- A
Deliver the enriched data to Amazon Kinesis Data Firehose and enable Parquet conversion.
Kinesis Data Firehose can convert incoming data to Parquet and write to S3.
- B
Configure an AWS Lambda function to read from the stream, enrich, and write to S3.
Why wrong: Lambda has concurrency limits and may be more expensive per record for high throughput.
- C
Use AWS Glue streaming ETL to enrich and convert data to Parquet.
Why wrong: Glue streaming ETL adds latency and cost compared to native Kinesis integrations.
- D
Use Amazon EMR with Spark Streaming to process and store the data.
Why wrong: EMR adds operational overhead and is not as cost-effective for this simple enrichment.
- E
Perform a DynamoDB lookup in the Flink application for each record.
Flink applications can query DynamoDB for enrichment in real time.
- F
Use Amazon Kinesis Data Analytics for Apache Flink to enrich the stream with data from DynamoDB.
Kinesis Data Analytics for Flink can perform real-time enrichment with low latency.
Quick Answer
The answer is to use Amazon Kinesis Data Analytics for Apache Flink for enrichment, Kinesis Data Firehose for delivery, and again Kinesis Data Analytics for Flink to perform the DynamoDB lookups. This combination achieves stream enrichment with DynamoDB and delivery to S3 Parquet using Kinesis by leveraging Flink’s low-latency, stateful processing to query device metadata on the fly, while Firehose handles the buffering and automatic conversion to Parquet format, minimizing both latency and cost. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of the optimal streaming pipeline: Flink for real-time enrichment (not Lambda, which has invocation limits and higher per-record cost) and Firehose for efficient batch conversion to columnar storage. A common trap is choosing AWS Glue or Lambda for streaming, but those add latency or cost at scale. Memory tip: “Flink for the link, Firehose for the rows” — Flink connects the stream to DynamoDB, Firehose rows the data into Parquet.
DEA-C01 Data Ingestion and Transformation Practice Question
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 ingests IoT sensor data into Amazon Kinesis Data Streams. The data must be enriched with device metadata from Amazon DynamoDB and then stored in Amazon S3 in Apache Parquet format. The solution must minimize latency and cost. Which THREE steps should a data engineer implement? (Choose three.)
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
Deliver the enriched data to Amazon Kinesis Data Firehose and enable Parquet conversion.
Option A: Using Kinesis Data Analytics for Flink allows stream enrichment with low latency. Option D: Kinesis Data Firehose can buffer data and convert it to Parquet before delivering to S3. Option E: Kinesis Data Analytics for Flink can look up DynamoDB data for enrichment. Option B (Lambda) has invocation limits and higher cost per record. Option C (Glue) adds latency and cost for streaming. Option F (EMR) is managed and not as seamless.
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.
- ✓
Deliver the enriched data to Amazon Kinesis Data Firehose and enable Parquet conversion.
Why this is correct
Kinesis Data Firehose can convert incoming data to Parquet and write to S3.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Configure an AWS Lambda function to read from the stream, enrich, and write to S3.
Why it's wrong here
Lambda has concurrency limits and may be more expensive per record for high throughput.
- ✗
Use AWS Glue streaming ETL to enrich and convert data to Parquet.
Why it's wrong here
Glue streaming ETL adds latency and cost compared to native Kinesis integrations.
- ✗
Use Amazon EMR with Spark Streaming to process and store the data.
Why it's wrong here
EMR adds operational overhead and is not as cost-effective for this simple enrichment.
- ✓
Perform a DynamoDB lookup in the Flink application for each record.
Why this is correct
Flink applications can query DynamoDB for enrichment in real time.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use Amazon Kinesis Data Analytics for Apache Flink to enrich the stream with data from DynamoDB.
Why this is correct
Kinesis Data Analytics for Flink can perform real-time enrichment with low latency.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which DEA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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Data Ingestion and Transformation — study guide chapter
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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: Deliver the enriched data to Amazon Kinesis Data Firehose and enable Parquet conversion. — Option A: Using Kinesis Data Analytics for Flink allows stream enrichment with low latency. Option D: Kinesis Data Firehose can buffer data and convert it to Parquet before delivering to S3. Option E: Kinesis Data Analytics for Flink can look up DynamoDB data for enrichment. Option B (Lambda) has invocation limits and higher cost per record. Option C (Glue) adds latency and cost for streaming. Option F (EMR) is managed and not as seamless.
What should I do if I get this DEA-C01 question wrong?
Identify which DEA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 2026
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.
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