DP-203 Structured Streaming Practice Question
You are a data engineer for a healthcare company that processes patient data. You have an Azure Databricks workspace with a cluster configured for data processing. You need to implement a solution that processes streaming data from Azure Event Hubs, enriches it with reference data stored in Azure Cosmos DB, and writes the output to Delta Lake in Azure Data Lake Storage Gen2. The solution must ensure that the data processing is fault-tolerant and can handle schema evolution. The reference data is updated infrequently. You need to choose an approach that minimizes complexity and cost. What should you do?
⚠ Common exam trap
The trap is that candidates might choose Delta Live Tables or Stream Analytics for streaming, but the question emphasizes minimizing complexity and cost, making Structured Streaming with static join the best fit.
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 a streaming static join to enrich with reference data from Cosmos DB, and write to Delta Lake. Enable schema evolution on the Delta table.
Azure Databricks Structured Streaming can read from Event Hubs, and a streaming static join allows enriching with reference data from Cosmos DB (which is updated infrequently). Writing to Delta Lake with schema evolution enabled handles schema changes. This approach minimizes complexity and cost by using native Databricks features without additional services.
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 Auto Loader with Delta Live Tables to ingest streaming data, and use Change Data Capture from Cosmos DB to update the reference data inline.
Why it's wrong here
Change Data Capture adds unnecessary complexity for infrequent updates.
- ✗
Use Azure Data Factory to copy data from Event Hubs to Azure Data Lake Storage Gen2 in batches, then use Azure Databricks to process and enrich with Cosmos DB.
Why it's wrong here
Batch processing increases latency and complexity.
- ✗
Use Azure Stream Analytics to ingest from Event Hubs, join with Cosmos DB reference data, and output to Azure Data Lake Storage Gen2 in Parquet format.
Why it's wrong here
Adds extra service and cost without benefit.
- ✓
Use Azure Databricks Structured Streaming to read from Event Hubs, use a streaming static join to enrich with reference data from Cosmos DB, and write to Delta Lake. Enable schema evolution on the Delta table.
Why this is correct
Simplifies processing and handles schema evolution.
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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
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