DEA-C01 Data Operations and Support Practice Question
A company uses Amazon Kinesis Data Firehose to deliver streaming data to Amazon S3. The data must be transformed in real-time using a custom Lambda function. Which TWO steps are required to enable this? (Choose TWO)
⚠ Common exam trap
DEA-C01 often tests the Firehose transformation trap: candidates assume transformation logic is written inside the Firehose configuration or that Kinesis Data Analytics is required, when in fact Firehose delegates to a Lambda function that must return a specific JSON envelope.
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 to use a Lambda function for data transformation
Option A is correct because enabling real-time transformation in Kinesis Data Firehose requires configuring the delivery stream to invoke a Lambda function as its processing step, which Firehose then calls synchronously for each batch of records. Option B is correct because the Lambda function must return the transformed records in the exact format Firehose expects — a JSON object containing the processed records with fields such as recordId, result (Ok, Dropped, or ProcessingFailed), and data (base64-encoded) — otherwise Firehose cannot continue delivery. Option C is wrong because Kinesis Data Analytics is a separate service for SQL/Flink stream analytics, not the mechanism Firehose uses for Lambda-based transformation. Option D is wrong because Firehose does not execute inline transformation logic; it only supports invoking a Lambda function for that purpose. Option E is wrong because using Kinesis Data Streams as a source is unrelated to enabling Lambda transformation and is not a required step.
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 use a Lambda function for data transformation
Why this is correct
Firehose only invokes a Lambda function for transformation when the delivery stream is explicitly configured with that function's Amazon Resource Name under its processing parameters. Without this configuration, records pass through untransformed, so the real-time transformation requirement cannot be met.
- ✓
Ensure the Lambda function returns the transformed records in the correct format
Why this is correct
Firehose invokes the Lambda synchronously and expects a specific response structure: recordId, result (Ok, Dropped, or ProcessingFailed), and base64-encoded data. Returning records in that format lets Firehose continue delivery to Amazon S3, satisfying the real-time transformation requirement.
- ✗
Create a Kinesis Data Analytics application to transform the data
Why it's wrong here
Kinesis Data Analytics performs SQL or Apache Flink analytics, not the Firehose Lambda processor the scenario requires. It is tempting because it genuinely transforms streaming data, but it is the right choice for continuous queries and aggregations, not for invoking a custom Lambda within a Firehose delivery stream.
- ✗
Write the transformation logic directly in the Firehose delivery stream configuration
Why it's wrong here
Firehose delivery stream configuration has no field for arbitrary transformation code; it only references a Lambda function ARN. It is tempting because inline logic sounds self-contained, but Firehose invokes Lambda, so the transformation must be authored and deployed as a separate function.
- ✗
Use Kinesis Data Streams as the source for Firehose
Why it's wrong here
Firehose already ingests directly from sources such as Kinesis Data Streams, so adding a stream does not enable Lambda transformation. It is tempting because streams offer replay and multiple consumers, but the requirement is configuring the delivery stream's Lambda processor, not changing its source.
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 |
Go deeper
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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