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NEW QUESTION: 1

A. Option A
B. Option D
C. Option B
D. Option C
Answer: D

NEW QUESTION: 2
What is the ideal bridge in which mixed-vendor environments are communicating in high-definition video?
A. Cisco TelePresence MCU 4500 Series
B. Cisco TelePresence MCU 4200 Series
C. Cisco TelePresence MCU 4501 Series
D. Cisco TelePresence MCU MSE 8000 Series
Answer: A
Explanation:
Explanation/Reference:

NEW QUESTION: 3
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.
After you answer a question, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
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 Stream Analytics as an IoT Edge module.
Does this meet the goal?
A. Yes
B. No
Answer: A
Explanation:
Explanation
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