DEA-C01 Data Operations and Support Practice Question
A data engineer is using Amazon Athena to query data stored in Amazon S3. The engineer notices that queries are slow and scan large amounts of data. The data is stored in CSV format without compression. Which action should the engineer take to improve query performance and reduce cost?
⚠ Common exam trap
The trap here is focusing on partitioning alone while ignoring the larger gains from columnar storage and compression, which directly reduce data scanned.
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
✓
Convert the data to Parquet format and compress it with Snappy.
Athena queries are optimized by using columnar formats like Parquet, which allow column pruning, and compression, which reduces data scanned. Converting from CSV to Parquet with Snappy compression will dramatically reduce the amount of data read, improving performance and lowering cost. Partitioning can further help but is secondary to format and compression.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Amazon Redshift Spectrum to query the S3 data instead of Athena.
Why it's wrong here
Redshift Spectrum allows querying S3 data from Redshift, but it is not a direct replacement for Athena and requires a Redshift cluster. The scenario is about improving Athena queries, so switching to another service is not the appropriate action. Additionally, Spectrum performance depends on file formats and partitioning, similar to Athena.
- ✓
Convert the data to Parquet format and compress it with Snappy.
Why this is correct
Converting data to a columnar format like Parquet allows Athena to read only the columns needed for a query, reducing data scanned. Compressing with Snappy further reduces storage size and I/O. This combination significantly improves query performance and lowers cost because Athena charges based on data scanned.
- ✗
Increase the number of Athena query executions by using a larger workgroup.
Why it's wrong here
Athena workgroups are used to manage query access, cost controls, and limits. Increasing the number of query executions or using a larger workgroup does not change how data is stored or scanned. It may allow more concurrent queries but does not improve individual query performance or reduce data scanned.
- ✗
Partition the data by date and store it in CSV format.
Why it's wrong here
Partitioning can reduce data scanned by limiting queries to specific partitions, but it does not address the inefficiency of row-based CSV format. CSV still requires reading all columns and is not compressed, so performance gains are limited compared to using a columnar format. Partitioning alone is not the most effective action here.
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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