Quick Answer
Amazon Athena is the correct choice because it enables ad-hoc SQL on S3 without any data movement or infrastructure provisioning. As a serverless interactive query service built on Presto, Athena lets you run standard SQL directly against sensor data stored in S3, charging only for the data scanned per query—making it ideal for exploratory analysis where you don’t know the schema or query patterns in advance. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of when to use Athena versus services like Redshift Spectrum or Glue ETL; a common trap is selecting Redshift for its SQL capabilities, but Athena’s serverless, pay-per-query model is explicitly designed for ad-hoc exploration without managing clusters. Remember the memory tip: “Athena for ad-hoc, no data to move—just query in place.”
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 company stores sensor data in Amazon S3. A data scientist wants to explore the data using SQL without moving it. Which AWS service should they use?
Amazon EMR
Why wrong: EMR is for processing using frameworks like Spark.
Amazon Redshift
Why wrong: Redshift requires loading data into a cluster.
Amazon QuickSight
Why wrong: QuickSight is a BI tool for dashboards.
Amazon Athena
Athena queries data directly in S3 using SQL.
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
Amazon Athena is the correct choice because it is a serverless interactive query service that allows you to analyze data directly in Amazon S3 using standard SQL without any data movement or infrastructure management. Athena uses Presto under the hood and charges only for the data scanned per query, making it ideal for ad-hoc exploratory analysis on sensor data stored in S3.
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.
Amazon EMR
Why it's wrong here
EMR is for processing using frameworks like Spark.
Amazon Redshift
Why it's wrong here
Redshift requires loading data into a cluster.
Amazon QuickSight
Why it's wrong here
QuickSight is a BI tool for dashboards.
Amazon Athena
Why this is correct
Athena queries data directly in S3 using SQL.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often confuse Amazon Athena with Amazon EMR or Redshift, thinking they need a full cluster or data warehouse for SQL queries, but Athena is specifically designed for serverless, direct S3 querying with no data movement.
Detailed technical explanation
Athena leverages the Presto distributed SQL engine with a connector for S3 that supports reading various formats (Parquet, ORC, JSON, CSV) and can push down predicate filters to reduce data scanned. A subtle behavior is that Athena charges per query based on the amount of data scanned, so partitioning and columnar formats (e.g., Parquet) are critical for cost efficiency in exploratory analysis. In a real-world scenario, a data scientist might use Athena to run ad-hoc SQL queries on terabytes of sensor logs in S3 without waiting for ETL jobs or cluster spin-up times.
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
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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 — Amazon Athena is the correct choice because it is a serverless interactive query service that allows you to analyze data directly in Amazon S3 using standard SQL without any data movement or infrastructure management. Athena uses Presto under the hood and charges only for the data scanned per query, making it ideal for ad-hoc exploratory analysis on sensor data stored in S3.
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 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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