MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company wants to serve a scikit-learn model via SageMaker. The inference code requires a custom preprocessing step that is not in the default scikit-learn container. What is the simplest way to deploy?
⚠ Common exam trap
Many exam-takers confuse SageMaker's built-in algorithm containers with the ability to inject arbitrary code via environment variables or Lambda layers, when in fact custom preprocessing requires a custom Docker image that extends the official container.
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 Docker image extending the SageMaker scikit-learn container
Extending the SageMaker scikit-learn container with a custom Docker image is the simplest and most direct way to add custom preprocessing logic that is not included in the default container. SageMaker's pre-built scikit-learn container supports only standard scikit-learn inference code; any additional dependencies or custom preprocessing steps require you to build a custom image that inherits from the official SageMaker scikit-learn image and adds your code. This approach avoids the complexity of managing separate inference pipelines or external services.
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 custom Docker image extending the SageMaker scikit-learn container
Why this is correct
Extending the container with the custom preprocessing is straightforward and supported.
- ✗
Package the code in a Lambda layer and use SageMaker hosting
Why it's wrong here
Lambda layers are not directly compatible with SageMaker containers.
- ✗
Use SageMaker Batch Transform with a custom processing script
Why it's wrong here
Batch Transform is for batch inference, not real-time.
- ✗
Use SageMaker Neo to compile the model and add preprocessing
Why it's wrong here
Neo is for model optimization, not for adding custom preprocessing code.
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