DEA-C01 Data Ingestion and Transformation Practice Question
A company ingests clickstream data into Amazon S3 via Kinesis Data Firehose. The data arrives in 20 MB files every 2 minutes. The data engineering team needs to transform nested JSON into a flat structure before loading into Amazon Redshift. Which approach is most cost-effective and scalable?
⚠ Common exam trap
The trap here is that candidates overestimate Lambda's suitability for file transformations, overlooking its payload and timeout constraints, while underestimating Glue's efficiency for small, frequent batch jobs compared to the overhead of a full EMR cluster.
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 an AWS Glue ETL job that runs on a schedule, using dynamic frames to flatten the data and write to S3 in Parquet
AWS Glue ETL jobs are designed for serverless, scalable data transformation, and using dynamic frames to flatten nested JSON and write to Parquet is both cost-effective (pay per DPU-hour) and scalable (automatically handles data volume). The 20 MB files arriving every 2 minutes are well-suited for Glue's batch processing, and Parquet output optimizes Redshift loading via COPY commands.
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 AWS Glue ETL job that runs on a schedule, using dynamic frames to flatten the data and write to S3 in Parquet
Why this is correct
Glue's dynamic frames natively handle nested JSON and can run cost-effectively on a schedule.
- ✗
Run an Amazon EMR cluster with Spark to flatten the data and write back to S3
Why it's wrong here
EMR adds complexity and cost for a simple flattening operation.
- ✗
Use AWS Lambda to transform each file as it arrives in S3
Why it's wrong here
Lambda has timeout and payload limits; 20 MB files may cause issues.
- ✗
Use Amazon Redshift Spectrum to query the nested JSON directly and create a view
Why it's wrong here
Spectrum queries raw data but does not flatten nested structures effectively.
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.