MLA-C01 Data Preparation for Machine Learning Practice Question

A data scientist is working with a dataset that contains missing values in several numeric features. The data scientist wants to impute the missing values with the median of each feature. Which Amazon SageMaker Data Wrangler transformation should be used?

⚠ Common exam trap

It's easy for candidates to confuse the 'Replace missing with constant' option (which uses a fixed value) with the median strategy, or they may overcomplicate the solution by choosing a custom Python transform when a built-in option exists.

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

✓

Handle missing values (with median strategy)

Amazon SageMaker Data Wrangler includes a built-in 'Handle missing values' transformation that supports imputation with the median strategy. This directly matches the requirement to replace missing numeric values with the median of each feature without writing 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.

✗
Replace missing with constant
Why it's wrong here
Constant replacement does not use median.
✗
Custom transform with Python
Why it's wrong here
Possible but not the native median imputation method.
✗
Drop missing rows
Why it's wrong here
Drop missing rows removes data, not imputation.
✓
Handle missing values (with median strategy)
Why this is correct
This transform allows imputation with median.

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MLA-C01 Data Preparation for Machine Learning Practice Question

Why each option matters

Option-by-option breakdown

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