DP-203 Develop data processing Practice Question
You are designing a near-real-time data processing solution that ingests millions of events per second from IoT devices. The data must be aggregated on a per-minute basis and stored in Azure Data Lake Storage Gen2 for long-term analytics. The solution must also support alerting when certain thresholds are exceeded. Which combination of Azure services should you use?
⚠ Common exam trap
A common mix-up: candidates choose Azure Databricks (Option C) for streaming because of its popularity, but they overlook that Azure Stream Analytics is purpose-built for low-latency, SQL-based stream processing and alerting with minimal code, making it more appropriate for this near-real-time IoT scenario.
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 IoT Hub, Azure Stream Analytics, and Azure Functions.
Azure IoT Hub is designed for secure, bidirectional communication with IoT devices and can ingest millions of events per second. Azure Stream Analytics provides low-latency, SQL-based stream processing to aggregate data on a per-minute basis and can output to Azure Data Lake Storage Gen2 for long-term analytics. Additionally, Stream Analytics can trigger Azure Functions for alerting when thresholds are exceeded, enabling near-real-time responses.
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 Event Hubs, Azure Data Factory, and Azure SQL Database.
Why it's wrong here
Event Hubs ingests events but Data Factory is batch-oriented; SQL Database is not optimized for high-velocity data.
- ✓
Azure IoT Hub, Azure Stream Analytics, and Azure Functions.
Why this is correct
IoT Hub ingests device data, Stream Analytics performs per-minute aggregation and alerting, and Functions can output to Data Lake Storage.
- ✗
Azure IoT Hub, Azure Databricks with Structured Streaming, and Azure Data Lake Storage Gen2.
Why it's wrong here
Databricks can do streaming but is overkill for simple aggregation; alerting would require additional components.
- ✗
Azure Event Hubs, Azure Data Explorer, and Power BI.
Why it's wrong here
Data Explorer is for analytics, not for real-time alerting; Power BI is for visualization.
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 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.