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NEW QUESTION: 1
Your network contains one Active Directory domain named contoso.com.
From the Group Policy Management console, you view the details of a Group Policy object (GPO) named GPO1 as shown in the exhibit. (Click the Exhibit button.)
You need to ensure that the comments field of GPO1 contains a detailed description of GPO1.
What should you do?
Exhibit: * Missing*
A. From Group Policy Management, click View, and then click Customize.
B. From Notepad, edit \\contoso.com\SYSVOL\contoso.com\Policies\{229DCD27-9D98ACC2-A6AE -ED765F065FF5}\gpt.ini.
C. From Active Directory Users and Computers, edit the properties of contoso.com/System/Policies/{229DCD27-9D98-ACC2-A6AE -ED765F065FF5}.
D. Open GPO1 in the Group Policy Management Editor, and then modify the properties of GPO1.
Answer: D
Explanation:
You can include comments for each Group Policy object. You can use this space to document the Group Policy object and why its implementation is important to your environment. Commenting GPOs allows you to later use keyword filter to help you quickly find GPOs with matching keywords. Adding a comment to a Group Policy object
- -- ---
Open the Group Policy Management Console. Expand the Group Policy Objects
node.
Right-click the Group Policy object you want to comment and then click Edit .
In the console tree, right-click the name of the Group Policy object and then click
Properties .
Click the Comment tab.
Type your comments in the Comment box.
Click OK
Reference: Comment a Group Policy Object https://technet.microsoft.com/en-us/library/cc770974.aspx

NEW QUESTION: 2
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

NEW QUESTION: 3
DRAG DROP
Select and Place:
Select and Place:

Answer:
Explanation: