- A
Amazon S3 Transfer Acceleration
Why wrong: Acceleration speeds up uploads, does not transform.
- B
Amazon Kinesis Data Firehose with Lambda transformation
Firehose can call Lambda to transform records.
- C
AWS Glue ETL
Glue can process and transform data during ETL.
- D
Amazon Athena
Why wrong: Athena queries data at rest, not in transit.
- E
AWS Data Pipeline
Why wrong: Data Pipeline moves data but transformations are limited.
Question 414 of 1,786
DEA-C01 In-transit 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. A key principle to apply: in-transit transformation. 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.
Which TWO AWS services can be used to transform data in transit during ingestion? (Choose 2.)
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
✓
Amazon Kinesis Data Firehose with Lambda transformation
Amazon Kinesis Data Firehose can invoke an AWS Lambda function to transform streaming data in real time before delivering it to a destination, making it suitable for transforming data in transit during ingestion. AWS Glue ETL can be used for both batch and streaming transformations; specifically, AWS Glue supports streaming ETL jobs that can transform data in transit as it is ingested from sources like Amazon MSK or Kinesis Data Streams. Therefore, both services can transform data during ingestion, albeit with different use cases and latency characteristics.
Key principle: In-transit transformation
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon S3 Transfer Acceleration
Why it's wrong here
Acceleration speeds up uploads, does not transform.
- ✓
Amazon Kinesis Data Firehose with Lambda transformation
Why this is correct
Firehose can call Lambda to transform records.
Related concept
In-transit transformation
- ✓
AWS Glue ETL
Why this is correct
Glue can process and transform data during ETL.
Related concept
In-transit transformation
- ✗
Amazon Athena
Why it's wrong here
Athena queries data at rest, not in transit.
- ✗
AWS Data Pipeline
Why it's wrong here
Data Pipeline moves data but transformations are limited.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often mistakenly think that AWS Glue ETL is only for batch processing and cannot transform data in transit, but AWS Glue supports streaming ETL jobs that can transform data during ingestion, making it a valid choice for in-transit transformation.
Detailed technical explanation
How to think about this question
Kinesis Data Firehose with Lambda transformation buffers incoming records and invokes the Lambda function synchronously, allowing you to modify, filter, or enrich each record before delivery. The Lambda function must return the transformed records within the Firehose buffer interval (default 60 seconds) and payload size limits (up to 3 MB per invocation). A common real-world scenario is converting JSON logs to Parquet format on the fly to optimize downstream analytics costs.
KKey Concepts to Remember
- In-transit transformation
- Amazon Kinesis Data Firehose
- AWS Glue ETL (Streaming)
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
In-transit transformation
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
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.
Review in-transit transformation, then practise related DEA-C01 questions on the same topic to reinforce the concept.
- →
Data Ingestion and Transformation — study guide chapter
Learn the concepts, then practise the questions
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Data Ingestion and Transformation practice questions
Targeted practice on this topic area only
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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 — In-transit transformation.
What is the correct answer to this question?
The correct answer is: Amazon Kinesis Data Firehose with Lambda transformation — Amazon Kinesis Data Firehose can invoke an AWS Lambda function to transform streaming data in real time before delivering it to a destination, making it suitable for transforming data in transit during ingestion. AWS Glue ETL can be used for both batch and streaming transformations; specifically, AWS Glue supports streaming ETL jobs that can transform data in transit as it is ingested from sources like Amazon MSK or Kinesis Data Streams. Therefore, both services can transform data during ingestion, albeit with different use cases and latency characteristics.
What should I do if I get this DEA-C01 question wrong?
Review in-transit transformation, then practise related DEA-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
In-transit transformation
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Last reviewed: Jul 4, 2026
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