DEA-C01 Data Ingestion and Transformation Practice Question
A company needs to transform JSON data from Amazon Kinesis Data Streams into Parquet format and store it in Amazon S3. The transformation includes simple field mappings and type conversions. Which approach is most cost-effective and serverless?
⚠ Common exam trap
The trap here is that candidates often overestimate the need for full ETL engines like Glue or Spark for simple transformations, overlooking that Kinesis Data Firehose with Lambda is the most cost-effective and serverless option for lightweight field mappings and format conversions.
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 Amazon Kinesis Data Firehose with an AWS Lambda function for transformation and output to Parquet
Amazon Kinesis Data Firehose can directly deliver streaming data to Amazon S3 in Parquet format. By attaching an AWS Lambda function for simple field mappings and type conversions, you achieve a fully serverless, cost-effective solution without managing any infrastructure. This approach minimizes costs because you pay only for the data volume processed by Firehose and the Lambda invocations, avoiding the overhead of always-on compute resources.
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 EC2 instances running Apache Spark Streaming
Why it's wrong here
Not serverless; requires management.
- ✓
Use Amazon Kinesis Data Firehose with an AWS Lambda function for transformation and output to Parquet
Why this is correct
Firehose can invoke Lambda per record and convert to Parquet.
- ✗
Use Amazon SageMaker for transformation
Why it's wrong here
SageMaker is for ML, not data transformation.
- ✗
Use an AWS Glue ETL job triggered by a Kinesis stream
Why it's wrong here
Glue jobs are billed per DPU and are better for complex transforms.
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.