DEA-C01 Data Store Management Practice Question
A data engineer is building a data lake on Amazon S3. The engineer needs to store structured data that will be queried by Amazon Athena. The data is currently in CSV format and is partitioned by date. The engineer wants to improve query performance and reduce the amount of data scanned. Which action should the engineer take?
⚠ Common exam trap
The trap here is assuming that compression alone will provide the same performance benefits as converting to a columnar format like Parquet.
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 Apache Parquet format and use AWS Glue to update the table metadata in the AWS Glue Data Catalog.
Converting data to a columnar format like Parquet significantly reduces the amount of data scanned by Athena because only the required columns are read. Updating the AWS Glue Data Catalog ensures the table metadata reflects the new format. This combination improves query performance and lowers cost.
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 CSV data directly from S3.
Why it's wrong here
Redshift Spectrum allows querying S3 data from Redshift, but the scenario specifies Athena. Introducing Redshift Spectrum would add complexity and cost. It does not improve Athena query performance for CSV data and does not address the file format inefficiency.
- ✗
Compress the CSV files using gzip and update the table metadata in the AWS Glue Data Catalog.
Why it's wrong here
Compressing CSV files reduces storage and can lower data scanned, but CSV is row-based and not columnar. Athena still reads entire rows, so performance improvement is limited compared to Parquet. Compression alone does not provide the same efficiency as columnar storage.
- ✓
Convert the data to Apache Parquet format and use AWS Glue to update the table metadata in the AWS Glue Data Catalog.
Why this is correct
Parquet is a columnar format that enables Athena to read only the columns needed, reducing data scanned and improving performance. Updating the AWS Glue Data Catalog ensures Athena can query the new format. This is a best practice for optimizing Athena queries on S3 data lakes.
- ✗
Increase the number of partitions by adding a partition for each hour of the day.
Why it's wrong here
More granular partitions can reduce data scanned if queries filter on the partition key. However, over-partitioning can lead to many small files, increasing overhead and slowing queries. Without changing the file format, the benefit is limited and may cause performance degradation.
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
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.