DEA-C01 Data Ingestion and Transformation Practice Question
A data pipeline ingests streaming data from thousands of IoT devices into Kinesis Data Streams. The data must be transformed using a simple field mapping before being stored in S3. Which service should be used to perform the transformation with minimal operational overhead?
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
✓
Kinesis Data Firehose with a Lambda transformation
Kinesis Data Firehose can invoke a Lambda function to perform simple field mapping transformations before delivering data to S3, minimizing operational overhead. Option A is wrong because AWS Lambda invoked directly by the Kinesis stream requires custom logic for S3 delivery and stream management, increasing overhead. Option B is wrong because AWS Glue ETL jobs are designed for batch processing and are more complex to set up for streaming transformations. Option C is wrong because Kinesis Data Analytics is used for real-time analytics with SQL or Flink, not simple field mapping 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.
- ✗
AWS Lambda function invoked by the Kinesis stream
Why it's wrong here
Lambda's 15-minute maximum execution and per-invocation model suit event-driven enrichment, but the question asks for the service designed for the transformation with minimal operational overhead, which is a managed stream-processing service rather than function-by-function code you must write and maintain. Lambda is tempting because it is serverless and integrates natively with Kinesis as an event source.
- ✗
AWS Glue ETL job
Why it's wrong here
A Glue ETL job runs on Apache Spark and is oriented to batch and micro-batch processing with provisioned or billed DPUs, adding scheduling and cluster overhead for a simple field mapping on streaming records. It is tempting because Glue is the managed ETL service for S3 data, and it would be correct for large-scale batch transformations or crawler-driven catalog work.
- ✗
Kinesis Data Analytics
Why it's wrong here
Kinesis Data Analytics (managed Apache Flink) is built for continuous SQL or Flink queries over streams — windowed aggregations, joins, and time-series analytics — not one-to-one field mapping. It is tempting because it processes Kinesis streams natively, and it would be correct when the requirement is real-time aggregation or complex event processing rather than simple field renaming.
- ✓
Kinesis Data Firehose with a Lambda transformation
Why this is correct
Firehose performs the field mapping via an attached Lambda function and delivers to S3, so no clusters or application code need managing. This satisfies the minimal operational overhead constraint, unlike Kinesis Data Analytics or a custom consumer on EC2.
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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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.