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NEW QUESTION: 1
製造工場の品質監査を行うために内部監査人が任命されています。監査人は最初にどの行動方針を実行する必要がありますか？
A. 計画された出力と実際の出力を比較します。
B. 購入した材料のコストを確認します。
C. スクラップとやり直しのレベルを確認します。
D. 生産割当を満たすプラントの能力を評価します。
Answer: C

NEW QUESTION: 2
What is the most secure way to address an unused Windows service so it cannot be exploited by malware?
A. Uninstall it
B. Set to manual startup
C. Firewall it
D. Disable it
Answer: A

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 Functions as an IoT Edge module.
Does this meet the goal?
A. Yes
B. No
Answer: B
Explanation:
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