Quick Answer
The answer is Amazon Athena, as it enables the quickest ad-hoc summary statistics on S3 CSV data without provisioning any infrastructure. Athena leverages serverless SQL queries directly against data stored in S3, allowing you to compute mean, median, min, and max across numeric columns in seconds using standard SQL functions like AVG, PERCENTILE_CONT, MIN, and MAX. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of serverless analytics versus provisioned services—a common trap is choosing SageMaker Data Wrangler or AWS Glue ETL, which require spinning up notebook instances or jobs, adding latency. QuickSight is also a distractor because it focuses on visualization, not direct computation. Remember the memory tip: “Athena for ad-hoc, no infrastructure to catch.”
This MLS-C01 practice question tests your understanding of exploratory data analysis. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 performing EDA on a dataset with 500,000 rows and 10 columns. The dataset is stored in an S3 bucket as CSV files. The scientist wants to generate summary statistics (mean, median, min, max) for all numeric columns. Which service allows the quickest ad-hoc analysis without provisioning any infrastructure?
AWS Glue ETL
Why wrong: Requires job configuration.
Amazon Athena
Serverless SQL query service.
Amazon SageMaker Data Wrangler
Why wrong: Requires a notebook instance.
Amazon QuickSight
Why wrong: Not for direct summary statistics.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Amazon Athena
Option B is correct because Amazon Athena can query data in S3 directly using SQL. Option A is wrong because SageMaker Data Wrangler requires a notebook instance. Option C is wrong because AWS Glue ETL requires job setup. Option D is wrong because QuickSight is for visualization, not direct summary statistics.
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.
AWS Glue ETL
Why it's wrong here
Requires job configuration.
Amazon Athena
Why this is correct
Serverless SQL query service.
Related concept
Read the scenario before looking for a memorised answer.
Amazon SageMaker Data Wrangler
Why it's wrong here
Requires a notebook instance.
Amazon QuickSight
Why it's wrong here
Not for direct summary statistics.
Common exam traps
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
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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 — study guide chapter
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FAQ
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
The correct answer is: Amazon Athena — Option B is correct because Amazon Athena can query data in S3 directly using SQL. Option A is wrong because SageMaker Data Wrangler requires a notebook instance. Option C is wrong because AWS Glue ETL requires job setup. Option D is wrong because QuickSight is for visualization, not direct summary statistics.
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.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 2026
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.
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