MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is using Amazon SageMaker to train a model using a custom Docker container. The training job fails with an error message indicating that the container exited with a non-zero code. Which THREE steps should the data scientist take to diagnose the issue? (Choose THREE.)
⚠ Common exam trap
A common mix-up: candidates think retrying the job (Option A) is a valid first step for transient errors, but the MLS-C01 exam emphasizes systematic debugging using tools like CloudWatch Logs and SageMaker Debugger rather than guesswork.
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
✓
Use the SageMaker Debugger to capture system metrics and output tensors for analysis.
SageMaker Debugger can capture system metrics (e.g., CPU/GPU utilization, memory) and output tensors during training, which helps identify issues like resource exhaustion or problematic gradients that cause non-zero exit codes. It provides deep visibility into the training process without requiring code changes, making it a powerful diagnostic tool for custom container failures.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Retry the training job with the same configuration; the error might be transient.
Why it's wrong here
Retrying without diagnosis will likely fail again.
- ✓
Use the SageMaker Debugger to capture system metrics and output tensors for analysis.
Why this is correct
Debugger can capture detailed metrics that help identify why the container exited.
- ✓
Check the CloudWatch Logs for the training job to see the container's stdout and stderr.
Why this is correct
CloudWatch logs capture the container's output, which often contains error details.
- ✗
Increase the number of training instances to distribute the workload.
Why it's wrong here
Increasing instances does not fix a code or environment issue.
- ✓
Run the container locally using SageMaker Local Mode to simulate the training environment.
Why this is correct
Local mode allows debugging without incurring cloud costs.
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