- A
AWS Glue Data Catalog
Glue Data Catalog stores metadata and schemas.
- B
Amazon Kinesis Data Streams
Why wrong: Kinesis is for streaming data, not cataloging.
- C
Amazon RDS
Why wrong: RDS is a relational database, not a catalog.
- D
Amazon DynamoDB
Why wrong: DynamoDB is a NoSQL database, not a data catalog.
- E
Amazon Athena
Why wrong: Incorrect. Amazon Athena is a serverless query service that reads data from S3 and uses the Glue Data Catalog for schema information. It does not act as a catalog itself; it requires an existing catalog to provide schema information.
Question 1,747 of 1,755
MLS-C01 AWS Glue Data Catalog Practice Question
This MLS-C01 practice question tests your understanding of data engineering. 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. A key principle to apply: aWS Glue Data Catalog. 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 engineering team is designing a data lake on AWS. They need to store raw data in S3 and allow multiple analytics services to query the data. Which TWO services can be used to catalog and provide schema information for the data?
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
✓
AWS Glue Data Catalog
AWS Glue Data Catalog is a fully managed metadata repository that stores table definitions, schema information, and partition details for data in S3. Amazon Athena, while it can query data in S3 using SQL, does not provide its own catalog; it relies on the Glue Data Catalog for schema information. Therefore, only AWS Glue Data Catalog directly catalogs and provides schema information.
Key principle: AWS Glue Data Catalog
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
AWS Glue Data Catalog
Why this is correct
Glue Data Catalog stores metadata and schemas.
Related concept
AWS Glue Data Catalog
- ✗
Amazon Kinesis Data Streams
Why it's wrong here
Kinesis is for streaming data, not cataloging.
- ✗
Amazon RDS
Why it's wrong here
RDS is a relational database, not a catalog.
- ✗
Amazon DynamoDB
Why it's wrong here
DynamoDB is a NoSQL database, not a data catalog.
- ✗
Amazon Athena
Why it's wrong here
Incorrect. Amazon Athena is a serverless query service that reads data from S3 and uses the Glue Data Catalog for schema information. It does not act as a catalog itself; it requires an existing catalog to provide schema information.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap is that Amazon Athena can create and query tables using DDL statements, leading candidates to think it serves as a catalog. However, Athena stores its table definitions in the Glue Data Catalog, making the Data Catalog the actual schema repository. Thus, only AWS Glue Data Catalog is the correct service for cataloging and providing schema information.
Detailed technical explanation
How to think about this question
The AWS Glue Data Catalog uses a Hive-compatible metastore, allowing it to work seamlessly with Apache Spark, Presto, and Hive engines. When you run a crawler, it connects to data sources (e.g., S3), infers schema by sampling files, and writes table metadata to the catalog, which Athena then reads via its built-in JDBC/ODBC driver. This decouples schema management from compute, enabling multiple query engines to share the same metadata without redundant schema definitions.
KKey Concepts to Remember
- AWS Glue Data Catalog
- Amazon Athena
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
AWS Glue Data Catalog
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.
Review aWS Glue Data Catalog, then practise related MLS-C01 questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Data Engineering — This question tests Data Engineering — AWS Glue Data Catalog.
What is the correct answer to this question?
The correct answer is: AWS Glue Data Catalog — AWS Glue Data Catalog is a fully managed metadata repository that stores table definitions, schema information, and partition details for data in S3. Amazon Athena, while it can query data in S3 using SQL, does not provide its own catalog; it relies on the Glue Data Catalog for schema information. Therefore, only AWS Glue Data Catalog directly catalogs and provides schema information.
What should I do if I get this MLS-C01 question wrong?
Review aWS Glue Data Catalog, then practise related MLS-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
AWS Glue Data Catalog
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Last reviewed: Jul 4, 2026
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