- A
Amazon Kinesis Data Firehose
Why wrong: Cannot directly enrich from DynamoDB.
- B
Amazon Kinesis Data Analytics for Apache Flink
Performs stream enrichment with DynamoDB lookups.
- C
Amazon Kinesis Data Streams
Ingests high-throughput clickstream data.
- D
AWS Lambda with DynamoDB Accelerator (DAX)
Why wrong: Lambda can enrich but DAX is not needed; Lambda concurrency may be a bottleneck.
- E
AWS Glue Streaming ETL
Why wrong: Glue streaming is more suited for batch-oriented transformations.
Question 1,192 of 1,786
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 data engineer is designing a near-real-time streaming pipeline to ingest clickstream data from a web application. The data must be enriched with user metadata from a DynamoDB table before being stored in S3. Which combination of AWS services should the engineer use? (Choose TWO.)
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
✓
Amazon Kinesis Data Analytics for Apache Flink
Amazon Kinesis Data Streams (C) provides the low-latency, durable ingestion layer for the clickstream data, while Amazon Kinesis Data Analytics for Apache Flink (B) allows you to run a Flink application that can perform stream-to-stream joins with the DynamoDB user metadata in near-real time. The enriched output can then be written to S3 via a Kinesis Data Firehose delivery stream or directly from the Flink application.
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.
- ✗
Amazon Kinesis Data Firehose
Why it's wrong here
Cannot directly enrich from DynamoDB.
- ✓
Amazon Kinesis Data Analytics for Apache Flink
Why this is correct
Performs stream enrichment with DynamoDB lookups.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Amazon Kinesis Data Streams
Why this is correct
Ingests high-throughput clickstream data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
AWS Lambda with DynamoDB Accelerator (DAX)
Why it's wrong here
Lambda can enrich but DAX is not needed; Lambda concurrency may be a bottleneck.
- ✗
AWS Glue Streaming ETL
Why it's wrong here
Glue streaming is more suited for batch-oriented transformations.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Kinesis Data Firehose's ability to invoke a Lambda for simple transformations with the need for stateful stream enrichment, leading them to select Firehose alone without a stream processing engine.
Detailed technical explanation
How to think about this question
Kinesis Data Analytics for Apache Flink uses the Flink DataStream API, which supports stateful processing and exactly-once semantics. The enrichment pattern typically involves using an AsyncIO function to query DynamoDB without blocking the stream, or using a Flink MapFunction with a cache to reduce read pressure. In production, engineers often configure a side input or broadcast state to periodically refresh the user metadata, balancing freshness with throughput.
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
Got this wrong? Here's your next step.
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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: Amazon Kinesis Data Analytics for Apache Flink — Amazon Kinesis Data Streams (C) provides the low-latency, durable ingestion layer for the clickstream data, while Amazon Kinesis Data Analytics for Apache Flink (B) allows you to run a Flink application that can perform stream-to-stream joins with the DynamoDB user metadata in near-real time. The enriched output can then be written to S3 via a Kinesis Data Firehose delivery stream or directly from the Flink application.
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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