DEA-C01 Data Store Management Practice Question
A company stores application logs in Amazon S3 in JSON format. The logs are partitioned by year/month/day. A data engineer needs to create a table in the AWS Glue Data Catalog so that Amazon Athena can query the logs efficiently. The engineer wants to minimize query costs and ensure that new partitions are automatically recognized. Which combination of actions should the engineer take?
⚠ Common exam trap
The trap here is assuming that a scheduled AWS Glue crawler is required to discover new partitions, when partition projection can eliminate that need entirely for a known partition scheme.
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 a table with partition projection enabled for year/month/day and set the storage location to the S3 prefix.
Partition projection in Athena allows the table to compute partition locations dynamically based on a defined pattern, so new partitions are recognized immediately without manual intervention or crawlers. This reduces metadata operations and enables partition pruning, which lowers query costs. Manual partition management, non-partitioned tables, or scheduled crawlers either add overhead or fail to provide immediate partition recognition.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create a table with partitions and configure an AWS Glue crawler to run on a schedule to discover new partitions.
Why it's wrong here
Using a scheduled crawler can discover new partitions, but it introduces latency between partition creation and availability. It also adds cost and operational overhead for running the crawler. While it reduces manual effort compared to manual ALTER TABLE, it does not provide immediate recognition of new partitions and is less efficient than partition projection for a known, fixed scheme like year/month/day.
- ✗
Create a table with partitions defined manually and run ALTER TABLE ADD PARTITION for each new day.
Why it's wrong here
Manually adding partitions for each new day is operationally intensive and error-prone. It does not scale for daily log ingestion and can lead to missing partitions if automation fails. While it can reduce costs by limiting scanned data, the requirement to automatically recognize new partitions is not met, so this approach is not optimal.
- ✓
Create a table with partition projection enabled for year/month/day and set the storage location to the S3 prefix.
Why this is correct
Partition projection in Athena allows the table to automatically infer partitions based on a defined pattern, eliminating the need to manually add partitions or run crawlers. By specifying year/month/day projection, Athena can generate partition locations on the fly, reducing metadata overhead and ensuring new partitions are immediately queryable. This minimizes query costs by enabling partition pruning.
- ✗
Create a table without partitions and rely on Athena to scan the entire S3 prefix.
Why it's wrong here
A non-partitioned table forces Athena to scan all objects under the S3 prefix for every query, which increases both latency and cost. The logs are already organized by year/month/day, so ignoring partitions wastes the existing structure. This approach does not meet the goal of minimizing query costs and would perform poorly as data volume grows.
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 |
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.