- A
Use Amazon EC2 instances running Apache Spark Streaming
Why wrong: Not serverless; requires management.
- B
Use Amazon Kinesis Data Firehose with an AWS Lambda function for transformation and output to Parquet
Firehose can invoke Lambda per record and convert to Parquet.
- C
Use Amazon SageMaker for transformation
Why wrong: SageMaker is for ML, not data transformation.
- D
Use an AWS Glue ETL job triggered by a Kinesis stream
Why wrong: Glue jobs are billed per DPU and are better for complex transforms.
Question 1,326 of 1,786
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. 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 company needs to transform JSON data from Amazon Kinesis Data Streams into Parquet format and store it in Amazon S3. The transformation includes simple field mappings and type conversions. Which approach is most cost-effective and serverless?
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
✓
Use Amazon Kinesis Data Firehose with an AWS Lambda function for transformation and output to Parquet
Amazon Kinesis Data Firehose can directly deliver streaming data to Amazon S3 in Parquet format. By attaching an AWS Lambda function for simple field mappings and type conversions, you achieve a fully serverless, cost-effective solution without managing any infrastructure. This approach minimizes costs because you pay only for the data volume processed by Firehose and the Lambda invocations, avoiding the overhead of always-on compute resources.
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 Amazon EC2 instances running Apache Spark Streaming
Why it's wrong here
Not serverless; requires management.
- ✓
Use Amazon Kinesis Data Firehose with an AWS Lambda function for transformation and output to Parquet
Why this is correct
Firehose can invoke Lambda per record and convert to Parquet.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Amazon SageMaker for transformation
Why it's wrong here
SageMaker is for ML, not data transformation.
- ✗
Use an AWS Glue ETL job triggered by a Kinesis stream
Why it's wrong here
Glue jobs are billed per DPU and are better for complex transforms.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often overestimate the need for full ETL engines like Glue or Spark for simple transformations, overlooking that Kinesis Data Firehose with Lambda is the most cost-effective and serverless option for lightweight field mappings and format conversions.
Detailed technical explanation
How to think about this question
Kinesis Data Firehose natively supports converting incoming data to Parquet using a schema from AWS Glue Data Catalog or a custom schema, but for dynamic transformations like field mappings, a Lambda function acts as a data processor. Under the hood, Firehose buffers data up to 60 seconds or 1-128 MB before invoking Lambda, then delivers the transformed records to S3 in the specified format. A real-world scenario is ingesting IoT sensor data where you need to rename fields and cast timestamps before analytics, making Firehose+Lambda ideal for low-latency, high-throughput pipelines.
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
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 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: Use Amazon Kinesis Data Firehose with an AWS Lambda function for transformation and output to Parquet — Amazon Kinesis Data Firehose can directly deliver streaming data to Amazon S3 in Parquet format. By attaching an AWS Lambda function for simple field mappings and type conversions, you achieve a fully serverless, cost-effective solution without managing any infrastructure. This approach minimizes costs because you pay only for the data volume processed by Firehose and the Lambda invocations, avoiding the overhead of always-on compute resources.
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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