DP-203 Develop data processing Practice Question
You are a data engineer at a manufacturing company. You need to process sensor data from IoT devices that arrive in real time. The data is sent to Azure Event Hubs. You need to aggregate the data over 5-minute windows and store the results in Azure Data Lake Storage Gen2 in Parquet format. The solution should minimize cost and use serverless components. Which solution 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
✓
Use Azure Stream Analytics to create a query with a tumbling window of 5 minutes, and output the results to Azure Data Lake Storage Gen2 in Parquet format.
Azure Stream Analytics is a serverless, cost-effective solution for real-time stream processing with windowed aggregations. It can output directly to ADLS Gen2 in Parquet. Option B is wrong because Azure Databricks with Structured Streaming requires a running cluster, which is not serverless and incurs cost. Option C is wrong because Azure Data Factory is not designed for real-time streaming. Option D is wrong because Azure Functions would require custom code and may not handle large throughput efficiently.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use Azure Stream Analytics to create a query with a tumbling window of 5 minutes, and output the results to Azure Data Lake Storage Gen2 in Parquet format.
Why this is correct
Serverless, real-time, and cost-effective.
- ✗
Use Azure Databricks with Structured Streaming to read from Event Hubs, aggregate with a sliding window, and write to ADLS Gen2 in Parquet.
Why it's wrong here
Requires a running cluster, not serverless.
- ✗
Use Azure Data Factory with a tumbling window trigger to run a pipeline every 5 minutes that copies data from Event Hubs to ADLS Gen2.
Why it's wrong here
Not real-time; batch copy.
- ✗
Use Azure Functions with an Event Hubs trigger to aggregate data in memory and write to ADLS Gen2.
Why it's wrong here
Complex and less scalable.
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.