DEA-C01 Amazon Kinesis Data Firehose Practice Question
A company wants to ingest streaming data from IoT devices into Amazon S3 using Amazon Kinesis Data Firehose. The data must be transformed from JSON to Parquet format before landing in S3. What is the SIMPLEST way to achieve this?
⚠ Common exam trap
Candidates often overlook the native Parquet conversion capability in Kinesis Data Firehose and assume a Lambda function is required. The built-in conversion using an AWS Glue schema is actually simpler and supported directly.
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
✓
Configure Kinesis Data Firehose with a built-in Parquet converter.
Amazon Kinesis Data Firehose has a built-in Parquet conversion feature that uses an AWS Glue schema to convert incoming JSON data to Parquet format. This is the simplest approach because it requires no custom code or additional services; you only need to provide a schema and enable the conversion in the Firehose delivery stream configuration. Option B (using Lambda) is more complex, as it requires writing and maintaining a custom transformation function.
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 with a built-in Parquet converter.
Why this is correct
Correct. Firehose has a built-in Parquet converter that uses an AWS Glue schema. This is the simplest method as it requires no custom code or additional services.
- ✗
Use an AWS Lambda function as a data transformation in Kinesis Data Firehose to convert JSON to Parquet.
Why it's wrong here
Using a Lambda function for data transformation in Kinesis Data Firehose is incorrect for this scenario because Firehose offers native, built-in capabilities to convert JSON to Parquet directly, eliminating the need for custom code. This built-in feature satisfies the 'simplest way' requirement. Lambda transformations are designed for custom processing logic, such as data enrichment, filtering, or complex schema manipulations not covered by Firehose's standard format conversions. It would be the correct choice if a non-native or bespoke transformation were needed.
- ✗
Use Kinesis Data Firehose to deliver data directly to S3 in JSON format and run a nightly Glue job to convert to Parquet.
Why it's wrong here
Incorrect. This approach involves additional complexity (nightly Glue job) and delays, whereas Firehose can convert in near real-time with native support.
- ✗
Use Kinesis Data Analytics to convert the data to Parquet before sending to Firehose.
Why it's wrong here
Kinesis Data Analytics performs real-time stream processing using Apache Flink, but it lacks a native, direct output connector for Kinesis Data Firehose that can serialise data into Parquet; instead, it sends processed records back to a Firehose stream as JSON, requiring a separate Lambda transformation step to convert to Parquet. This option is tempting because Kinesis Data Analytics is designed for stream-level transformations like filtering and aggregation, and in scenarios where complex analytics or stateful processing is needed before delivery, it would be the correct choice—but here the requirement is solely a format conversion, which Firehose’s built-in Lambda integration handles without an intermediate analytics layer.
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 |
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.