MLA-C01 ML Model Development Practice Question
A data scientist is using SageMaker to train a custom PyTorch model for image classification. They want to use SageMaker Debugger to detect training issues. Which TWO built-in rules are most relevant for detecting common training problems? (Select TWO.)
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
ExplodingGradients detects gradients becoming too large, and Overfit detects when validation loss diverges from training loss. Both are common issues.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
DataDistribution
Why it's wrong here
Not a built-in rule in Debugger.
- ✓
Overfit
Why this is correct
The Overfit rule in SageMaker Debugger monitors the validation loss relative to the training loss; if validation loss begins to increase while training loss continues to decrease, the rule emits a warning. This directly addresses the image classification scenario, where a custom PyTorch model can easily memorise training data rather than generalising, satisfying the stem’s requirement to detect common training problems.
- ✓
ExplodingGradients
Why this is correct
Detects large gradient values that can destabilize training.
- ✗
ImageQuality
Why it's wrong here
Not a built-in rule in Debugger.
- ✗
ConfusionMatrix
Why it's wrong here
Not a built-in rule; confusion matrices are metrics, not rules.
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Same concept, more angles
1 more way this is tested on MLA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. An ML engineer is debugging a training job that is consistently failing due to an out-of-memory error. The engineer is using SageMaker's built-in XGBoost algorithm. Which Debugger rule can help identify the issue?
medium- ✓ A.Exploding gradients
- B.Overfit
- C.Dead relu
- D.OOM rule
Why A: The 'Exploding gradients' rule detects when gradients become too large, which is a common cause of training instability but not necessarily OOM. The 'Overfit' rule detects overfitting. The 'Dead relu' rule is for ReLU activation. None of these directly address OOM. However, Debugger does not have a specific OOM rule; instead, the engineer should monitor memory utilization via CloudWatch or adjust instance type. Among the options, 'Exploding gradients' is the most relevant because large gradients can lead to memory spikes.
JA
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Senior Network & Security Engineer · founder of Courseiva
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