MLS-C01 Modeling Practice Question
A machine learning engineer is deploying a model to SageMaker for real-time inference. The model is a TensorFlow SavedModel. Which SageMaker capability should be used to create an endpoint?
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
✓
SageMaker hosting with TensorFlow Serving container
SageMaker provides managed TensorFlow serving containers for deploying TensorFlow SavedModels to real-time endpoints. Option B is wrong because SageMaker Pipelines is used for building and managing ML workflows, not for deploying models to endpoints. Option C is wrong because SageMaker Model Monitor is used for monitoring model quality and drift, not for deployment. Option D is wrong because SageMaker Ground Truth is used for labeling data, not for hosting models.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
SageMaker hosting with TensorFlow Serving container
Why this is correct
SageMaker provides managed TensorFlow serving containers, which can be used to host the SavedModel for real-time inference.
- ✗
SageMaker Pipelines
Why it's wrong here
SageMaker Pipelines is for building and automating ML workflows, not for hosting models.
- ✗
SageMaker Model Monitor
Why it's wrong here
SageMaker Model Monitor is for monitoring model quality and drift, not for hosting.
- ✗
SageMaker Ground Truth
Why it's wrong here
SageMaker Ground Truth is for data labeling, not for hosting.
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