MLA-C01 ML Model Development Practice Question
A machine learning engineer is using SageMaker Debugger to detect if a neural network has dead ReLU units during training. Which built-in rule should they enable?
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
✓
DeadRelu
The 'DeadRelu' rule in Debugger monitors the fraction of ReLU activations that are zero and alerts if too many neurons are dead.
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 this is correct
DeadRelu rule specifically detects dead ReLU units.
- ✗
Overfit
Why it's wrong here
Overfit rule detects overfitting, not dead ReLU.
- ✗
ExplodingGradients
Why it's wrong here
Exploding gradients is a different issue.
- ✗
LossNotDecreasing
Why it's wrong here
LossNotDecreasing monitors loss plateau, not dead neurons.
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