient norms and raises an alert if they exceed a threshold.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
DeadRelu
Why it's wrong here
DeadRelu detects neurons that always output zero.
- ✓
ExplodingGradients
Why this is correct
ExplodingGradients detects gradients becoming too large.
- ✗
VanishingGradients
Why it's wrong here
VanishingGradients detects gradients becoming too small.
- ✗
Overfit
Why it's wrong here
Overfit detects overfitting by comparing training and validation loss.
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Written by Johnson Ajibi, MSc IT Security
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