- A
Enable Firehose's built-in Parquet conversion without any additional configuration.
Why wrong: Firehose requires a schema (Glue Data Catalog) for Parquet conversion.
- B
Use Amazon Kinesis Data Analytics to convert the data format.
Why wrong: Kinesis Data Analytics is for analytics, not format conversion.
- C
Configure Firehose to convert the data to Apache Avro format.
Why wrong: Avro conversion requires a schema, and Firehose does not support Avro natively.
- D
Create a Glue Data Catalog table defining the schema and configure Firehose to use the table for Parquet conversion.
Firehose can use the schema from Glue Data Catalog to convert to Parquet.
- E
Create an AWS Lambda function to transform the data to Parquet and use it as a Firehose transformation.
Lambda can convert JSON to Parquet and Firehose can invoke the transformation.
Quick Answer
The correct answer involves two actions: creating an AWS Lambda function to transform the data to Parquet and using it as a Firehose transformation, and configuring Firehose to directly convert JSON to Parquet using a schema from the Glue Data Catalog. This works because Kinesis Data Firehose natively supports converting JSON to Parquet when you provide a table schema via the Glue Data Catalog, but it also allows custom transformations through Lambda for cases where additional logic or complex parsing is needed. On the AWS Certified Data Engineer Associate DEA-C01 exam, this question tests your understanding of Firehose’s built-in format conversion versus Lambda-based transformation—a common trap is assuming Firehose can convert to Parquet without a schema, or that Kinesis Data Analytics is required for format changes. Remember the memory tip: “Firehose needs a Glue guide for Parquet pride, but Lambda can lend a hand for custom land.”
DEA-C01 Data Store Management Practice Question
This DEA-C01 practice question tests your understanding of data store management. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 data pipeline that ingests streaming data from IoT devices into Amazon S3 using Amazon Kinesis Data Firehose. The data must be transformed from JSON to Parquet format before storage. Which TWO actions should the data engineer take to achieve this?
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
Create a Glue Data Catalog table defining the schema and configure Firehose to use the table for Parquet conversion.
Kinesis Data Firehose can convert JSON to Parquet using a schema from a Glue Data Catalog table. Option C is correct because Firehose can use an AWS Lambda function for transformation. Option E is correct because Firehose can directly convert to Parquet if a schema is provided via Glue Data Catalog. Option A is wrong because Firehose does not support direct conversion to Avro without a schema. Option B is wrong because Kinesis Data Analytics is for real-time analytics, not format conversion. Option D is wrong because Firehose cannot directly convert to Parquet without a schema; it needs Glue Data Catalog.
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.
- ✗
Enable Firehose's built-in Parquet conversion without any additional configuration.
Why it's wrong here
Firehose requires a schema (Glue Data Catalog) for Parquet conversion.
- ✗
Use Amazon Kinesis Data Analytics to convert the data format.
Why it's wrong here
Kinesis Data Analytics is for analytics, not format conversion.
- ✗
Configure Firehose to convert the data to Apache Avro format.
Why it's wrong here
Avro conversion requires a schema, and Firehose does not support Avro natively.
- ✓
Create a Glue Data Catalog table defining the schema and configure Firehose to use the table for Parquet conversion.
Why this is correct
Firehose can use the schema from Glue Data Catalog to convert to Parquet.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Create an AWS Lambda function to transform the data to Parquet and use it as a Firehose transformation.
Why this is correct
Lambda can convert JSON to Parquet and Firehose can invoke the transformation.
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 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.
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Data Store Management — study guide chapter
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FAQ
Questions learners often ask
What does this DEA-C01 question test?
Data Store Management — This question tests Data Store Management — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Create a Glue Data Catalog table defining the schema and configure Firehose to use the table for Parquet conversion. — Kinesis Data Firehose can convert JSON to Parquet using a schema from a Glue Data Catalog table. Option C is correct because Firehose can use an AWS Lambda function for transformation. Option E is correct because Firehose can directly convert to Parquet if a schema is provided via Glue Data Catalog. Option A is wrong because Firehose does not support direct conversion to Avro without a schema. Option B is wrong because Kinesis Data Analytics is for real-time analytics, not format conversion. Option D is wrong because Firehose cannot directly convert to Parquet without a schema; it needs Glue Data Catalog.
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.
About these practice questions
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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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