DP-900 Describe an analytics workload on Azure Practice Question
A manufacturing company installs temperature sensors in a factory. Sensor data is streamed to Azure Event Hubs. The company needs to detect when the average temperature of any sensor exceeds 100°F over a 5-minute sliding window and then send an alert. Which Azure service should be used for this real-time stream processing?
⚠ Common exam trap
A common mix-up: candidates confuse Azure Functions with Stream Analytics because both can process Event Hubs data, but Functions lacks native windowing and stateful aggregation capabilities, making it unsuitable for sliding window calculations without significant custom code.
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 designed for real-time stream processing, including the ability to define a 5-minute sliding window over sensor data from Event Hubs, compute the average temperature per sensor, and trigger an alert when the threshold of 100°F is exceeded. It natively integrates with Event Hubs as an input and supports temporal window functions like TumblingWindow, HoppingWindow, and SlidingWindow for exactly this kind of time-based aggregation.
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 Stream Analytics
Why this is correct
Azure Stream Analytics is a fully managed stream-processing engine that consumes from Azure Event Hubs and applies continuous SQL queries with time-windowed semantics (Tumbling, Hopping, Sliding) to compute values such as rolling temperature averages. A query can specify a SlidingWindow(second, 30) to emit an average every time an event arrives, or a TumblingWindow to emit on fixed intervals. This native windowing support, plus built-in state management and alerting to outputs like Power BI or SQL Database, makes it the correct choice for this real-time scenario.
- ✗
Azure Functions
Why it's wrong here
Azure Functions is an event-driven compute service that executes a function per message, but it does not provide native time-windowed aggregation or sliding-window state. To compute a 30-second average across a stream, you would have to persist partial aggregates in an external store (like Redis or Table Storage) and manage timers manually, which is error-prone and not a stream-processing feature. It can react to individual temperature alerts but cannot natively answer 'average over a sliding window' across the Event Hub stream.
- ✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is a platform-as-a-service relational database for persistent, structured data, not a real-time streaming processor. While it can serve as a destination for aggregates, it cannot subscribe to Event Hubs and compute tumbling or sliding window averages directly from a live stream. Attempting to poll Event Hubs from SQL Database would introduce latency and lack the temporal query constructs required for this scenario.
- ✗
Azure Logic Apps
Why it's wrong here
Azure Logic Apps is a low-code integration and orchestration service that coordinates connectors and workflows, but it is fundamentally a request-reply or trigger-based system, not a stream analytics engine. It can invoke an action for an incoming event, yet it has no built-in support for maintaining state across a time window or performing SQL-like aggregate calculations over sliding windows. Using it for temperature averaging would require complex manual state management across separate workflow runs.
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Related to this question
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Data Roles and Core Concepts
Key term
Trigger
In Azure data services, a trigger is a predefined automatic action that initiates a process when a specific event occurs, such as data arriving or a schedule being met.
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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