The answer is Amazon Kinesis Data Analytics for Apache Flink, as it is purpose-built for real-time transformation to Parquet with Kinesis Data Analytics for Flink, offering a serverless runtime that natively handles complex stream processing logic. This service allows you to write Apache Flink applications that continuously read from Kinesis Data Streams, apply transformations, and write the results directly to Amazon S3 in Parquet format, all with exactly-once semantics and low latency. On the AWS Certified Data Engineer Associate DEA-C01 exam, this question tests your ability to distinguish between real-time stream processing and simpler data-loading services; a common trap is choosing Kinesis Data Firehose, which can convert to Parquet but lacks native Flink’s stateful transformation capabilities. Another pitfall is Lambda, which is limited by a 15-minute timeout and is not designed for sustained streaming workloads. Remember the mnemonic “Flink for Flowing” — when you need continuous, complex transformations on streaming data, think Apache Flink on Kinesis Data Analytics.
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 company is streaming data from an application to Amazon Kinesis Data Streams. The data must be transformed in real time and then stored in Amazon S3 in Parquet format. Which AWS service should be used for the transformation step?
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.
Option D is correct because Amazon Kinesis Data Analytics for Apache Flink is a serverless service that can run Apache Flink applications to perform real-time transformations on streaming data. Option A (AWS Lambda) can be used but is limited by execution time and is not optimized for complex streaming transformations. Option B (Amazon Kinesis Data Firehose) can transform data using Lambda functions but is more suited for loading data. Option C (Amazon EMR) is for batch processing, not real-time streaming.
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 with a Lambda transformation.
Why it's wrong here
Firehose can transform data using Lambda, but it is primarily a delivery service and may not support all transformation scenarios.
✗
Amazon EMR running Apache Spark Streaming.
Why it's wrong here
EMR requires cluster management and is more suited for large-scale batch processing.
✓
Amazon Kinesis Data Analytics for Apache Flink.
Why this is correct
Kinesis Data Analytics for Flink is designed for real-time stream processing and can perform complex transformations.
Related concept
Read the scenario before looking for a memorised answer.
✗
AWS Lambda with a Kinesis trigger.
Why it's wrong here
Lambda can process records but may not be suitable for complex real-time transformations with stateful operations.
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.
Trap categories for this question
Scenario analysis trap
Firehose can transform data using Lambda, but it is primarily a delivery service and may not support all transformation scenarios.
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 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.
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.
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. — Option D is correct because Amazon Kinesis Data Analytics for Apache Flink is a serverless service that can run Apache Flink applications to perform real-time transformations on streaming data. Option A (AWS Lambda) can be used but is limited by execution time and is not optimized for complex streaming transformations. Option B (Amazon Kinesis Data Firehose) can transform data using Lambda functions but is more suited for loading data. Option C (Amazon EMR) is for batch processing, not real-time streaming.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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