Quick Answer
The correct transformation type is the Window function. This is because window functions in SageMaker Data Wrangler are specifically designed to compute moving averages and rolling statistics over ordered partitions of data, such as sensor readings grouped by device and ordered by timestamp. They allow you to perform operations like SUM() OVER (ORDER BY timestamp ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) directly in the visual interface, preserving row-level granularity without requiring custom PySpark or SQL code. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of which built-in transformation supports time-series feature engineering without data leakage. A common trap is selecting “Group by” or “Aggregate,” which collapse rows and lose the time-ordered sequence needed for rolling calculations. Remember the mnemonic: “Windows keep rows, groups collapse them”—if you need a moving value per row, always reach for the window function.
This MLA-C01 practice question tests your understanding of data preparation for machine learning. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Join
Why wrong: Join combines datasets, not for time-series features.
Custom Python script
Why wrong: Custom script works but is less efficient than the built-in Window function.
Group by and aggregate
Why wrong: Group by aggregates per group but does not support rolling windows.
Window function
Window function is designed for rolling computations like moving averages.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates 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.
Detailed technical explanation
Under the hood, Data Wrangler translates window functions into Spark SQL window specifications, which partition and order the data before applying aggregate functions like `AVG`, `SUM`, or `LAG` over a defined frame (e.g., rows between 2 preceding and current row). A subtle behavior is that window functions require the data to be sorted by the timestamp column; if the data is not sorted, the results will be incorrect. In real-world IoT scenarios, missing timestamps or irregular intervals can cause window boundaries to include unexpected rows, so you may need to resample or fill gaps before applying the window transformation.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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FAQ
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.
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