DVA-C02 Development with AWS Services Practice Question
Which THREE AWS services are commonly used together to build a serverless event-driven architecture that processes real-time streaming data? (Choose three.)
⚠ Common exam trap
Many exam-takers confuse Amazon SQS with Kinesis Data Streams, but SQS is a pull-based queue for decoupled messaging, not a streaming data platform with ordered, replayable records.
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 Streams
Amazon Kinesis Data Streams (A) is correct because it ingests and buffers real-time streaming data at scale, serving as the event source for downstream serverless processing. AWS Lambda (B) is correct because it provides serverless compute that can be triggered by Kinesis stream records via event source mappings, enabling event-driven processing without managing servers. Amazon DynamoDB (C) is correct because it is a fully managed, serverless NoSQL database commonly used as the sink to store processed streaming results, and DynamoDB Streams can further propagate events. Amazon SQS (D) is not selected because it is a message queue for decoupling components, not a real-time streaming data service, and it is not one of the three services typically combined for streaming ingestion and processing in this scenario. Amazon Redshift (E) is not selected because it is a data warehouse designed for analytical queries on batch-loaded data, not for real-time serverless stream processing.
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 Kinesis Data Streams
Why this is correct
Amazon Kinesis Data Streams is a highly scalable and durable real-time data streaming service. It can continuously capture gigabytes of data per second from hundreds of thousands of sources, such as website clickstreams, IoT device data, and financial transactions. This service acts as the entry point for real-time data pipelines, providing a persistent, ordered, and replayable stream of records for downstream processing.
- ✓
AWS Lambda
Why this is correct
AWS Lambda is a serverless compute service that allows you to run code without provisioning or managing servers. When integrated with Kinesis Data Streams, Lambda functions can be automatically invoked to process batches of records as they arrive in the stream. This enables real-time data transformation, enrichment, and analysis, making it a powerful component for event-driven architectures that react instantly to incoming data.
- ✓
Amazon DynamoDB
Why this is correct
Amazon DynamoDB is a fast, flexible NoSQL database service designed for single-digit millisecond performance at any scale. After data is ingested by Kinesis and processed by Lambda, DynamoDB is an ideal choice for storing the results due to its low-latency read/write capabilities and high availability. It effectively serves as a persistent store for processed real-time data, enabling quick retrieval for applications or dashboards.
- ✗
Amazon SQS
Why it's wrong here
Amazon SQS (Simple Queue Service) is a fully managed message queuing service that enables you to decouple and scale microservices, distributed systems, and serverless applications. While SQS handles messages, it is primarily designed for asynchronous point-to-point or fan-out messaging patterns, not for continuous, ordered, and replayable streams of data like Kinesis. Its pull-based model and lack of inherent stream replay capabilities make it unsuitable for the continuous ingestion and processing of real-time data streams.
- ✗
Amazon Redshift
Why it's wrong here
Amazon Redshift is a fully managed, petabyte-scale data warehouse service optimized for analytical workloads. It is designed for complex queries over large datasets, typically for batch processing and business intelligence, rather than real-time, record-by-record ingestion and processing. While processed data might eventually be loaded into Redshift for historical analysis, it is not used as an immediate component for ingesting or processing real-time data streams directly from sources.
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 DVA-C02 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 DVA-C02 exam.