MLA-C01 ML Model Development Practice Question
A company is using SageMaker Debugger to monitor a training job for a deep learning model. They want to detect when gradients become extremely large, which may cause training instability. Which built-in rule should they use?
⚠ Common exam trap
MLA-C01 often tests the distinction between ExplodingGradients and VanishingGradients — candidates must read the symptom (extremely large vs. extremely small) carefully, as both are gradient-related Debugger rules.
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
SageMaker Debugger provides a built-in rule named ExplodingGradients that monitors gradient tensors during training and triggers when gradient magnitudes exceed a threshold, indicating training instability. It is purpose-built to detect the exact condition described — gradients becoming extremely large. The rule can emit a warning or stop the job based on configuration.
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 it's wrong here
DeadRelu detects neurons stuck outputting zero, not exploding gradients. It is tempting because both are training-instability rules in SageMaker Debugger, but DeadRelu inspects activation outputs. The exploding-gradient rule monitors gradient magnitude, which is the metric the scenario specifies.
- ✓
ExplodingGradients
Why this is correct
The ExplodingGradients built-in rule monitors gradient values across training iterations and raises an alert when they exceed a threshold, indicating instability. This matches the stem's requirement to detect gradients becoming extremely large during the SageMaker Debugger training job.
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VanishingGradients
Why it's wrong here
VanishingGradients detects gradients shrinking toward zero, the opposite failure mode. It is tempting because it is a gradient-monitoring built-in rule, but it triggers on small magnitudes. The scenario requires detecting extremely large gradients, which the exploding-gradient rule covers.
- ✗
Overfit
Why it's wrong here
Overfit detects a widening gap between training and validation loss, not gradient magnitude. It is tempting because it is a SageMaker Debugger built-in rule, but it analyses loss curves across epochs. The scenario needs gradient-magnitude monitoring, which Overfit does not perform.
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.