DP-900 Describe core data concepts Practice Question
Which TWO Azure services can be used to perform real-time stream processing?
⚠ Common exam trap
It's easy for candidates to confuse Azure Data Factory and Azure Synapse Pipelines with stream processing because they can handle data movement, but they are fundamentally batch-oriented orchestration tools, not real-time stream processors.
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
✓
Azure Stream Analytics
Azure Stream Analytics (B) is a fully managed, real-time analytics service designed to ingest continuous streams from sources like Event Hubs, IoT Hub, or Blob Storage and run SQL-like queries with temporal windows (Tumbling, Hopping, Sliding, Session) to produce low-latency output to sinks such as Power BI, Cosmos DB, or SQL Database, making it a canonical real-time stream processing engine. Azure Databricks Structured Streaming (D) is built on Apache Spark and treats a live data stream as an unbounded table, supporting exactly-once semantics, event-time processing with watermarking, and continuous or micro-batch execution against sources like Kafka, Event Hubs, and Delta Lake, so it also performs real-time stream processing. Azure Data Factory (A) is a batch-oriented data integration and orchestration service using pipelines and activities, not a streaming engine. Azure Analysis Services (C) provides semantic tabular modeling and OLAP query capabilities over pre-processed data rather than processing live streams. Azure Synapse Pipelines (E) is the orchestration component of Synapse Analytics, used for scheduled batch ETL/ELT workflows, not real-time stream processing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Azure Data Factory
Why it's wrong here
Azure Data Factory orchestrates batch-oriented data movement and transformation pipelines, not real-time stream processing; it lacks native support for unbounded data ingestion or low-latency event handling. It is tempting because Data Factory can schedule recurring data loads and integrate with various sources, making it a correct choice for scheduled, incremental batch processing scenarios where near-real-time latency is acceptable.
- ✓
Azure Stream Analytics
Why this is correct
Azure Stream Analytics is a serverless, purpose-built real-time stream processing engine that executes SQL-like continuous queries over data arriving from sources such as Event Hubs, IoT Hub, or Blob storage. It provides sub-second to single-digit-second latency by applying temporal windows (tumbling, hopping, sliding) and supports outputs to Power BI, SQL Database, and other targets, making it the most direct Azure service for real-time analytics.
- ✗
Azure Analysis Services
Why it's wrong here
Azure Analysis Services is not a streaming engine; it hosts tabular or multidimensional semantic models used for enterprise BI and reporting. It loads and caches data from sources at rest through partitions, allowing users to define measures, KPIs, and hierarchies for tools like Excel or Power BI, but it cannot consume unbounded event flows or perform event-by-event processing, so it cannot satisfy a real-time streaming requirement.
- ✓
Azure Databricks Structured Streaming
Why this is correct
Azure Databricks Structured Streaming is a valid near-real-time solution because it uses Apache Spark's DataFrame API to process incoming data as incremental micro-batches, supporting event-time windows, watermarking, and exactly-once semantics when writing to Delta Lake. Although it is not a true event-at-a-time engine like Stream Analytics, it offers low-latency, stateful, and code-flexible stream processing that fits many real-time ETL and machine learning workloads.
- ✗
Azure Synapse Pipelines
Why it's wrong here
Synapse Pipelines are an orchestration and data integration service designed for scheduled, batch-oriented tasks such as copying data, invoking notebooks, or running stored procedures. Pipelines can be triggered by a schedule or an external event, but they have no native stateful streaming capability or low-latency event-by-event execution; instead, they move finite blocks of data, making them unsuitable for true real-time processing.
Go deeper
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Key term
IoT Hub
IoT Hub is a managed cloud service in Azure that acts as a central message hub for secure, bidirectional communication between IoT devices and the cloud.
Key term
OLAP
OLAP (Online Analytical Processing) is a computing approach that enables users to quickly and interactively analyze multidimensional data from multiple perspectives for business intelligence and decision support.
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