Quick Answer
The answer is AWS Lambda, as it is the most suitable service for performing near real-time transformations on streaming data ingested from IoT devices via Kinesis Data Streams. Lambda functions can be triggered directly by Kinesis records, allowing you to run custom code to filter, enrich, or reshape each event before writing the transformed output to Amazon S3. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of serverless compute integration with streaming services, often appearing in questions that distinguish between transformation and analytics or delivery. A common trap is confusing Kinesis Data Firehose, which can perform simple transformations only through an integrated Lambda, or Kinesis Data Analytics, which is designed for complex SQL-based stream analysis rather than lightweight record-by-record transforms. Remember the memory tip: “Lambda for logic, Firehose for flow, Analytics for analysis.”
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 data engineering team needs to ingest real-time streaming data from thousands of IoT devices and transform the data before storing it in Amazon S3. Which AWS service is most suitable for performing the transformation step in near real-time?
AWS Lambda
Lambda can process Kinesis stream records and transform them.
Amazon Kinesis Data Analytics
Why wrong: Kinesis Data Analytics is for complex analytics, not simple transforms.
Amazon Kinesis Data Firehose
Why wrong: Firehose can transform using Lambda but is not a compute service; Lambda is the transformation engine.
Amazon S3
Why wrong: S3 is storage, not a compute service.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
AWS Lambda
Option A is correct because AWS Lambda can run code in response to Kinesis Data Streams events and perform transformations before writing to S3. Option B is incorrect because Kinesis Data Analytics is for running SQL/Java on streams, not simple transforms. Option C is incorrect because Kinesis Data Firehose is for loading data to destinations with optional simple transformations via Lambda. Option D is incorrect because Amazon S3 is storage, not a transformation service.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
AWS Lambda
Why this is correct
Lambda can process Kinesis stream records and transform them.
Related concept
Static NAT maps one inside address to one outside address.
Amazon Kinesis Data Analytics
Why it's wrong here
Kinesis Data Analytics is for complex analytics, not simple transforms.
Amazon Kinesis Data Firehose
Why it's wrong here
Firehose can transform using Lambda but is not a compute service; Lambda is the transformation engine.
Amazon S3
Why it's wrong here
S3 is storage, not a compute service.
Common exam traps
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
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.
What to study next
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related DEA-C01 NAT questions on configuration and troubleshooting.
Data Ingestion and Transformation — study guide chapter
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FAQ
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Static NAT maps one inside address to one outside address..
The correct answer is: AWS Lambda — Option A is correct because AWS Lambda can run code in response to Kinesis Data Streams events and perform transformations before writing to S3. Option B is incorrect because Kinesis Data Analytics is for running SQL/Java on streams, not simple transforms. Option C is incorrect because Kinesis Data Firehose is for loading data to destinations with optional simple transformations via Lambda. Option D is incorrect because Amazon S3 is storage, not a transformation service.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related DEA-C01 NAT questions on configuration and troubleshooting.
Static NAT maps one inside address to one outside address.
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Last reviewed: Jun 20, 2026
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.
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