DP-900 Describe an analytics workload on Azure Practice Question
A company wants to build a near-real-time analytics solution on Azure. IoT devices send telemetry data to Azure Event Hubs. The data must be processed and stored in Azure Cosmos DB for low-latency queries. Which Azure service should be used to process the streaming data?
⚠ Common exam trap
Watch out — candidates often confuse Azure Functions (a general-purpose event-driven compute service) with a dedicated stream processing engine, overlooking that Functions lacks native support for continuous streaming, windowing, and exactly-once semantics required for near-real-time analytics.
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 is the correct choice because it is a fully managed stream processing engine designed specifically for real-time analytics on high-throughput data streams from sources like Azure Event Hubs. It can run SQL-like queries to filter, aggregate, and join streaming data, and output results directly to Azure Cosmos DB for low-latency queries, making it ideal for near-real-time IoT 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.
- ✗
Azure Logic Apps
Why it's wrong here
Azure Logic Apps is an integration and workflow orchestration service, not a stream processing engine. It can trigger on incoming events via connectors, but every execution is a discrete workflow that is inherently stateless and lacks built-in temporal windowing, aggregations, and query semantics needed for near-real-time analytics. Using it for analytics would require manually managing state and would introduce significant latency and complexity.
- ✗
Azure Functions
Why it's wrong here
Azure Functions is a serverless compute service that can react to individual events from Event Hubs, but each invocation processes one message at a time with no native in-memory state across events. For near-real-time analytics you need windowed aggregations, such as rolling averages or session detection, which Functions does not natively provide; scaling for high-throughput streams also requires careful configuration and does not guarantee the low-latency, ordered processing that a dedicated analytics service offers.
- ✓
Azure Stream Analytics
Why this is correct
Azure Stream Analytics is purpose-built for real-time analytics on unbounded data streams. It natively ingests from services like Event Hubs and IoT Hub, lets you express complex temporal queries in a SQL-like language with built-in windows and event-time handling, and can write results to Cosmos DB, Power BI, or other sinks with sub-second latency. As a fully managed platform, it handles checkpointing, failure recovery, and scaling so you can process high-volume streaming data without managing infrastructure.
- ✗
Azure Data Factory
Why it's wrong here
Azure Data Factory is a data integration service focused on batch ETL and ELT pipelines, not on continuous stream processing. It moves and transforms data on scheduled or tumbling-window triggers, giving minute-level or hourly granularity, which falls short of the sub-second response required for near-real-time analytics. Its activities are designed for large-volume historical loads and orchestration, not for stateful, event-time-aware analysis of a live event stream.
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Related to this question
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Data Roles and Core Concepts
Key term
Azure Cosmos DB
Azure Cosmos DB is a fully managed, globally distributed NoSQL database service that offers fast reads and writes anywhere in the world with automatic scaling and multiple consistency models.
Key term
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
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