You have Azure IoT Edge devices that collect measurements every 30 seconds. You plan to send the measurements to an Azure IoT hub. You need to ensure that every event is processed as quickly as possible. What should you use?
Suggested Answer:D🗳️
Use Azure Notebooks to develop a machine learning module and deploy it to a Linux device running Azure IoT Edge. You can use IoT Edge modules to deploy code that implements your business logic directly to your IoT Edge devices. References: https://docs.microsoft.com/en-us/azure/iot-edge/tutorial-deploy-machine-learning
I think it should be ASA with windowing function, that perform operation on events within a window, and one of the types is window hopping that can collect this data based on time
To ensure that every event is processed as quickly as possible when sending measurements from Azure IoT Edge devices to an Azure IoT hub, you should use:
B. Azure Stream Analytics record functions
Azure Stream Analytics is a fully managed real-time analytics service in Azure that can process and analyze streaming data from various sources, including IoT devices. By utilizing record functions within Azure Stream Analytics, you can process each event or measurement as it arrives, enabling near real-time processing.
Answer: D
Use Azure Notebooks to develop a machine learning module and deploy it to a Linux device running Azure IoT Edge. You can use IoT Edge modules to deploy code that implements your business logic directly to your IoT Edge devices.
References: https://docs.microsoft.com/en-us/azure/iot-edge/tutorial-deploy-machine-learning
The question doesn't give sufficient details to answer precisely. For instance, there is nothing preventing Kafka as the right fit. ASA windowing function also could work by using the Now option (only output though). I'm more inclined to saying Kafka is the answer.
However, this is an AI exam. Since Kafka does fall under HDInsight documentation, it could be the closest.
The explanation you provided is in no way answering the question but the only relevance is that it is ML related.
If every event should be processed as quickly as possible, it should be done on the edge using a machine learning module. https://docs.microsoft.com/en-us/azure/iot-edge/tutorial-machine-learning-edge-05-configure-edge-device
Agree as well. Btw, no need to have the winodwing function. The event is already generated every 30s.
upvoted 2 times
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