MLS-C01 Modeling Practice Question
A team has trained a deep learning model on Amazon SageMaker using a custom Docker container. They want to deploy the model to a SageMaker endpoint for real-time inference. Which format should the model artifacts be in?
⚠ Common exam trap
Candidates often assume SageMaker accepts common archive formats like .zip or any file structure, but the exam specifically tests the requirement for a single .tar.gz file as the only supported format for model artifacts in custom container deployments.
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
✓
A single .tar.gz file containing the model files.
Amazon SageMaker requires model artifacts to be packaged as a single .tar.gz file when using a custom Docker container for real-time inference. This compressed archive must contain the model files (e.g., model.pth, model.h5) and any necessary inference code, as SageMaker extracts the archive to the /opt/ml/model directory during deployment. The .tar.gz format ensures consistent extraction and compatibility with SageMaker's inference pipeline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
A single .tar.gz file containing the model files.
Why this is correct
SageMaker requires model artifacts as a tarball.
- ✗
A folder on S3 with the model files.
Why it's wrong here
SageMaker expects a tar.gz file, not a folder.
- ✗
No format requirement; any file works.
Why it's wrong here
SageMaker requires a specific format.
- ✗
A .zip file containing the model files.
Why it's wrong here
SageMaker expects .tar.gz, not .zip.
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