DEA-C01 Data Store Management Practice Question
A data engineer is building a data lake on Amazon S3 and needs to catalog metadata for a large number of CSV files stored in a nested folder structure. The engineer wants to automatically discover the schema and update the AWS Glue Data Catalog as new files are added. Which solution should the engineer use?
⚠ Common exam trap
Many candidates confuse AWS Lake Formation with AWS Glue crawlers; Lake Formation manages permissions and governance but does not automatically discover schemas.
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
✓
Create an AWS Glue crawler that points to the S3 bucket and schedule it to run periodically.
AWS Glue crawlers are designed to automatically scan data stores, infer schemas, and create or update tables in the Data Catalog. Scheduling a crawler ensures that new files are detected and the catalog stays current. This is the standard, low-effort solution for the described scenario. Other options either require manual intervention or do not provide automatic schema discovery.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Run an AWS Glue ETL job that reads the CSV files and writes to a new location.
Why it's wrong here
An AWS Glue ETL job can transform data but does not automatically catalog metadata. While it can write to the Data Catalog, it does not discover schemas for existing files or update as new files arrive unless explicitly programmed. This adds complexity and does not meet the requirement for automatic discovery and catalog updates.
- ✓
Create an AWS Glue crawler that points to the S3 bucket and schedule it to run periodically.
Why this is correct
AWS Glue crawlers automatically scan data sources, infer schemas, and populate the Data Catalog. They can be scheduled to run periodically to detect new files and update table definitions. This meets the requirement for automatic schema discovery and catalog updates with minimal manual effort. The crawler can handle nested folder structures and CSV format.
- ✗
Use AWS Lake Formation to define a data lake and register the S3 location.
Why it's wrong here
AWS Lake Formation simplifies setting up a data lake and managing permissions, but it does not automatically discover schemas or update the Data Catalog. It relies on the Data Catalog, which still needs to be populated via crawlers or manual definitions. Thus, it does not fulfill the automatic schema discovery requirement.
- ✗
Manually define tables in the AWS Glue Data Catalog using the AWS Management Console.
Why it's wrong here
Manual table definition does not automatically discover schemas or update as new files are added. It requires ongoing manual effort to keep the catalog in sync, which is error-prone and not scalable. The scenario explicitly asks for automatic discovery and updates, so this approach fails to meet the 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 |
Go deeper
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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