MLA-C01 ML Model Development Practice Question
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?
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
✓
Exploding gradients
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Exploding gradients
Why this is correct
Exploding gradients can cause memory spikes leading to OOM; Debugger can capture this.
- ✗
Overfit
Why it's wrong here
Overfit rule detects overfitting, not OOM.
- ✗
Dead relu
Why it's wrong here
Dead relu detects dead neurons, not memory issues.
- ✗
OOM rule
Why it's wrong here
There is no built-in OOM rule in SageMaker Debugger.
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