DEA-C01 Data Store Management Practice Question
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?
⚠ Common exam trap
Test-takers frequently 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.
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 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.
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 EMR
Why it's wrong here
Amazon EMR processes and transforms data with Spark or Hive, but it provides no persistent metadata catalog or SQL query endpoint over S3 objects by itself. It is tempting because EMR genuinely handles large-scale analytics; it would be the right choice for heavy batch transformation jobs, not for cataloguing CSV, Parquet and image metadata.
- ✗
Amazon Redshift Spectrum
Why it's wrong here
Redshift Spectrum queries S3 data using an external schema, but it does not itself build or maintain the metadata catalog the scenario requires. It is tempting because it delivers SQL over S3 without loading, yet it would be correct alongside AWS Glue rather than replacing the catalog service.
- ✗
Amazon QuickSight
Why it's wrong here
QuickSight is a business intelligence visualisation service that consumes queried data; it neither catalogs S3 metadata nor executes SQL against the data lake. It is tempting because it queries Athena and Redshift sources, but it would be correct for building dashboards after cataloguing and querying are already in place.
- ✓
Amazon Athena
Why this is correct
Amazon Athena queries S3 data directly using standard SQL, reading the table and partition metadata held in the AWS Glue Data Catalog. It satisfies the SQL query requirement without loading data, complementing Glue's cataloguing role for the CSV, Parquet and image lake.
- ✓
AWS Glue
Why this is correct
AWS Glue crawlers scan S3 objects, including CSV, Parquet and images, populating the Glue Data Catalog with table definitions and partition metadata. That central catalogue is the prerequisite the team needs before any SQL engine can query the lake's heterogeneous data.
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 |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.