MLA-C01 ML Model Development Practice Question

A machine learning engineer is training a model using SageMaker and wants to set up monitoring to detect if gradients become too large, which could destabilize training. Which SageMaker Debugger built-in rule should they enable?

⚠ Common exam trap

MLA-C01 often tests the specific purpose of each SageMaker Debugger built-in rule, and candidates may confuse ExplodingGradients with LossNotDecreasing or DeadRelu, which address different training issues.

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 built-in rule in SageMaker Debugger is specifically designed to detect when gradient values become excessively large during training, which can cause numerical instability and prevent convergence. It monitors the gradients tensor and triggers if the ratio of the maximum absolute gradient to the average absolute gradient exceeds a threshold (default 10.0). This directly addresses the engineer's requirement to detect destabilizing gradients.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

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DeadRelu
Why it's wrong here
DeadRelu detects ReLU units that never activate, indicating vanishing gradients rather than exploding ones. It is tempting because both are gradient pathologies, but the stem specifies gradients becoming too large, which the built-in ExplodingTensor rule monitors by comparing gradient norms against a threshold.
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LossNotDecreasing
Why it's wrong here
LossNotDecreasing monitors whether the training loss plateaus or rises across steps, so it cannot detect exploding gradients — the vanishing/exploding gradient rules (VanishingGradient, ExplodingGradient) track tensor magnitudes instead. It is tempting because stalled loss is a genuine training failure, and LossNotDecreasing would be the right rule when diagnosing a model that simply stops improving.
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Overfit
Why it's wrong here
The Overfit rule compares training versus validation loss to flag overfitting, so it never inspects gradient tensors and cannot detect exploding gradients. It is tempting because it is a genuine built-in Debugger rule for diagnosing generalisation problems, and it would be the right choice when validation loss diverges from training loss.
✓
ExplodingGradients
Why this is correct
ExplodingGradients monitors gradient magnitudes during training and raises an alert when values exceed a threshold, directly detecting the instability described. It satisfies the requirement to catch gradients becoming too large before they destabilise training, unlike rules targeting vanishing gradients, overfitting, or loss convergence.

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MLA-C01 ML Model Development Practice Question

Why each option matters

Option-by-option breakdown

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