Correct Answer: Azure Stream Analytics with a tumbling window query over IoT Hub input
Azure Stream Analytics is designed for real-time stream processing with time-based windowing. A tumbling window query on the IoT Hub input can compute the average temperature over ...
Correct Answer
Azure Stream Analytics with a tumbling window query over IoT Hub input
Why This Answer Is Correct
Azure Stream Analytics is designed for real-time stream processing with time-based windowing. A tumbling window query on the IoT Hub input can compute the average temperature over 5-minute windows and output an alert to Service Bus, Event Hubs, or another sink when the threshold is exceeded — all with minimal latency and no polling.
This question evaluates your knowledge of Azure Stream Analytics — a core concept tested in Microsoft Certified: Azure IoT Developer Specialty (AZ-220). Understanding Azure Stream Analytics is essential for designing scalable, secure, and cost-effective cloud architectures. Mastery of this topic will help you answer scenario-based questions confidently on exam day.
Your Stream Analytics job processes IoT Hub telemetry. You notice that out-of-order events are causing incorrect window calculations because some sensors report data with a delay of up to 10 seconds. How should you configure Stream Analytics to handle this?
You are using Stream Analytics to calculate the average temperature of a sensor over a moving 10-minute window, where a calculation is triggered every time a new event arrives. Which window type should you use?
A Stream Analytics job processing device telemetry is showing high latency and a large backlog. Which metric should you monitor, and what scaling action is appropriate?
A Stream Analytics query needs to archive telemetry data to Azure Storage and send real-time alerts to a Service Bus Queue. How should you implement this in a single Stream Analytics job?