DEA-C01 Data Store Management Practice Question
A company uses Amazon S3 to store historical stock market data as CSV files. They run daily Amazon Athena queries to generate reports. Recently, the finance team reported that queries are timing out and costs have increased significantly. The data engineering team notices that the S3 bucket contains thousands of small files (average 100 KB) due to a misconfigured ingestion pipeline. They need to improve query performance and reduce costs without changing the existing reporting schedule. The team has access to AWS Glue and can create new tables. Which solution should they implement?
⚠ Common exam trap
A common mix-up: candidates assume partitioning (Option A) is a universal performance fix, but they overlook that partitioning does not address the 'small files problem' which is a distinct performance killer in Athena due to S3 request overhead and file open costs.
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
✓
Use AWS Glue ETL to read the CSV files, convert them to Parquet, and write them back to S3 in fewer, larger files.
Converting the thousands of small CSV files into fewer, larger Parquet files using AWS Glue ETL directly addresses the root cause of poor Athena performance and high costs. Parquet is a columnar format that reduces the amount of data scanned per query, and larger files minimize the overhead of S3 LIST and GET operations, improving throughput. This solution does not change the reporting schedule and leverages existing Glue capabilities to create new optimized tables.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Partition the data by date and create a new Athena table with partitions.
Why it's wrong here
Partitioning helps but does not consolidate small files.
- ✗
Use S3 Select to filter rows within each file before Athena processes them.
Why it's wrong here
S3 Select is not integrated with Athena in this way.
- ✗
Increase the Athena query timeout to 30 minutes.
Why it's wrong here
Does not address root cause; still scans many small files.
- ✓
Use AWS Glue ETL to read the CSV files, convert them to Parquet, and write them back to S3 in fewer, larger files.
Why this is correct
Consolidates small files and uses columnar format to reduce scan size.
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.