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?
⚠ Common exam trap
MLA-C01 often tests the exact built-in rule names for Debugger — candidates may pick a plausible-sounding rule like 'PoorWeightInitialization' or confuse Overfit with a general loss-monitoring rule, but the exam expects the precise 'Overfit' rule.
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
SageMaker Debugger includes a built-in rule called Overfit that monitors the gap between training and validation loss (or accuracy) and triggers when the model begins to overfit. It is the direct, purpose-built rule for automatic overfitting detection. Enabling it requires the training script to emit both training and validation metrics via the debugger hook.
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
ExplodingGradients detects gradient magnitudes growing abnormally large during training, causing divergence, not the generalisation gap that defines overfitting. It is the right rule when gradients spike or loss becomes NaN, whereas overfitting is identified by comparing training and validation loss trends.
- ✗
PoorWeightInitialization
Why it's wrong here
PoorWeightInitialization flags weights whose initial distribution is unsuitable for the chosen activation, causing slow or stalled early training. It applies at initialisation, before any epoch, so it cannot detect the later divergence between training and validation loss that signals overfitting.
- ✓
Overfit
Why this is correct
The Overfit rule directly satisfies the requirement to detect overfitting automatically. It monitors training and validation loss across steps, raising an issue when validation loss stops decreasing while training loss continues falling. This divergence is the defining signature of overfitting, so it flags the problem without manual threshold tuning.
- ✗
DeadRelu
Why it's wrong here
DeadRelu flags activations stuck at zero, indicating vanishing gradients or dead neurons, not a widening gap between training and validation performance. It is the correct rule when diagnosing stalled ReLU learning, whereas overfitting detection requires comparing training and validation loss through the LossNotDecreasing rule.
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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