easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A machine learning team needs to deploy a model…
A machine learning team needs to deploy a model that was built using scikit-learn. They want to use SageMaker for hosting. Which approach should they take?
⚠ Common exam trap
The trap here is that candidates often overcomplicate the solution by assuming a custom Docker container is always required for scikit-learn, overlooking the fact that SageMaker provides a fully managed, built-in container specifically for this framework.
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
✓
Package the model artifacts and use the SageMaker built-in scikit-learn container for inference
SageMaker provides a pre-built, optimized Docker container for scikit-learn that supports inference. By packaging the model artifacts (e.g., a joblib or pickle file) and deploying them using the built-in container, the team avoids the overhead of custom container creation while ensuring compatibility with SageMaker's hosting infrastructure, including automatic scaling and load balancing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create a Jupyter notebook that loads the model and runs predictions on the SageMaker notebook instance
Why it's wrong here
Notebook instances are for development, not production hosting.
- ✗
Create a custom Docker container with scikit-learn and deploy it on SageMaker
Why it's wrong here
While SageMaker supports custom Docker containers for frameworks not natively integrated, scikit-learn is already a built-in framework with an optimised SageMaker Estimator that handles container provisioning, training, and deployment automatically. The custom container approach introduces unnecessary overhead and bypasses SageMaker’s native integration, which directly supports scikit-learn models out of the box. This option is tempting because custom containers are the correct approach for unsupported frameworks or when specific library versions or system dependencies are required that SageMaker’s pre-built images do not include.
- ✗
Launch a SageMaker training job with the model and use the training instance as an endpoint
Why it's wrong here
Training instances cannot serve as endpoints; separate hosting is required.
- ✓
Package the model artifacts and use the SageMaker built-in scikit-learn container for inference
Why this is correct
Built-in container supports scikit-learn models; simply point to model artifacts.
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