Quick Answer
The correct answer is AWS Glue and Amazon Athena, which together form the backbone of a serverless data lake catalog and query solution on Amazon S3. AWS Glue provides the fully managed Data Catalog that automatically crawls and stores metadata—including schema, partitions, and location—for CSV, Parquet, and even image files, while Athena uses that catalog to run standard SQL queries directly against the data in S3 without any ETL or infrastructure management. On the AWS Certified Data Engineer Associate DEA-C01 exam, this pairing tests your understanding of the separation between metadata management and query execution in a data lake architecture; a common trap is to confuse Athena with a data warehouse or to think Glue is only for ETL jobs. Remember the memory tip: Glue catalogs the “what and where,” Athena queries the “how and now.”
This DEA-C01 practice question tests your understanding of data store management. 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 is designing a data lake on Amazon S3. The data includes CSV files, Parquet files, and images. The data engineering team needs to catalog the metadata and enable SQL queries. Which TWO AWS services should be used together?
Amazon EMR
Why wrong: EMR is for processing frameworks like Spark, not for ad-hoc SQL.
Amazon Redshift Spectrum
Why wrong: Redshift Spectrum requires a Redshift cluster, additional cost.
Amazon QuickSight
Why wrong: QuickSight is a BI tool, not a catalog or query engine.
Amazon Athena
Athena can directly query data in S3 using the Glue Data Catalog.
AWS Glue
Glue crawlers can discover schema and populate the Data Catalog.
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 correct because it is a serverless interactive query service that can directly query data stored in Amazon S3 using standard SQL, without needing to load or transform data. AWS Glue is correct because it provides a fully managed data catalog (AWS Glue Data Catalog) that stores metadata about the data lake's schema, partitions, and locations, which Athena can use to discover and query the data efficiently.
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 frameworks like Spark, not for ad-hoc SQL.
Amazon Redshift Spectrum
Why it's wrong here
Redshift Spectrum requires a Redshift cluster, additional cost.
Amazon QuickSight
Why it's wrong here
QuickSight is a BI tool, not a catalog or query engine.
Amazon Athena
Why this is correct
Athena can directly query data in S3 using the Glue Data Catalog.
Related concept
Read the scenario before looking for a memorised answer.
AWS Glue
Why this is correct
Glue crawlers can discover schema and populate the Data Catalog.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often confuse Amazon Redshift Spectrum (which requires a Redshift cluster) with Athena (which is serverless), or they think Amazon EMR is needed for SQL queries on S3, not realizing Athena provides a simpler, cluster-free solution.
Detailed technical explanation
AWS Glue Data Catalog is a central metadata repository that stores table definitions, partition information, and file formats (e.g., CSV, Parquet) for data in S3, and it is Hive-compatible, meaning Athena can use it as its metastore. Athena uses Presto under the hood to execute SQL queries, and it automatically handles schema-on-read, allowing it to query diverse formats like CSV and Parquet without prior transformation. A real-world scenario is a data lake with mixed file formats where Glue crawlers automatically infer schemas and update the catalog, enabling Athena to run ad-hoc analytics without manual schema management.
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
Data Store Management — This question tests Data Store Management — Read the scenario before looking for a memorised answer..
The correct answer is: Amazon Athena — Amazon Athena is correct because it is a serverless interactive query service that can directly query data stored in Amazon S3 using standard SQL, without needing to load or transform data. AWS Glue is correct because it provides a fully managed data catalog (AWS Glue Data Catalog) that stores metadata about the data lake's schema, partitions, and locations, which Athena can use to discover and query the data efficiently.
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 DEA-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 DEA-C01 exam.
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