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?

⚠ Common exam trap

The trap is assuming Script Mode allows arbitrary framework versions; in reality Script Mode still relies on a SageMaker-managed container, so only BYOC provides full control over the framework version.

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

When a team needs a custom training container with a specific PyTorch version or custom dependencies that are not available in the built-in SageMaker images, the correct approach is Bring Your Own Container (BYOC), where they build a Docker image and push it to Amazon ECR, then reference it in the SageMaker estimator. This gives full control over the framework version and environment.

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
The built-in PyTorch estimator pulls AWS-managed images, so it cannot supply a custom container with a chosen PyTorch version. It is tempting because setting framework_version is the standard route when a supported AWS image already matches the required version.
✓
Use SageMaker Bring Your Own Container (BYOC) with a custom Docker image
Why this is correct
BYOC lets the team supply a custom Docker image containing their exact PyTorch version, so the training environment matches their dependency requirements precisely. SageMaker's prebuilt PyTorch containers fix the framework version, which cannot satisfy the stated need for a specific PyTorch build.
✗
Use SageMaker Script Mode with a PyTorch script
Why it's wrong here
Script Mode still runs inside AWS-managed framework containers, so it cannot deliver a custom container with a specific PyTorch version. It is tempting because Script Mode is correct when only the training code is custom while the AWS image suffices.
✗
Use SageMaker Autopilot to automatically select the container
Why it's wrong here
Autopilot automates algorithm selection and tuning for tabular data; it does not build or accept a custom PyTorch training container. It is tempting because Autopilot removes manual container and hyperparameter work, and would be right for automated tabular model development.

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MLA-C01 ML Model Development Practice Question

Why each option matters

Option-by-option breakdown

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