DP-203 Develop data processing Practice Question
You are designing a data processing solution for a healthcare organization. The solution must process streaming data from IoT devices and store it in Azure Data Lake Storage Gen2. The data must be available for both real-time dashboards and historical analysis. You need to minimize operational overhead. What should you do?
⚠ Common exam trap
The trap here is that candidates often overcomplicate the solution by choosing Databricks (Option C) for its flexibility, overlooking that Stream Analytics provides a simpler, fully managed approach with native dual-output support that minimizes operational overhead.
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
✓
Use Azure Stream Analytics to output to both Power BI and Data Lake Storage
Azure Stream Analytics can directly output to both Power BI (for real-time dashboards) and Azure Data Lake Storage Gen2 (for historical analysis) in a single job, minimizing operational overhead by avoiding the need for multiple services or custom code. This serverless, fully managed service handles streaming data processing with low latency and integrates natively with Azure IoT Hub or Event Hubs for ingestion, making it ideal for healthcare IoT scenarios.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Ingest data via Azure Functions and write to Data Lake Storage; use Power BI to query Data Lake
Why it's wrong here
Azure Functions add operational overhead and latency for real-time dashboards.
- ✓
Use Azure Stream Analytics to output to both Power BI and Data Lake Storage
Why this is correct
Stream Analytics is serverless, supports real-time output to Power BI and batch writes to Data Lake.
- ✗
Use Azure Databricks with Structured Streaming to write to Data Lake Storage and use Power BI DirectQuery
Why it's wrong here
Operational overhead of managing clusters.
- ✗
Ingest data to Azure Event Hubs, then use Event Hubs Capture to store in Data Lake Storage; use Power BI with Event Hubs
Why it's wrong here
Event Hubs Capture is batch, not real-time for dashboards.
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.