MLA-C01 ML Model Development Practice Question
A team is training a PyTorch model using SageMaker and wants to use their own custom training container with a specific PyTorch version. Which approach should they use?
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 SageMaker Bring Your Own Container (BYOC) with a custom Docker image
BYOC (Bring Your Own Container) allows teams to package their own environment, including custom PyTorch versions, into a Docker container and use it with SageMaker.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the SageMaker built-in PyTorch estimator and set the framework_version
Why it's wrong here
Built-in estimators only support specific versions; custom versions require BYOC.
- ✓
Use SageMaker Bring Your Own Container (BYOC) with a custom Docker image
Why this is correct
BYOC allows full control over the container, including custom PyTorch versions.
- ✗
Use SageMaker Script Mode with a PyTorch script
Why it's wrong here
Script Mode uses SageMaker's managed PyTorch container, not a custom one.
- ✗
Use SageMaker Autopilot to automatically select the container
Why it's wrong here
Autopilot is for AutoML, not for custom container training.
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