DEA-C01 Data Ingestion and Transformation Practice Question
A media company is building a data pipeline to ingest user activity logs from multiple sources into Amazon S3. The logs are JSON files generated every minute. The company wants to use Amazon Athena to query the logs with minimal latency and cost. The current approach is to use AWS Kinesis Data Firehose to deliver the logs to S3 with a prefix like 'logs/2024/01/01/00/file.json'. However, when running Athena queries, the team notices high query costs because Athena scans all files in the 'logs/' prefix even when querying for a specific date. What should the team do to reduce the amount of data scanned by Athena?
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
✓
Create a Hive-style partition structure in S3 with keys like 'year=2024/month=01/day=01/hour=00/' and update the Glue Data Catalog accordingly.
Creating a Hive-style partition structure (e.g., year=2024/month=01/day=01/) enables Athena to perform partition pruning. When querying for a specific date, Athena scans only the relevant partition, reducing data scanned and cost. Option A is incorrect because Athena views do not reduce data scanning; they just store query logic. Option B is incorrect because more granular prefixes without a partition structure (like Hive-style) do not enable partition pruning in Athena; Athena treats the prefix as a folder and still scans all files. Option C is incorrect because while converting to Parquet reduces scan size due to columnar storage and compression, it does not address the lack of partitioning; the main issue is full scan of all files regardless of format.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create an Athena view that filters by date.
Why it's wrong here
Views do not reduce data scanned; they are just saved queries.
- ✗
Increase the number of partitions by using a more granular prefix like 'logs/2024/01/01/00/00/'.
Why it's wrong here
Granular prefixes do not create Hive-style partitions without proper table definition.
- ✗
Convert the JSON files to Apache Parquet format using AWS Glue ETL jobs.
Why it's wrong here
Parquet reduces data size but still requires scanning all partitions unless partitioned.
- ✓
Create a Hive-style partition structure in S3 with keys like 'year=2024/month=01/day=01/hour=00/' and update the Glue Data Catalog accordingly.
Why this is correct
Partition pruning allows Athena to scan only relevant directories, reducing costs.
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.