DEA-C01 Amazon Kinesis Data Streams Practice Question
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.)
⚠ Common exam trap
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.
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.
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.
- ✓
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.
- ✗
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.
- ✗
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.
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 |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.