- 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.
DEA-C01 Data Store Management Practice Question
This DEA-C01 practice question tests your understanding of data store management. 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 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.
Option D is correct because Amazon Kinesis Data Firehose can directly convert incoming JSON data to Parquet format by referencing a table schema defined in the AWS Glue Data Catalog. This allows Firehose to perform the schema-aware conversion without custom code, leveraging the Glue table's column definitions and SerDe for Parquet serialization.
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
The DEA-C01 exam often tests the misconception that Firehose can automatically convert to Parquet without a schema definition, leading candidates to select Option A, but in reality, Firehose requires an explicit schema (via Glue or Lambda) for Parquet conversion.
Detailed technical explanation
How to think about this question
Under the hood, Firehose uses the Glue Data Catalog table's schema to map JSON fields to Parquet columns, leveraging the Parquet SerDe (org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe) for serialization. A subtle behavior is that the Glue table must define the schema exactly matching the incoming JSON structure, including nested fields, or the conversion will fail with schema mismatch errors. In a real-world scenario, if IoT devices send varying schemas (e.g., optional fields), you must use a Lambda transformation to normalize the data before Parquet conversion.
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.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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.
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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. — Option D is correct because Amazon Kinesis Data Firehose can directly convert incoming JSON data to Parquet format by referencing a table schema defined in the AWS Glue Data Catalog. This allows Firehose to perform the schema-aware conversion without custom code, leveraging the Glue table's column definitions and SerDe for Parquet serialization.
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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Last reviewed: Jul 4, 2026
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