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NEW QUESTION: 1
SCシリーズアレイを設置する際、顧客は、コントローラに最大容量のストレージスペースを提供するように設定したいことを示しました。ストレージコントローラには、12台の900 GBドライブと12台の同じタイプの1.8 TBドライブが付属していました。
最大のストレージ容量を提供するためにどの冗長性レベルを選択できますか？
A. 同じサイズのドライブをそれぞれの階層に配置します。900GBドライブのデフォルトの冗長性レベルを変更します。
B. 同じサイズのドライブを専用のページプールに入れます。900GBと100 GBの両方のデフォルトの冗長レベルを維持します。
1.8 TBのドライブ。
C. 同じサイズのドライブを専用のページプールに入れます。1.8TBドライブのデフォルトの冗長性レベルを変更します。
D. すべてのドライブを同じ階層に配置します。900 GBと1.8 TBの両方のドライブの既定の冗長レベルを維持します。
Answer: A
NEW QUESTION: 2
A. Option D
B. Option C
C. Option A
D. Option B
Answer: A
NEW QUESTION: 3
The following choices which belong to HD Resolution Ratio are ( ). (Multiple Choice)
A. 2560*1440
B. 1080P
C. 1024*768
D. 720P
Answer: A,B,D
NEW QUESTION: 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.
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 expose a Machine Learning model as an Azure web service.
Does this meet the goal?
A. No
B. Yes
Answer: A
Explanation:
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
