hardMultiple SelectObjective-mapped
DP-203 Practice Question: Which THREE factors should be considered when…
Which THREE factors should be considered when choosing between Azure Stream Analytics and Azure Databricks for a real-time data processing solution?
⚠ Common exam trap
Test-takers frequently assume Power BI integration or late-arriving data handling are unique to one service, when in fact both services support these features, and the key differentiators are throughput scalability, exactly-once semantics, and the ability to perform complex transformations with ML integration.
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
✓
Need for complex transformations and machine learning model integration
Azure Databricks provides native support for complex transformations (e.g., windowed aggregations, multi-step ETL) and seamless integration with machine learning libraries (e.g., MLflow, Spark MLlib), which are not natively available in Azure Stream Analytics. Stream Analytics uses a SQL-like query language and is optimized for simpler, declarative transformations, making Databricks the better choice when advanced analytics or ML model scoring is required in real-time pipelines.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Integration with Power BI for real-time dashboards
Why it's wrong here
Both integrate with Power BI.
- ✓
Need for complex transformations and machine learning model integration
Why this is correct
Databricks supports complex ML pipelines natively.
- ✓
Volume of data per second (throughput)
Why this is correct
Stream Analytics is optimized for high throughput; Databricks may need scaling.
- ✓
Requirement for exactly-once semantics
Why this is correct
Stream Analytics offers built-in exactly-once; Databricks does not.
- ✗
Maximum allowed latency for late-arriving data
Why it's wrong here
Both handle late data with windowing.
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 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.
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