- A
AWS Lambda
AWS Lambda can be used as a consumer of Kinesis Data Streams to perform lightweight, per-record transformations on the JSON payloads in real-time, but it is not sufficient alone for aggregation.
- B
Amazon Kinesis Data Analytics
Amazon Kinesis Data Analytics is necessary for real-time aggregation (e.g., per-second averages) and complex transformations using SQL or Flink.
- C
Amazon S3
Why wrong: Amazon S3 is a storage service and does not provide real-time transformation or aggregation; it is not suitable for this pipeline.
- D
Amazon Kinesis Data Streams
Amazon Kinesis Data Streams is the ingestion service that receives sensor data every second and makes it available for real-time processing.
- E
Amazon Kinesis Data Firehose
Why wrong: Amazon Kinesis Data Firehose is a delivery service with near-real-time latency (minimum 60 seconds) and cannot perform per-second aggregations; it is not appropriate here.
DEA-C01 Amazon Kinesis Data Streams 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: amazon Kinesis Data Streams. 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 is ingesting IoT sensor data into Amazon Kinesis Data Streams. Each sensor sends a JSON payload every second. The data must be transformed and aggregated in real-time before being stored in Amazon DynamoDB. Which THREE services should be used together in the pipeline? (Choose THREE.)
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
AWS Lambda
Amazon Kinesis Data Streams (D) is the ingestion layer that captures the JSON payloads from IoT sensors in real-time, ensuring data is available for processing. AWS Lambda (A) can be used as a consumer of the stream to perform lightweight, per-record transformations, such as filtering or enriching the JSON payloads. Amazon Kinesis Data Analytics (B) is required for real-time aggregation and complex transformations using SQL or Apache Flink, enabling calculations like averages or counts per second before storing in DynamoDB. Together, these three services form a complete real-time pipeline: ingest with Kinesis Data Streams, transform/aggregate with Kinesis Data Analytics, and optionally further transform with Lambda before writing to DynamoDB.
Key principle: Amazon Kinesis Data Streams
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
AWS Lambda
Why this is correct
AWS Lambda can be used as a consumer of Kinesis Data Streams to perform lightweight, per-record transformations on the JSON payloads in real-time, but it is not sufficient alone for aggregation.
Related concept
Amazon Kinesis Data Streams
- ✓
Amazon Kinesis Data Analytics
Why this is correct
Amazon Kinesis Data Analytics is necessary for real-time aggregation (e.g., per-second averages) and complex transformations using SQL or Flink.
Related concept
Amazon Kinesis Data Streams
- ✗
Amazon S3
Why it's wrong here
Amazon S3 is a storage service and does not provide real-time transformation or aggregation; it is not suitable for this pipeline.
- ✓
Amazon Kinesis Data Streams
Why this is correct
Amazon Kinesis Data Streams is the ingestion service that receives sensor data every second and makes it available for real-time processing.
Related concept
Amazon Kinesis Data Streams
- ✗
Amazon Kinesis Data Firehose
Why it's wrong here
Amazon Kinesis Data Firehose is a delivery service with near-real-time latency (minimum 60 seconds) and cannot perform per-second aggregations; it is not appropriate here.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap is that candidates often confuse Amazon Kinesis Data Firehose with a real-time processing service, but Firehose is a delivery service with near-real-time latency (minimum 60 seconds) and cannot perform per-second aggregations. Additionally, some might think only Lambda is needed for transformation, but Kinesis Data Analytics is better suited for real-time aggregations like sliding windows.
Detailed technical explanation
How to think about this question
Kinesis Data Streams provides a durable, ordered stream of data records, and Kinesis Data Analytics uses SQL or Apache Flink to perform real-time aggregations (e.g., windowed sums or averages) on the stream. Lambda functions can be attached as event sources to the stream for fine-grained transformations, but for complex aggregations, Kinesis Data Analytics is more efficient as it processes data in-memory using time windows, reducing the need for custom code and managing state internally.
KKey Concepts to Remember
- Amazon Kinesis Data Streams
- AWS Lambda
- Amazon Kinesis Data Analytics
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
Amazon Kinesis Data Streams
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 amazon Kinesis Data Streams, then practise related DEA-C01 questions on the same topic to reinforce the concept.
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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 — Amazon Kinesis Data Streams.
What is the correct answer to this question?
The correct answer is: AWS Lambda — Amazon Kinesis Data Streams (D) is the ingestion layer that captures the JSON payloads from IoT sensors in real-time, ensuring data is available for processing. AWS Lambda (A) can be used as a consumer of the stream to perform lightweight, per-record transformations, such as filtering or enriching the JSON payloads. Amazon Kinesis Data Analytics (B) is required for real-time aggregation and complex transformations using SQL or Apache Flink, enabling calculations like averages or counts per second before storing in DynamoDB. Together, these three services form a complete real-time pipeline: ingest with Kinesis Data Streams, transform/aggregate with Kinesis Data Analytics, and optionally further transform with Lambda before writing to DynamoDB.
What should I do if I get this DEA-C01 question wrong?
Review amazon Kinesis Data Streams, then practise related DEA-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Amazon Kinesis Data Streams
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Last reviewed: Jul 4, 2026
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