DP-203 Develop data processing Practice Question
Your organization uses Azure Synapse Analytics. You need to design a data transformation pipeline that processes streaming data from Azure Event Hubs, performs aggregations over a 5-minute tumbling window, and loads the results into a dedicated SQL pool table. Which Azure service should you use to implement the streaming transformation?
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 the appropriate service for real-time stream processing with windowed aggregations. Option B is wrong because Azure Data Factory is for batch orchestration. Option C is wrong because Spark Structured Streaming is for big data workloads but less integrated with SQL pools. Option D is wrong because Azure Functions is not designed for streaming windowed aggregations.
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 Stream Analytics
Why this is correct
Azure Stream Analytics natively consumes Event Hubs streams and supports tumbling windows, enabling the required five-minute aggregations before writing results to the dedicated SQL pool. This satisfies the stem's streaming-transformation requirement without building custom windowing logic in Spark or Functions.
- ✗
Azure Data Factory
Why it's wrong here
Azure Data Factory orchestrates batch movement and scheduled pipelines; its data flows do not natively maintain tumbling-window state over a continuous Event Hubs stream. It is the right choice for scheduled batch ingestion and orchestration, whereas Stream Analytics performs the windowed streaming aggregation and sink write.
- ✗
Apache Spark for Azure Synapse
Why it's wrong here
Apache Spark for Azure Synapse processes micro-batches and can window streams, but requires you to write and maintain structured streaming code plus checkpointing, and it does not write to a dedicated SQL pool as a native sink. Stream Analytics provides declarative tumbling windows and a built-in SQL pool output.
- ✗
Azure Functions
Why it's wrong here
Azure Functions executes event-driven code per invocation, so maintaining 5-minute tumbling-window state across events requires external state storage and manual windowing. It suits lightweight, short-lived triggers. Stream Analytics natively implements tumbling windows and writes directly to a dedicated SQL pool.
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
Develop Stream Processing Solutions
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.
Key term
Data Transformation Pipelines
Data transformation pipelines are automated sequences of steps that take raw data from a source, clean and reshape it into a usable format, and then load it into a destination for analysis or storage.
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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.