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?
⚠ Common exam trap
DP-203 often tests the choice between stream processing services. The trap is assuming Azure Stream Analytics is always the best for real-time analytics, but when advanced machine learning and Spark capabilities are needed, Databricks Structured Streaming is the correct choice.
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 is designed for scalable, low-latency stream processing and integrates with Event Hubs and Cosmos DB via connectors. It allows using Spark SQL and machine learning libraries to detect cheating patterns in real time. This approach handles high throughput and provides the necessary analytics capabilities. The other options either lack the required real-time processing or the advanced analytics.
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
Structured Streaming reads Event Hubs incrementally, satisfying the low-latency, high-throughput requirement, while Spark SQL and MLlib detect cheating patterns in-stream. The Azure Cosmos DB Spark connector writes micro-batch results directly to Cosmos DB, enabling real-time dashboards without a separate serving layer.
- ✗
Use Azure Functions with Event Hubs trigger to process each event and write to Cosmos DB.
Why it's wrong here
Per-event Azure Functions invocations introduce cold-start and per-execution overhead that cannot sustain the stem's high-throughput, low-latency streaming requirement; Databricks Structured Streaming handles this. Functions suit sporadic, event-driven workloads such as image resizing or queue-triggered tasks, not continuous real-time pattern detection.
- ✗
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
Data Factory continuous copy plus Synapse serves batch and micro-batch loading, not the low-latency per-event detection required; Synapse pools add query latency. This pattern is tempting for scheduled ingestion and analytical warehousing, but real-time cheating detection needs Databricks Structured Streaming reading Event Hubs directly.
- ✗
Use Azure Stream Analytics to query the stream for cheating patterns and output to Cosmos DB.
Why it's wrong here
Azure Stream Analytics supports SQL-based stream queries, yet the scenario specifies an Azure Databricks pipeline, and Structured Streaming there handles the Event Hubs throughput and Cosmos DB sink natively. Stream Analytics suits standalone, low-code streaming when no Databricks workspace is mandated.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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.