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Answer: Use the built-in 'SELECT' statement with a threshold comparison to filter the temperature readings and output the results to an alerting service.
Option C is the most suitable approach for implementing real-time temperature threshold detection and alerting. By using the built-in 'SELECT' statement in Azure Stream Analytics, you can filter the temperature readings based on a specified threshold. When a reading exceeds the threshold, the result can be output to an alerting service, such as Azure Event Hubs or Azure Logic Apps, to trigger an alert. This approach allows for real-time processing and immediate alerting without the need for batch processing or custom deserialization logic.
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You are using Azure Stream Analytics to process real-time data from IoT devices. The devices send temperature readings, and you need to detect and alert when the temperature exceeds a certain threshold. Which of the following approaches would be most suitable for implementing this scenario?
A
Use a batch processing approach and store the data in a temporary storage before processing.
B
Implement a custom deserialization logic to handle the temperature readings.
C
Use the built-in 'SELECT' statement with a threshold comparison to filter the temperature readings and output the results to an alerting service.
D
Leverage Azure Machine Learning to predict temperature thresholds and send alerts based on the predictions.