DP-203 Develop data processing Practice Question
You need to process streaming data from Azure Event Hubs and store the results in Azure Cosmos DB for a real-time dashboard. The solution must handle duplicate events and ensure exactly-once processing. Which Azure service should you use?
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
✓
Azure Stream Analytics
(Azure Stream Analytics) is correct because it provides exactly-once processing when configured with Event Hubs and Cosmos DB output. Option A (Azure Data Factory) is batch-oriented. Option B (Azure Functions) may have at-least-once guarantees. Option D (Azure Databricks) can achieve exactly-once but requires more configuration.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Azure Data Factory
Why it's wrong here
Not designed for streaming.
- ✗
Azure Functions with Event Hubs trigger
Why it's wrong here
Typically at-least-once.
- ✓
Azure Stream Analytics
Why this is correct
Supports exactly-once semantics with Event Hubs.
- ✗
Azure Databricks Structured Streaming
Why it's wrong here
While Databricks Structured Streaming provides exactly-once semantics through checkpointing and write-ahead logs, it requires manual implementation of idempotent logic to handle duplicates when writing to Cosmos DB. It is tempting because Spark's micro-batch architecture excels at complex transformations on high-volume streams; however, Azure Stream Analytics manages stateful de-duplication natively via built-in functions, satisfying the specific requirement for exactly-once processing without additional custom code.
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 |
Go deeper
Related to this question
Learn chapter
Introduction to Azure Data Engineering
Key term
Azure Databricks
Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform optimized for Azure that lets data teams prepare data, run machine learning models, and build data pipelines using a single workspace.
Key term
Azure Stream Analytics
Azure Stream Analytics is a fully managed, real-time data processing service that analyzes and transforms high volumes of streaming data from various sources to deliver low-latency insights and trigger actions.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DP-203 practice question is part of Courseiva's free Microsoft 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 DP-203 exam.