easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A data scientist is training a regression model…
A data scientist is training a regression model in Amazon SageMaker. The dataset contains missing values in several features. The scientist wants to handle missing values as part of the training pipeline to ensure consistency between training and inference. Which approach should the scientist use?
⚠ Common exam trap
Watch out — candidates often assume SageMaker Autopilot (Option B) is the correct choice because it automates preprocessing, but they miss that the question specifically requires a custom, reproducible pipeline that ensures consistency between training and inference, which Autopilot does not expose for custom control.
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 a scikit-learn container in SageMaker to create a preprocessing step that imputes missing values and include it in the inference pipeline.
It uses a scikit-learn container within SageMaker to create a preprocessing step that imputes missing values, then includes that step in the inference pipeline. This ensures the same imputation logic (e.g., mean, median, or custom strategy) is applied consistently during both training and inference, preventing data drift and maintaining reproducibility. SageMaker Pipelines or the built-in scikit-learn container allow the preprocessing to be serialized as part of the model artifact, so inference requests automatically undergo the same transformation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Impute missing values in a separate Jupyter notebook and save the cleaned data.
Why it's wrong here
Inconsistent between training and inference.
- ✗
Use SageMaker Autopilot to automatically handle missing values.
Why it's wrong here
Autopilot may not allow custom logic.
- ✗
Drop all rows with missing values before training.
Why it's wrong here
Dropping rows loses data.
- ✓
Use a scikit-learn container in SageMaker to create a preprocessing step that imputes missing values and include it in the inference pipeline.
Why this is correct
Consistent preprocessing in pipeline.
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