MLS-C01 SageMaker PyTorch Container Practice Question
A machine learning engineer is deploying a PyTorch model to SageMaker. The model requires custom inference logic. Which approach should the engineer 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
✓
Create a custom inference script and use the SageMaker PyTorch container
SageMaker allows you to provide a custom inference script (entry point) when using the PyTorch container, enabling custom inference logic. Option A is wrong because the built-in container as-is would not incorporate custom logic. Option B is wrong because SageMaker Ground Truth is for labeling, not model deployment. Option C is wrong because SageMaker Processing is for data processing, not inference.
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 a SageMaker built-in PyTorch container as-is
Why it's wrong here
SageMaker built-in PyTorch container as-is does not support custom inference logic without modification.
- ✗
Use SageMaker Ground Truth to deploy the model
Why it's wrong here
SageMaker Ground Truth is for labeling, not for deploying models.
- ✗
Use SageMaker Processing to run inference
Why it's wrong here
SageMaker Processing is designed for data processing, not for running inference.
- ✓
Create a custom inference script and use the SageMaker PyTorch container
Why this is correct
Creating a custom inference script and using the SageMaker PyTorch container allows you to define custom processing logic for inference.
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