MLA-C01 ML Model Development Practice Question
During a SageMaker training job, the loss stops decreasing and the validation accuracy plateaus early. SageMaker Debugger rules are enabled. Which rule is MOST likely to identify 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
✓
Overfit rule
The overfit rule detects when validation accuracy plateaus or decreases while training accuracy continues to improve, which is a sign of overfitting. Exploding gradients detects gradient spikes, dead relu detects dead neurons, and weight distribution checks weight distributions but not directly 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.
- ✗
Weight distribution rule
Why it's wrong here
Weight distribution rule checks for weight updates but not directly overfitting.
- ✗
Exploding gradients rule
Why it's wrong here
Exploding gradients cause loss to become NaN or spike, not plateau.
- ✓
Overfit rule
Why this is correct
Overfit rule monitors validation vs training metrics to detect overfitting.
- ✗
Dead relu rule
Why it's wrong here
Dead relu detects neurons that always output zero, not overfitting.
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