Question 158 of 760
DP-203 Develop data processing Practice Question
You are developing a data processing pipeline for a gaming company that uses Azure Databricks. The pipeline processes game event data from Azure Event Hubs. You need to detect cheating patterns by analyzing events in real time. The solution must be able to handle high throughput and low latency. The output should be written to Azure Cosmos DB for real-time dashboards. Which approach 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 Databricks Structured Streaming to read from Event Hubs, use Spark SQL and machine learning to detect cheating patterns, and write to Cosmos DB using the Azure Cosmos DB Spark connector.
Azure Databricks Structured Streaming can handle high throughput and low latency, and it can write to Cosmos DB using the Azure Cosmos DB Spark connector. Option B is wrong because Azure Functions with Event Hubs trigger would process each event individually, which may struggle with very high throughput and lacks the flexibility for complex pattern detection using machine learning. Option C is wrong because Azure Data Factory is not designed for real-time processing; it is an orchestration tool for batch data movement. Option D is wrong because Azure Stream Analytics is simpler but may not provide the same flexibility for custom machine learning models as Databricks.
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 Databricks Structured Streaming to read from Event Hubs, use Spark SQL and machine learning to detect cheating patterns, and write to Cosmos DB using the Azure Cosmos DB Spark connector.
Why this is correct
Supports high throughput and complex analytics.
- ✗
Use Azure Functions with Event Hubs trigger to process each event and write to Cosmos DB.
Why it's wrong here
Not scalable for high throughput.
- ✗
Use Azure Data Factory with continuous copy to load data into Cosmos DB and then use Azure Synapse Analytics to detect patterns.
Why it's wrong here
Not real-time.
- ✗
Use Azure Stream Analytics to query the stream for cheating patterns and output to Cosmos DB.
Why it's wrong here
Less flexible for complex pattern detection.
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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Last reviewed: Jun 21, 2026
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.
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