MLA-C01 Data Preparation for Machine Learning Practice Question

A company uses Amazon SageMaker Data Wrangler to prepare data for ML. The dataset contains a timestamp column and sensor readings from IoT devices. The data scientist needs to create features such as moving averages and rolling statistics over time windows. Which Data Wrangler transformation type should be selected?

⚠ Common exam trap

Test-takers frequently confuse 'Group by and aggregate' with 'Window function' because both involve aggregation, but Group by reduces rows while Window functions preserve row-level detail, which is essential for rolling statistics.

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

✓

Window function

Window functions in Amazon SageMaker Data Wrangler allow you to compute moving averages, rolling statistics, and other time-window-based aggregations over ordered partitions of data. This is the correct transformation type because it directly supports operations like `SUM() OVER (ORDER BY timestamp ROWS BETWEEN 2 PRECEDING AND CURRENT ROW)` without requiring custom code or losing row-level granularity.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Join
Why it's wrong here
Join combines datasets, not for time-series features.
✗
Custom Python script
Why it's wrong here
Custom script works but is less efficient than the built-in Window function.
✗
Group by and aggregate
Why it's wrong here
Group by aggregates per group but does not support rolling windows.
✓
Window function
Why this is correct
Window function is designed for rolling computations like moving averages.

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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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