Correct Answer: Monitor 'Watermark Delay' and increase the number of Streaming Units (SUs) or partition the query
Watermark Delay measures the delay between the event time and the time the event is processed. An increasing delay means the job is falling behind. To fix this, scale the job by in...
Correct Answer
Monitor 'Watermark Delay' and increase the number of Streaming Units (SUs) or partition the query
Why This Answer Is Correct
Watermark Delay measures the delay between the event time and the time the event is processed. An increasing delay means the job is falling behind. To fix this, scale the job by increasing SUs and ensuring the query is partitioned for parallel processing.
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.
IoT sensor data arrives in Azure IoT Hub. You need to detect when the average temperature across all sensors exceeds 75°C for any 5-minute window, and send an alert immediately when that threshold is crossed. Which Azure service should you use?
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 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?