- A
Amazon Athena
Athena is serverless and supports SQL on S3.
- B
Amazon QuickSight
Why wrong: QuickSight is a BI tool, not a query engine.
- C
AWS Glue
Why wrong: Glue is for ETL jobs, not ad-hoc queries.
- D
Amazon Redshift
Why wrong: Redshift is a data warehouse that requires provisioning and management.
Question 1,085 of 1,755
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. 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 needs to query a dataset stored as Parquet files in Amazon S3 using standard SQL without managing any infrastructure. Which service should they use?
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
✓
Amazon Athena
Amazon Athena is a serverless interactive query service that allows you to query data stored in Amazon S3 using standard SQL without any infrastructure to manage. It directly reads Parquet files from S3 and executes queries using Presto under the hood, making it the ideal choice for this use case.
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.
- ✓
Amazon Athena
Why this is correct
Athena is serverless and supports SQL on S3.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Amazon QuickSight
Why it's wrong here
QuickSight is a BI tool, not a query engine.
- ✗
AWS Glue
Why it's wrong here
Glue is for ETL jobs, not ad-hoc queries.
- ✗
Amazon Redshift
Why it's wrong here
Redshift is a data warehouse that requires provisioning and management.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse AWS Glue (which has a 'crawler' and 'catalog' feature) as a query service, but it is an ETL and cataloging tool, not an interactive SQL query engine; similarly, Redshift Spectrum might seem serverless but still requires a provisioned Redshift cluster.
Detailed technical explanation
How to think about this question
Athena leverages the Presto distributed SQL engine and the Hive metastore to read Parquet files directly from S3, taking advantage of columnar storage and predicate pushdown to minimize data scanned. It supports partitioning and compression (e.g., Snappy, Gzip) to reduce query costs, and charges only per query based on the amount of data scanned. A real-world scenario is querying terabytes of log data partitioned by date in Parquet format, where Athena can return results in seconds without any cluster setup.
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.
TExam Day Tips
- 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
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.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Amazon Athena — Amazon Athena is a serverless interactive query service that allows you to query data stored in Amazon S3 using standard SQL without any infrastructure to manage. It directly reads Parquet files from S3 and executes queries using Presto under the hood, making it the ideal choice for this use case.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 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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