DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer needs to transform CSV files arriving in an S3 bucket into Parquet format and store them in another S3 bucket. The transformation is simple and on-demand, triggered by data arrival. Which solution is the MOST cost-effective and requires the least operational overhead?
⚠ Common exam trap
The DEA-C01 exam often tests the misconception that AWS Glue is always the best choice for ETL, but for simple, event-driven transformations with minimal overhead, Lambda is more cost-effective and operationally simpler than Glue's managed Spark environment.
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 S3 Events to trigger an AWS Lambda function that transforms the data
Using S3 Events to trigger an AWS Lambda function is the most cost-effective and operationally lightweight solution for simple, on-demand CSV-to-Parquet transformations. Lambda scales automatically with each S3 PUT event, incurs no idle cost, and requires no cluster management, making it ideal for event-driven, low-volume transformations.
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 Amazon EMR with Spark streaming
Why it's wrong here
EMR requires cluster management and is overkill.
- ✗
Use Amazon Athena to create a new table with Parquet format
Why it's wrong here
Athena is for querying, not for creating transformed files.
- ✗
Use AWS Glue ETL jobs scheduled to run every hour
Why it's wrong here
Glue jobs have startup time and are not event-driven.
- ✓
Use S3 Events to trigger an AWS Lambda function that transforms the data
Why this is correct
Lambda is event-driven, cost-effective, and serverless.
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 |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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