DP-203 Develop data processing Practice Question
You are a Data Engineer at Contoso Ltd. The company operates an e-commerce platform that generates streaming data from user interactions (clicks, page views, purchases) at a rate of 50,000 events per second. The data is ingested into Azure Event Hubs. You need to design a data processing solution that meets the following requirements:
1. Real-time dashboards must display aggregated metrics (e.g., total sales, active users) with a latency of less than 2 seconds. 2. Historical data must be stored in Azure Data Lake Storage Gen2 in Delta Lake format for batch analytics. 3. The solution must support exactly-once semantics for the streaming output to both the dashboard and the data lake. 4. You must use Azure Stream Analytics as the primary processing engine. 5. The output to the dashboard should use Azure Power BI, while the output to the data lake should use Azure Synapse Analytics serverless SQL pool for querying.
You have configured an Azure Stream Analytics job with Event Hubs as input. For output, you added a Power BI dataset and an Azure Data Lake Storage Gen2 output. However, you discover that the Power BI dataset is being updated with duplicate records, and the data lake output sometimes misses small windows of data during job restarts. Which two actions should you take to meet the requirements? (Choose two.)
A. Change the Power BI output to use the 'Exactly once' output mode. B. Change the Event Hubs compatibility level to 1.0 to guarantee exactly-once. C. Enable checkpointing in the Stream Analytics job and use the 'Exactly once' output mode for the Azure Data Lake Storage Gen2 output. D. Use a second Stream Analytics job: one for Power BI with 'At least once' mode, and another for the data lake with 'Exactly once' mode.
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
✓
Change the Power BI output to use the 'Exactly once' output mode. [CORRECT]
Power BI output supports 'Exactly once' output mode, which prevents duplicate records in the dashboard. Option C is correct because enabling checkpointing and using 'Exactly once' output mode for Azure Data Lake Storage Gen2 ensures exactly-once delivery to the data lake and prevents data loss during job restarts. Option B is incorrect because changing Event Hubs compatibility level to 1.0 does not guarantee exactly-once semantics; it only affects the serialization format. Option D is incorrect because using a second Stream Analytics job adds unnecessary complexity, and 'At least once' mode for Power BI would still allow duplicates.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Change the Power BI output to use the 'Exactly once' output mode. [CORRECT]
Why this is correct
Correct: Power BI output can be configured with 'Exactly once' output mode, which eliminates duplicate records by ensuring each event is written only once to the dataset.
- ✗
Change the Event Hubs compatibility level to 1.0 to guarantee exactly-once. [wrong]
Why it's wrong here
Incorrect: Changing Event Hubs compatibility level to 1.0 does not provide exactly-once semantics; it only changes the Avro serialization format and does not affect output delivery guarantees.
- ✓
Enable checkpointing in the Stream Analytics job and use the 'Exactly once' output mode for the Azure Data Lake Storage Gen2 output. [CORRECT]
Why this is correct
Correct: Enabling checkpointing allows the job to resume from the last committed state, preventing data loss. Using 'Exactly once' output mode for Azure Data Lake Storage Gen2 ensures each event is written exactly once, meeting the exactly-once requirement.
- ✗
Use a second Stream Analytics job: one for Power BI with 'At least once' mode, and another for the data lake with 'Exactly once' mode. [wrong]
Why it's wrong here
Incorrect: Using two separate jobs adds operational complexity and cost. 'At least once' mode for Power BI could still produce duplicates, so it does not fully meet the exactly-once requirement for the dashboard.
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 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
Azure Synapse Analytics
Azure Synapse Analytics is a cloud-based data integration, warehousing, and analytics service that brings together big data and data warehouse capabilities under one platform.
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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.