MLS-C01 Modeling Practice Question
A data scientist is using Amazon SageMaker to train a model with a custom Docker container. The training job fails with an error: 'Container exited with code 137'. What is the most likely cause?
⚠ Common exam trap
Watch out — candidates often confuse exit code 137 with a generic 'container error' or 'runtime timeout' (option B), not realizing that 137 specifically signals a SIGKILL from the OOM killer due to memory exhaustion.
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
✓
The training instance ran out of memory.
Exit code 137 (128+9) indicates the container was killed by the SIGKILL signal, which typically occurs when the Linux Out-Of-Memory (OOM) killer terminates a process that has exceeded its memory allocation. In Amazon SageMaker, training instances have finite memory, and if the training algorithm or data loading exceeds that limit, the OOM killer forcibly stops the container, resulting in exit code 137.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The training data was corrupted.
Why it's wrong here
Corrupted data would cause a different error.
- ✗
The training job exceeded the maximum runtime.
Why it's wrong here
That would be a timeout, not exit code 137.
- ✗
The Docker entrypoint script was not found.
Why it's wrong here
That would cause exit code 127 or similar.
- ✓
The training instance ran out of memory.
Why this is correct
Exit code 137 indicates OOM kill.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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