The correct choice is using PySpark with window functions in SageMaker Processing because it efficiently handles aggregating time series data by partitioning the dataset by customer and applying a sliding window over the last 30 days without requiring manual iteration or data shuffling. This approach leverages Spark’s distributed computing to scale across large transaction volumes, making it far more efficient than row-by-row Python loops or pandas, which can hit memory limits. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of optimal feature engineering patterns in SageMaker, where PySpark is a first-class processing framework for large-scale, grouped time-series aggregations. A common trap is choosing pandas or Athena SQL, but remember that SageMaker Processing with PySpark is purpose-built for distributed, window-based computations. Memory tip: “Window functions partition and order—no loops, no borders.”
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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 data scientist is exploring a dataset containing customer transactions. They want to create a feature that captures the average purchase amount per customer over the last 30 days. Which approach is most efficient in Amazon SageMaker Processing?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use PySpark with window functions in SageMaker Processing
Option D is correct because using PySpark in SageMaker Processing with window functions is efficient for grouped time-series aggregations. Option A is wrong because iterating over rows is inefficient in Python. Option B is wrong because SQL in Athena may be simpler but requires moving data. Option C is wrong because pandas may not scale to large datasets.
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.
✗
Use Amazon Athena SQL query with GROUP BY
Why it's wrong here
Incorrect: Athena is a query service, but the data must be in a queryable format; this is not a processing job.
✓
Use PySpark with window functions in SageMaker Processing
Why this is correct
Correct: PySpark window functions are optimized for large-scale grouped rolling aggregates.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a Python script with a for loop to calculate per customer
Why it's wrong here
Incorrect: For loops are inefficient and do not scale.
✗
Use pandas groupby and rolling functions
Why it's wrong here
Incorrect: Pandas may not handle large datasets efficiently in memory.
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 MLS-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.
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use PySpark with window functions in SageMaker Processing — Option D is correct because using PySpark in SageMaker Processing with window functions is efficient for grouped time-series aggregations. Option A is wrong because iterating over rows is inefficient in Python. Option B is wrong because SQL in Athena may be simpler but requires moving data. Option C is wrong because pandas may not scale to large datasets.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-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.
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This MLS-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 MLS-C01 exam.
Question Discussion
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