DEA-C01 Data Ingestion and Transformation Practice Question
A company ingests application logs into Amazon S3 through Amazon Kinesis Data Firehose. The logs arrive as newline-delimited JSON, and analysts query them with Amazon Athena. Query performance is poor because the JSON files are small and uncompressed. The engineer must improve Athena query performance while keeping the raw JSON available for a downstream legacy system. Which change should the engineer make?
⚠ Common exam trap
The trap here is assuming buffer tuning or query-side filters can substitute for changing the storage format, when only a columnar, compressed layout reduces the bytes Athena actually reads.
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
✓
Add a Firehose record format conversion to Apache Parquet and keep the raw JSON in a separate S3 prefix using a second delivery stream.
Athena performance improves when it scans less data, and converting the stream to Parquet with a record format conversion makes the stored objects columnar and compressed. A second delivery stream that leaves the raw JSON in place satisfies the legacy consumer, so the engineer gets both performance and compatibility.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable S3 Transfer Acceleration on the target bucket to speed up Athena scans.
Why it's wrong here
Transfer Acceleration speeds up uploads and downloads over the public internet to S3, but Athena reads data inside the AWS network and is not affected by it. Enabling it adds cost without changing how much data Athena must scan, so it cannot improve the query performance problem caused by uncompressed, non-columnar small files.
- ✗
Configure the Athena table with a SerDe for JSON and add a WHERE clause on the timestamp column in every query.
Why it's wrong here
A JSON SerDe is already implied by the current setup, and adding filters helps only queries that can use them. It does not change the underlying storage layout, so Athena still reads uncompressed JSON across all columns for the rows it must inspect, and it places the optimization burden on every analyst instead of fixing the pipeline.
- ✗
Increase the Firehose buffer size and interval so larger objects are delivered to S3.
Why it's wrong here
Larger objects reduce the small-file problem and can modestly help Athena, but the data stays uncompressed text JSON, so Athena still reads every column of every row. It addresses file count rather than columnar layout or compression, which are the changes that produce the large scan reduction the scenario needs, and it does not by itself meet the stated performance goal.
- ✓
Add a Firehose record format conversion to Apache Parquet and keep the raw JSON in a separate S3 prefix using a second delivery stream.
Why this is correct
Converting to Parquet with a Firehose record format conversion gives Athena a columnar, compressed format that scans far less data, directly improving query performance. Delivering the raw JSON to a separate prefix through a second stream preserves the legacy feed, satisfying both requirements without duplicating logic in the consumers.
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
Related to this question
About these practice questions
Courseiva writes every DEA-C01 question from scratch — 1,321 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
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.