- A
Use AWS Glue as a transformation step between Firehose and Redshift, with a trigger on S3.
Why wrong: Adding Glue increases latency and cost.
- B
Use Kinesis Data Firehose with direct PUT to Redshift and rely on Redshift's COPY command to transform.
Why wrong: Firehose cannot directly PUT to Redshift; it uses COPY, but transformation must be done upstream.
- C
Configure a Lambda function in the Firehose delivery stream to transform records before delivery.
Firehose supports Lambda for data transformation with minimal overhead.
- D
Use the Kinesis Client Library (KCL) to consume the stream, transform in an EC2 instance, and then load to Redshift.
Why wrong: This adds operational overhead and cost.
Question 925 of 1,786
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 setting up an Amazon Kinesis Data Firehose delivery stream to load data into Amazon Redshift. The data is coming from an application that produces JSON records. The engineer needs to transform the data to match the Redshift table schema. Which approach is the MOST cost-effective and requires the least operational overhead?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"least"Why it matters: You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
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 a Lambda function in the Firehose delivery stream to transform records before delivery.
Option C is correct because Kinesis Data Firehose natively supports invoking a Lambda function as a transformation step within the delivery stream. This allows the engineer to write a simple Lambda function that parses the incoming JSON records and transforms them to match the Redshift table schema, all without provisioning or managing any additional infrastructure. This approach is the most cost-effective (pay per invocation) and requires the least operational overhead since Firehose handles the orchestration, retries, and delivery to Redshift automatically.
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.
- ✗
Use AWS Glue as a transformation step between Firehose and Redshift, with a trigger on S3.
Why it's wrong here
Adding Glue increases latency and cost.
- ✗
Use Kinesis Data Firehose with direct PUT to Redshift and rely on Redshift's COPY command to transform.
Why it's wrong here
Firehose cannot directly PUT to Redshift; it uses COPY, but transformation must be done upstream.
- ✓
Configure a Lambda function in the Firehose delivery stream to transform records before delivery.
Why this is correct
Firehose supports Lambda for data transformation with minimal overhead.
Clue confirmation
The clue word "least" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use the Kinesis Client Library (KCL) to consume the stream, transform in an EC2 instance, and then load to Redshift.
Why it's wrong here
This adds operational overhead and cost.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often overestimate the transformation capabilities of Redshift's COPY command, mistakenly believing it can perform complex record-level transformations, when in fact it only supports basic data mapping and format parsing, not arbitrary JSON restructuring.
Detailed technical explanation
How to think about this question
Under the hood, when you enable data transformation in Firehose, the service buffers incoming records and invokes your Lambda function synchronously with a batch of records. The Lambda function must return the transformed records in the same order and with the same record IDs, or Firehose will treat failed records as delivery failures. A subtle behavior is that Firehose imposes a 60-second Lambda invocation timeout and a 6 MB payload limit per invocation; for large volumes, you may need to adjust the batch size or use a more efficient transformation logic to avoid timeouts.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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 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 a Lambda function in the Firehose delivery stream to transform records before delivery. — Option C is correct because Kinesis Data Firehose natively supports invoking a Lambda function as a transformation step within the delivery stream. This allows the engineer to write a simple Lambda function that parses the incoming JSON records and transforms them to match the Redshift table schema, all without provisioning or managing any additional infrastructure. This approach is the most cost-effective (pay per invocation) and requires the least operational overhead since Firehose handles the orchestration, retries, and delivery to Redshift automatically.
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.
Are there clue words in this question I should notice?
Yes — watch for: "least". You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
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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