DEA-C01 Data Operations and Support Practice Question
A data analyst needs to query a large Amazon S3 bucket containing CSV files using Amazon Athena. The bucket has millions of small files (less than 1 MB each). The analyst reports that queries are very slow and often time out. The data is partitioned by date and the partition columns are defined in the table. What is the most effective way to improve query performance?
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
✓
Run a compaction job to consolidate small files into fewer larger files (e.g., 128 MB each).
Many small files (under 1 MB) cause high overhead because each file requires a separate read operation and metadata call. Consolidating them into fewer larger files (e.g., 128 MB each) reduces the number of read operations and improves I/O efficiency, directly addressing the root cause of slowdowns and timeouts. Option A (converting to Parquet) improves storage efficiency and query performance but does not reduce the file count; it is a beneficial addition but not the most effective standalone fix for the small file problem. Option C (adding more partitions) would increase overhead by creating even more directories/files to scan. Option D (S3 Select) applies within individual files and does not mitigate overhead from file quantity.
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 files to Apache Parquet format using an AWS Glue ETL job.
Why it's wrong here
Parquet is columnar and improves performance, but small files still cause overhead; this alone may not solve the issue.
- ✓
Run a compaction job to consolidate small files into fewer larger files (e.g., 128 MB each).
Why this is correct
Consolidating small files reduces the overhead of listing and reading many objects, significantly improving Athena performance.
- ✗
Add more partitions by including hour and minute as partition keys.
Why it's wrong here
More partitions increase the number of files to list and read, worsening performance.
- ✗
Use S3 Select to push down filtering to S3 before Athena processes the data.
Why it's wrong here
S3 Select works on individual objects and is not designed for querying many objects simultaneously.
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.