- A
Use AWS Glue to run a streaming ETL job.
Why wrong: Glue streaming is relatively new and may not be as mature for clickstream enrichment.
- B
Use Amazon EMR to consume the stream using Spark Streaming.
Why wrong: EMR is more complex and overkill for simple enrichment.
- C
Use AWS Lambda to process each record from the stream and write to S3.
Why wrong: Lambda is suitable for moderate throughput but may not be as efficient as Kinesis Data Analytics for continuous streaming.
- D
Use Amazon Kinesis Data Analytics to transform the stream and output to Amazon Kinesis Data Firehose, which writes to S3.
Kinesis Data Analytics can run SQL on the stream, and Firehose delivers to S3 in batches.
Question 834 of 1,786
Best Architecture for Real-Time Stream Transformation with Kinesis Data Analytics and Firehose
This DEA-C01 practice question tests your understanding of data ingestion and transformation. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Option D is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
EMR is more complex and overkill for simple enrichment.
- ✗
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
Kinesis Data Analytics uses Apache Flink under the hood, allowing stateful stream processing with exactly-once semantics, which is critical for accurate enrichment like geolocation lookups. The output to Firehose enables automatic data buffering (up to 128 MB or 900 seconds) and compression (e.g., GZIP) before S3 delivery, reducing storage costs and downstream processing overhead. In a real-world scenario, this architecture handles spikes in clickstream traffic without provisioning servers, as KDA scales shards automatically based on the incoming data rate.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 |
What to study next
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FAQ
Questions learners often ask
What does this DEA-C01 question test?
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Amazon Kinesis Data Analytics to transform the stream and output to Amazon Kinesis Data Firehose, which writes to S3. — Option D is correct because 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.
What should I do if I get this DEA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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