DEA-C01 Data Ingestion and Transformation Practice Question
A company uses Amazon Kinesis Data Firehose to deliver data to an Amazon S3 bucket. The data is in JSON format and contains a 'timestamp' field with a Unix epoch value. The company wants to partition the S3 objects by year, month, day, and hour based on the timestamp. What is the MOST efficient method to achieve this?
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 the dynamic partitioning feature of Kinesis Data Firehose with inline parsing to extract the timestamp and create the S3 prefix.
Kinesis Data Firehose supports dynamic partitioning with inline parsing to extract the timestamp and create the S3 prefix by year, month, day, and hour. Option B is incorrect because a custom prefix based on current time would use the delivery time, not the event timestamp, so partitioning would not reflect the actual data timestamps. Option C is incorrect because using an AWS Glue ETL job introduces additional latency and complexity; Firehose can partition directly without needing an extra service. Option D is incorrect because Amazon Athena is a query engine, not an ingestion tool; running a CTAS query would require the data to already be in S3 and adds 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.
- ✓
Use the dynamic partitioning feature of Kinesis Data Firehose with inline parsing to extract the timestamp and create the S3 prefix.
Why this is correct
Correct. Kinesis Data Firehose dynamic partitioning allows inline parsing to extract the timestamp from JSON data and automatically creates S3 prefixes based on the specified keys (year, month, day, hour).
- ✗
Configure a custom S3 prefix in Firehose using the 'YYYY/MM/dd/HH' format based on the current time.
Why it's wrong here
Incorrect. A custom S3 prefix with 'YYYY/MM/dd/HH' would use the current time at delivery, not the actual timestamp field in the data, so partitioning would not match the event time.
- ✗
Use an AWS Glue ETL job to read from Firehose, partition, and write to S3.
Why it's wrong here
Incorrect. While AWS Glue ETL could partition data, it adds latency and complexity because data must be written to S3 first and then processed; Firehose can partition in real-time during delivery.
- ✗
Use Amazon Athena to run a CTAS query that partitions the data by timestamp.
Why it's wrong here
Incorrect. Amazon Athena is for querying data already in S3; using a CTAS query would require the data to be written first and then partitioned, adding overhead and latency.
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.