Exam AI-100 topic 4 question 17 discussion

Actual exam question from Microsoft's AI-100

Question #: 17
Topic #: 4

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
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You have Azure IoT Edge devices that generate streaming data.
On the devices, you need to detect anomalies in the data by using Azure Machine Learning models. Once an anomaly is detected, the devices must add information about the anomaly to the Azure IoT Hub stream.
Solution: You deploy Azure Functions as an IoT Edge module.
Does this meet the goal?

A. Yes
B. No

Show Suggested Answer

Suggested Answer: B 🗳️
Instead use Azure Stream Analytics and REST API.
Note. Available in both the cloud and Azure IoT Edge, Azure Stream Analytics offers built-in machine learning based anomaly detection capabilities that can be used to monitor the two most commonly occurring anomalies: temporary and persistent.
Stream Analytics supports user-defined functions, via REST API, that call out to Azure Machine Learning endpoints.
References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-machine-learning-anomaly-detection

by blackdeath at Dec. 6, 2020, 4:59 p.m.

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rveney

2 years ago

A. Yes Azure Functions can be deployed as an IoT Edge module to detect anomalies in the data by using Azure Machine Learning models and add information about the anomaly to the Azure IoT Hub stream

upvoted 1 times

rveney

2 years ago

B. No The solution of deploying Azure Functions as an IoT Edge module does not meet the goal of detecting anomalies in the data generated by Azure IoT Edge devices and adding information about the anomaly to the Azure IoT Hub stream. Azure Functions are serverless compute resources that respond to events and execute code in the cloud. They are not designed to be deployed as IoT Edge modules running directly on edge devices. To achieve the goal, you would need to deploy an Azure Machine Learning model as an IoT Edge module. This allows you to run the machine learning model directly on the edge devices, enabling real-time anomaly detection on the devices themselves. Once an anomaly is detected, the IoT Edge module can add relevant information about the anomaly to the Azure IoT Hub stream.

upvoted 1 times

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blackdeath

4 years, 7 months ago

Correct

upvoted 4 times

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