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 conversion reduces scanned bytes but leaves millions of tiny objects, so Athena still incurs per-file listing, open and planning overhead — the actual bottleneck. Columnar conversion suits wide tables where scan volume dominates, not file-count-driven timeouts.
- ✓
Run a compaction job to consolidate small files into fewer larger files (e.g., 128 MB each).
Why this is correct
Millions of sub-1 MB files force Athena to open each object individually, so per-file overhead dominates and queries time out. Compacting into roughly 128 MB objects cuts the number of GET requests and partition listings, letting Athena scan far fewer, larger files.
- ✗
Add more partitions by including hour and minute as partition keys.
Why it's wrong here
Adding hour and minute partitions multiplies partition metadata and small-file overhead, worsening the millions-of-small-files problem rather than reducing bytes scanned. It would help when queries filter on those finer time grains. Compacting the small CSV files into larger objects is the effective fix.
- ✗
Use S3 Select to push down filtering to S3 before Athena processes the data.
Why it's wrong here
S3 Select filters rows within a single object during retrieval; it cannot consolidate millions of sub-1 MB files, so Athena still pays per-object request overhead and planning latency. It suits selectively reading columns from a few large CSV or JSON objects, not reducing file count.
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.