Question 590 of 835
MLA-C01 Data Preparation for Machine Learning Practice Question
A data engineer is using Amazon SageMaker Data Wrangler to prepare a dataset. The dataset contains a column 'review_date' with timestamps. The engineer wants to extract the day of the week as a new feature. How should this transformation be performed in Data Wrangler?
⚠ Common exam trap
AWS often tests the distinction between built-in transforms and custom scripting, and the trap here is that candidates may assume they need to write a Python script (Option A) because they are familiar with pandas, overlooking Data Wrangler's native 'day_of_week' transform that is simpler and more appropriate for the visual workflow.
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 'day_of_week' transform on the 'review_date' column
Amazon SageMaker Data Wrangler includes a built-in 'day_of_week' transform that directly extracts the day of the week (e.g., Monday, Tuesday) from a timestamp column without requiring custom code or additional formatting. This transform is optimized for Data Wrangler's visual interface and integrates seamlessly with its processing pipeline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Write a custom Python script using pandas dt.day_name()
Why it's wrong here
Unnecessarily complex when built-in transform exists.
- ✗
Use one-hot encoding on the timestamp
Why it's wrong here
One-hot encoding is for categorical features, not extraction.
- ✗
Use the 'extract' transform with format '%A'
Why it's wrong here
Not a supported transform in Data Wrangler.
- ✓
Use the 'day_of_week' transform on the 'review_date' column
Why this is correct
Built-in transform extracts day of week (Monday=0, etc.).
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Last reviewed: Jun 30, 2026
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