The correct approach is to configure Kinesis Data Firehose to invoke a Lambda function for data transformation. This works because Firehose natively supports a Lambda transformation step that processes incoming records in batches before delivery to S3, allowing you to convert JSON to Parquet inline using libraries like PyArrow without needing intermediate storage or a separate pipeline. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of Firehose’s built-in transformation capabilities versus external processing—a common trap is assuming you must use AWS Glue or an EMR cluster for Parquet conversion, but Firehose’s Lambda integration is the simplest and most cost-effective answer for real-time streaming. Remember the memory tip: “Firehose fires Lambda, Parquet lands in S3.”
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 uses Amazon Kinesis Data Firehose to deliver streaming data to an S3 bucket. The data must be transformed from JSON to Parquet format before delivery. Which approach should be used?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Configure Kinesis Data Firehose to invoke a Lambda function for data transformation.
Kinesis Data Firehose can invoke a Lambda function as a transformation step before data is delivered to S3. This allows you to convert JSON records to Parquet format inline, without needing an intermediate storage or separate processing pipeline. The Lambda function receives batches of records, transforms them (e.g., using PyArrow or similar libraries), and returns them to Firehose for delivery.
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.
✓
Configure Kinesis Data Firehose to invoke a Lambda function for data transformation.
Why this is correct
Firehose can call a Lambda function to transform records, including converting JSON to Parquet.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use an AWS Glue ETL job to read from S3 and write Parquet back to S3.
Why it's wrong here
This works but adds latency; Firehose can do it in-stream with Lambda.
✗
Use Amazon EMR to process the data and output Parquet.
Why it's wrong here
EMR is for large-scale batch processing, not real-time streaming.
✗
Use Kinesis Data Analytics to convert the data to Parquet.
Why it's wrong here
Kinesis Data Analytics processes streaming data but is not the primary tool for format conversion in Firehose.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think they need a separate ETL service like Glue or EMR for format conversion, but Firehose's built-in Lambda integration is the simplest and most cost-effective way to transform data in-flight before delivery.
Detailed technical explanation
How to think about this question
Under the hood, Kinesis Data Firehose invokes the Lambda function synchronously with a batch of records (up to 3 MB or 5 minutes of buffering). The Lambda function must return the transformed records in the same order and with the same record ID, and the output must be in the desired format (Parquet) as a base64-encoded blob. Firehose then writes the transformed data to S3 in the configured buffer interval or size, optionally partitioning by date/time. A common real-world scenario is streaming IoT sensor data as JSON and converting to Parquet for efficient querying with Amazon Athena or Redshift Spectrum.
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.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: Configure Kinesis Data Firehose to invoke a Lambda function for data transformation. — Kinesis Data Firehose can invoke a Lambda function as a transformation step before data is delivered to S3. This allows you to convert JSON records to Parquet format inline, without needing an intermediate storage or separate processing pipeline. The Lambda function receives batches of records, transforms them (e.g., using PyArrow or similar libraries), and returns them to Firehose for delivery.
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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Question Discussion
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