MLA-C01 ML Model Development Practice Question
A data scientist suspects that a deep learning model is overfitting. They enable SageMaker Debugger and want to detect overfitting automatically. 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
✓
Overfit
The overfit rule in SageMaker Debugger monitors training and validation loss divergence, a key indicator of 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.
- ✗
ExplodingGradients
Why it's wrong here
Detects gradients becoming too large, not overfitting.
- ✗
PoorWeightInitialization
Why it's wrong here
Checks for weight initialization issues, not overfitting.
- ✓
Overfit
Why this is correct
The Overfit rule alerts when validation loss stops decreasing while training loss continues.
- ✗
DeadRelu
Why it's wrong here
Detects dead ReLU neurons, not overfitting.
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