MLS-C01 Modeling Practice Question
A company wants to use Amazon SageMaker to train a model using a custom algorithm packaged in a Docker container. Which approach should they 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
✓
Use the SageMaker SDK to create an Estimator with the image URI of the custom container
The correct approach is to use the SageMaker SDK to create an Estimator with the image URI of the custom container, as SageMaker supports bring-your-own-container for custom algorithms. Option A is incorrect because SageMaker Ground Truth is a labeling service, not for training custom algorithms. Option B is incorrect because SageMaker Autopilot automates model selection and tuning, but it does not support custom containers. Option D is incorrect because built-in algorithms are predefined and do not allow custom code.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Use SageMaker Ground Truth
Why it's wrong here
SageMaker Ground Truth is a data labelling service, not a training environment; it cannot execute a custom Docker container for model training. It is tempting because Ground Truth integrates with SageMaker workflows and can preprocess data, but the stem’s requirement to train a model using a custom algorithm container demands SageMaker’s built-in training functionality, which Ground Truth lacks. Ground Truth would be correct if the task were to generate labelled training data from raw inputs.
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Use SageMaker Autopilot
Why it's wrong here
Autopilot automatically selects algorithms, not custom containers.
- ✓
Use the SageMaker SDK to create an Estimator with the image URI of the custom container
Why this is correct
The Estimator can accept a custom Docker image for training.
- ✗
Select one of the built-in algorithms in SageMaker
Why it's wrong here
Built-in algorithms are not custom.
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