DEA-C01 Data Store Management Practice Question
A company uses AWS Glue to catalog data stored in Amazon S3. The data is in Parquet format and partitioned by date. The company wants to improve query performance in Amazon Athena and reduce costs. Which THREE actions should the company take? (Choose THREE.)
⚠ Common exam trap
A common mix-up: candidates confuse data preparation tools (like Glue DataBrew) with query optimization techniques, or mistakenly think that converting to a non-columnar format like JSON improves schema evolution, when in fact columnar formats with compression and partitioning are the standard best practices for Athena performance and cost efficiency.
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
✓
Partition the data by date so Athena can use partition pruning.
Partitioning the data by date allows Athena to use partition pruning, which limits the amount of data scanned by only reading the partitions that match the query's WHERE clause. This directly reduces both query cost (since Athena charges per byte scanned) and query latency, especially for date-range queries on large datasets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Convert the data to JSON format for better schema evolution.
Why it's wrong here
JSON increases scan size and reduces performance.
- ✗
Use Glue DataBrew to clean the data before querying.
Why it's wrong here
DataBrew is for data preparation, not performance optimization.
- ✓
Partition the data by date so Athena can use partition pruning.
Why this is correct
Partition pruning limits the amount of data scanned per query.
- ✓
Ensure the data is in a columnar format like Parquet or ORC.
Why this is correct
Columnar formats improve query performance and reduce data scanned.
- ✓
Compress the data using a codec like Snappy or Gzip.
Why this is correct
Compression reduces storage cost and I/O.
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.