The answer is to use Amazon Kinesis Data Firehose with an AWS Lambda function for transformation and output to Parquet. This approach is the most cost-effective and serverless because Kinesis Data Firehose natively supports converting incoming JSON records into Parquet format, and you can attach a lightweight Lambda function to handle simple field mappings and type conversions before the data lands in Amazon S3. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of choosing the right serverless data pipeline—many candidates mistakenly jump to AWS Glue for any transformation, but Glue is designed for heavier ETL jobs and incurs higher costs for simple tasks, while EC2 is not serverless and SageMaker is ML-focused. Remember the memory tip: “Firehose for flow, Lambda for light lift, Glue for heavy grind.”
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 needs to transform JSON data from Amazon Kinesis Data Streams into Parquet format and store it in Amazon S3. The transformation includes simple field mappings and type conversions. Which approach is most cost-effective and serverless?
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 Firehose with an AWS Lambda function for transformation and output to Parquet
Kinesis Data Firehose with built-in Lambda transformation can convert JSON to Parquet efficiently. Glue is heavier and more expensive for simple transforms; EC2 is not serverless; SageMaker is ML-focused.
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 Amazon EC2 instances running Apache Spark Streaming
Why it's wrong here
Not serverless; requires management.
✓
Use Amazon Kinesis Data Firehose with an AWS Lambda function for transformation and output to Parquet
Why this is correct
Firehose can invoke Lambda per record and convert to Parquet.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Amazon SageMaker for transformation
Why it's wrong here
SageMaker is for ML, not data transformation.
✗
Use an AWS Glue ETL job triggered by a Kinesis stream
Why it's wrong here
Glue jobs are billed per DPU and are better for complex transforms.
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.
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 Firehose with an AWS Lambda function for transformation and output to Parquet — Kinesis Data Firehose with built-in Lambda transformation can convert JSON to Parquet efficiently. Glue is heavier and more expensive for simple transforms; EC2 is not serverless; SageMaker is ML-focused.
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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