MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company uses Amazon SageMaker to train a model. The training job uses a custom Docker container. The job fails with the error 'CannotStartContainerError: API error (500).' Which of the following is the most likely cause?
⚠ Common exam trap
A common mix-up: candidates confuse container start errors with runtime errors — they often pick 'syntax error' or 'missing data' because those are common training failures, but the specific Docker API error points to a pre-execution infrastructure issue, not a code or data problem.
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 Docker image is built for a different CPU architecture.
The error 'CannotStartContainerError: API error (500)' occurs when the Docker daemon on the SageMaker training instance fails to start the container. The most common cause is a CPU architecture mismatch: if the Docker image is built for a different architecture (e.g., ARM64) than the SageMaker training instance (which uses x86_64), the container cannot execute. SageMaker training instances are x86_64-based, so an image built for ARM64 will trigger this error at container launch time.
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 Docker image is built for a different CPU architecture.
Why this is correct
Incompatible architecture prevents container from running.
- ✗
The training script has a syntax error.
Why it's wrong here
Syntax errors cause Python errors, not container start errors.
- ✗
The S3 input data is missing.
Why it's wrong here
Missing data causes data loading errors, not container start.
- ✗
The output path is not writable.
Why it's wrong here
Output path issues cause errors during training, not start.
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Senior Network & Security Engineer · founder of Courseiva
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