DEA-C01 Data Ingestion and Transformation Practice Question
A company is using Amazon Kinesis Data Firehose to ingest log data from web servers into an Amazon S3 bucket. The data is then queried by Amazon Athena. The company has noticed that the Athena queries are slow and expensive. The data engineer wants to optimize the storage format to improve query performance and reduce costs. Which configuration change should the data engineer make to the Firehose delivery stream?
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
✓
Enable data transformation in Firehose to convert JSON to Parquet format with Snappy compression.
Enable data transformation in Firehose to convert JSON to Parquet format with Snappy compression. Parquet is a columnar storage format that significantly improves query performance in Athena by reducing the amount of data scanned per query. Snappy compression provides efficient compression and decompression, reducing storage costs and improving I/O. Option A is incorrect because increasing buffer interval and size simply creates larger files but does not change the storage format; the data remains in its original format (likely JSON or CSV), which is less efficient for columnar querying. Option B is incorrect because while ORC is also a columnar format, Parquet is more commonly used with Athena and offers better integration; additionally, GZIP compression is not as efficient as Snappy for Parquet files. Option C is incorrect because enabling S3 server access logs would track requests to the S3 bucket but does not optimize the data format or improve query performance; it adds additional cost and storage overhead.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the buffer interval to 600 seconds and buffer size to 128 MB to create larger files.
Why it's wrong here
Larger files improve performance but format matters more.
- ✗
Change the output format to ORC and enable GZIP compression.
Why it's wrong here
ORC is valid but Parquet is better for Athena.
- ✗
Enable S3 server access logs to track query patterns.
Why it's wrong here
This does not optimize query performance.
- ✓
Enable data transformation in Firehose to convert JSON to Parquet format with Snappy compression.
Why this is correct
Parquet is columnar and efficient for Athena.
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
One of 1,711 original DEA-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
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.