DEA-C01 Data Ingestion and Transformation Practice Question
A company uses Amazon Kinesis Data Streams to ingest clickstream data from a website. The data must be transformed (e.g., enrich with user location) before being stored in Amazon S3. Which architecture is MOST efficient for this transformation?
⚠ Common exam trap
Test-takers frequently choose AWS Lambda (Option C) because it seems serverless and simple, but they overlook Lambda's lack of native batching to S3 and its 15-minute timeout, which makes it inefficient for continuous, high-volume streaming transformations compared to KDA + Firehose.
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 Analytics to transform the stream and output to Amazon Kinesis Data Firehose, which writes to S3.
Amazon Kinesis Data Analytics (KDA) can perform real-time transformations (e.g., enriching clickstream data with user location via SQL or Flink) on the stream, then output the transformed data to Kinesis Data Firehose, which can batch and compress records before writing to S3. This architecture minimizes operational overhead and is purpose-built for streaming transformations, avoiding the latency and complexity of Lambda cold starts or the provisioning overhead of Glue/EMR.
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 AWS Glue to run a streaming ETL job.
Why it's wrong here
Glue streaming is relatively new and may not be as mature for clickstream enrichment.
- ✗
Use Amazon EMR to consume the stream using Spark Streaming.
Why it's wrong here
Amazon EMR with Spark Streaming introduces significant operational overhead for a continuous, low-latency enrichment task that Kinesis Data Firehose can handle natively with Lambda. It is tempting because Spark Streaming excels at complex, stateful transformations across large-scale batch micro-batches, which would be correct if the scenario required advanced analytics or machine learning on the stream rather than simple per-record enrichment before S3 delivery.
- ✗
Use AWS Lambda to process each record from the stream and write to S3.
Why it's wrong here
Lambda is suitable for moderate throughput but may not be as efficient as Kinesis Data Analytics for continuous streaming.
- ✓
Use Amazon Kinesis Data Analytics to transform the stream and output to Amazon Kinesis Data Firehose, which writes to S3.
Why this is correct
Kinesis Data Analytics can run SQL on the stream, and Firehose delivers to S3 in batches.
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
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.