- A
Write a custom PySpark SQL query in a SQL transform that uses the `AVG` window function partitioned by customer ID and ordered by timestamp with a range between 7 days preceding and current row
This computes the exact rolling average per customer over a 7-day window, which is the requirement.
- B
Export the data to Amazon SageMaker Feature Store and use point-in-time queries with a 7-day lookback
Why wrong: Point-in-time queries are for retrieving historical feature values, not for computing rolling aggregations within Data Wrangler.
- C
Use the built-in 'Aggregate' transform with a group-by on customer ID and average of transaction amount
Why wrong: A simple aggregate loses the time window; it computes a global average per customer, not a rolling 7-day average.
- D
Use the 'Handle Missing' transform to fill missing values with the mean transaction amount
Why wrong: This addresses missing values, not feature engineering for rolling averages.
MLA-C01 Practice Question: A machine learning engineer needs to prepare a…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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 machine learning engineer needs to prepare a dataset containing customer transactions for training a fraud detection model. The dataset includes features such as transaction amount, timestamp, merchant category, and customer ID. The engineer wants to create a feature representing the average transaction amount per customer over the last 7 days. Which approach should be used in Amazon SageMaker Data Wrangler?
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
Write a custom PySpark SQL query in a SQL transform that uses the `AVG` window function partitioned by customer ID and ordered by timestamp with a range between 7 days preceding and current row
SageMaker Data Wrangler supports custom SQL queries via PySpark SQL, which can compute windowed aggregations like a rolling average partitioned by customer ID over a time window. This is the most direct and scalable approach.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 PySpark SQL query in a SQL transform that uses the `AVG` window function partitioned by customer ID and ordered by timestamp with a range between 7 days preceding and current row
Why this is correct
This computes the exact rolling average per customer over a 7-day window, which is the requirement.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Export the data to Amazon SageMaker Feature Store and use point-in-time queries with a 7-day lookback
Why it's wrong here
Point-in-time queries are for retrieving historical feature values, not for computing rolling aggregations within Data Wrangler.
- ✗
Use the built-in 'Aggregate' transform with a group-by on customer ID and average of transaction amount
Why it's wrong here
A simple aggregate loses the time window; it computes a global average per customer, not a rolling 7-day average.
- ✗
Use the 'Handle Missing' transform to fill missing values with the mean transaction amount
Why it's wrong here
This addresses missing values, not feature engineering for rolling averages.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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.
What to study next
Got this wrong? Here's your next step.
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Write a custom PySpark SQL query in a SQL transform that uses the `AVG` window function partitioned by customer ID and ordered by timestamp with a range between 7 days preceding and current row — SageMaker Data Wrangler supports custom SQL queries via PySpark SQL, which can compute windowed aggregations like a rolling average partitioned by customer ID over a time window. This is the most direct and scalable approach.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jul 4, 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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