DEA-C01 Data Ingestion and Transformation Practice Question
A company ingests IoT data into an S3 bucket using AWS IoT Core rules. The data is in JSON format, and each record is about 500 bytes. The data volume is 5 GB per day. The company wants to convert the data to Parquet format and partition it by year/month/day. Which TWO AWS services can be used together to achieve this with minimal operational overhead?
⚠ Common exam trap
Candidates often choose Amazon Kinesis Data Firehose (Option E) thinking it's the simplest for Parquet conversion, but it is designed for streaming data, not for batch processing of S3-uploaded files, and it lacks native S3 event-driven partitioning for historical data.
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
✓
AWS Glue ETL job triggered by S3 event
(AWS Glue ETL job triggered by S3 event) is correct because it provides a serverless, fully managed ETL solution that can convert JSON to Parquet and partition by year/month/day with minimal operational overhead. The S3 event trigger automatically invokes the Glue job when new data arrives, eliminating the need for manual scheduling or infrastructure management.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon Athena CTAS query
Why it's wrong here
Athena is for querying, not for continuous transformation of incoming data.
- ✓
AWS Glue ETL job triggered by S3 event
Why this is correct
Glue can be triggered by S3 events (via Lambda or EventBridge) and perform the conversion and partitioning.
- ✓
AWS Lambda function triggered by S3 event
Why this is correct
Lambda can perform the conversion and write to partitioned S3 paths.
- ✗
Amazon EMR with Spark job
Why it's wrong here
EMR involves cluster management, increasing operational overhead.
- ✗
Amazon Kinesis Data Firehose with Parquet conversion
Why it's wrong here
Firehose can convert to Parquet but does not support custom partitioning like year/month/day without additional Lambda.
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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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.