DEA-C01 Data Store Management Practice Question
A data engineer is optimizing an Amazon S3 data lake for cost and performance. The data lake contains large volumes of CSV files that are queried by Amazon Athena. The engineer wants to reduce query costs and improve query performance. Which TWO actions should the engineer take? (Choose two.)
⚠ Common exam trap
Test-takers frequently confuse S3 bucket-level features like Transfer Acceleration or access logging with query optimization, when the real gains come from data format and partitioning.
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 a frequently filtered column, such as date.
Converting CSV to Parquet and partitioning the data by a frequently filtered column are the two most effective actions to reduce Athena query costs and improve performance. Parquet reduces scanned data through columnar compression and predicate pushdown, while partitioning limits the data scanned to relevant partitions. The other actions do not affect query execution or data scanned.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Partition the data by a frequently filtered column, such as date.
Why this is correct
Partitioning by a commonly filtered column allows Athena to scan only the relevant partitions, reducing the amount of data read. This lowers query cost and improves performance by skipping irrelevant data. It is a best practice for large datasets in S3 when queries filter on that column. Combined with Parquet, it maximizes savings.
- ✗
Enable S3 server access logging on the bucket.
Why it's wrong here
Server access logging records requests made to the bucket for auditing purposes. It does not change how Athena reads data or reduce scanned bytes. Enabling it may add storage costs for logs. This action does not contribute to query cost reduction or performance improvement, and is not a data optimization technique.
- ✗
Enable S3 Transfer Acceleration on the bucket.
Why it's wrong here
S3 Transfer Acceleration speeds up uploads and downloads over long distances by using AWS edge locations. It does not affect Athena query performance or the amount of data scanned. It also adds cost for data transfer. This action is irrelevant to reducing query costs and improving query performance for Athena.
- ✓
Convert the CSV files to Apache Parquet format.
Why this is correct
Converting CSV to Parquet provides columnar storage and compression, which reduces the amount of data scanned by Athena. Athena charges based on data scanned, so this directly lowers query costs. Parquet also enables predicate pushdown and column pruning, improving performance. This is a fundamental optimization for Athena queries over large datasets.
- ✗
Increase the number of Athena workgroups.
Why it's wrong here
Athena workgroups are used to manage query access, costs, and limits for different teams. Creating more workgroups does not affect the underlying data format, partitioning, or query execution efficiency. It does not reduce the amount of data scanned or improve performance. This action is unrelated to the goal of optimizing query costs and performance.
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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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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