DEA-C01 Data Operations and Support Practice Question
A data engineer is using Amazon Athena to query data stored in Amazon S3. The engineer wants to reduce query costs and improve performance for a table that is frequently queried with filters on a date column. The data is stored as uncompressed CSV files partitioned by year/month/day. Which action should the engineer take?
⚠ Common exam trap
The trap here is focusing on caching or infrastructure changes instead of the fundamental storage format and partitioning strategy that directly impact 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 use partitioning by date, then run MSCK REPAIR TABLE.
For Athena, the most effective way to reduce cost and improve performance is to use a columnar format like Parquet and partition the data. Parquet allows column pruning, and partitioning enables partition pruning, so queries scan less data. Updating partitions with MSCK REPAIR TABLE ensures the metadata is current, allowing these optimizations to take effect.
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 Parquet format and use partitioning by date, then run MSCK REPAIR TABLE.
Why this is correct
Converting to Parquet reduces the amount of data scanned because Athena reads only the necessary columns, and partitioning by date allows Athena to skip irrelevant partitions. Running MSCK REPAIR TABLE updates the partition metadata so queries can leverage the partitions, significantly lowering cost and improving performance.
- ✗
Use Amazon Redshift Spectrum to query the S3 data instead of Athena.
Why it's wrong here
Redshift Spectrum requires an Amazon Redshift cluster and does not inherently reduce costs compared to Athena. It may be suitable for complex joins, but the scenario focuses on Athena and S3. Switching to Spectrum adds complexity and cost without directly addressing the need for columnar storage and partitioning.
- ✗
Increase the number of S3 buckets storing the data to parallelize reads.
Why it's wrong here
Splitting data across multiple S3 buckets does not improve Athena query performance or reduce cost. Athena scans data based on table location and partitioning, not the number of buckets. This would complicate data management without addressing the core issue of inefficient columnar storage and partition pruning.
- ✗
Enable Athena query result reuse and set a short cache TTL.
Why it's wrong here
Query result reuse caches results for identical queries, but it does not help when filters change or new data arrives. It also does not reduce the cost of the initial query or improve performance for varied filters. The primary optimization is storage format and partitioning, not caching.
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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