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NEW QUESTION: 1
Sie haben einen Server namens Server1, auf dem Windows Server 2016 ausgeführt wird.
Server1 verfügt über vier SCSI-Datenträger und einen Speicherpool mit dem Namen Pool1, der drei Datenträger enthält.
Sie erstellen ein virtuelles Laufwerk mit dem Namen Laufwerk 1, das ein gespiegeltes Layout verwendet.
Sie erstellen eine Partition mit dem Namen Partition1, die den gesamten verfügbaren Speicherplatz auf Datenträger 1 verwendet.
Sie müssen Partition1 erweitern.
Was solltest du zuerst tun?
A. Erweitern Sie auf der Seite Speicherpools im Server-Manager eine virtuelle Festplatte.
B. Erweitern Sie in der Datenträgerverwaltung ein Volume.
C. Führen Sie in Windows PowerShell das Cmdlet Resize-Partition aus.
D. Ändern Sie in der Datenträgerverwaltung die Eigenschaften von Partition1.
Answer: A
Explanation:
Erläuterung
Verweise:
https://charbelnemnom.com/2015/03/step-by-step-how-to-extend-and-resize-a-two-way-mirrored-storage-tiered-
NEW QUESTION: 2
ユーザーがWindowsベースのEC2インスタンスを起動しました。ただし、インスタンスにはいくつかの問題があり、ユーザーはログを確認したいと考えています。ユーザーがAWSコンソールからインスタンスコンソールの出力を確認すると、何が表示されますか？
A. インスタンスの起動以降のすべてのイベントログ
B. Windowsインスタンスはコンソール出力をサポートしていません
C. 最後の10システムイベントログエラー
D. 最後の3つのシステムイベントのログエラー
Answer: D
Explanation:
Explanation
The AWS EC2 console provides a useful tool called Console output for problem diagnosis. It is useful to find out any kernel issues, termination reasons or service configuration issues. For a Windows instance it lists the last three system event log errors. For Linux it displays the exact console output.
NEW QUESTION: 3
Azure Container Instanceにモデルをデプロイします。
モデルAPIを呼び出すには、Azure Machine Learning SDKを使用する必要があります。
ネイティブSDKクラスおよびメソッドを使用して、デプロイされたモデルを呼び出す必要があります。
コマンドをどのように完了する必要がありますか？回答するには、回答領域で適切なオプションを選択します。
注：それぞれの正しい選択は1ポイントの価値があります。
Answer:
Explanation:
Explanation:
Box 1: from azureml.core.webservice import Webservice
The following code shows how to use the SDK to update the model, environment, and entry script for a web service to Azure Container Instances:
from azureml.core import Environment
from azureml.core.webservice import Webservice
from azureml.core.model import Model, InferenceConfig
Box 2: predictions = service.run(input_json)
Example: The following code demonstrates sending data to the service:
import json
test_sample = json.dumps({'data': [
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
[10, 9, 8, 7, 6, 5, 4, 3, 2, 1]
]})
test_sample = bytes(test_sample, encoding='utf8')
prediction = service.run(input_data=test_sample)
print(prediction)
Reference:
https://docs.microsoft.com/bs-latn-ba/azure/machine-learning/how-to-deploy-azure-container-instance
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment