MLS-C01 Exploratory Data Analysis Practice Question
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 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 PySpark with window functions in SageMaker Processing
Using PySpark with window functions in SageMaker Processing allows efficient distributed computation for grouped time-series aggregations like average purchase amount per customer over the last 30 days. Option A is wrong because Amazon Athena SQL requires moving data out of SageMaker Processing and may not be as tightly integrated. Option C is wrong because iterating over rows with a Python for loop is inefficient and does not scale. Option D is wrong because pandas groupby and rolling functions may not scale to large datasets in a distributed environment; SageMaker Processing with PySpark provides better performance.
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.
- ✗
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.
Go deeper
Related to this question
About these practice questions
This MLS-C01 question is part of Courseiva's 1,672-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.