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NEW QUESTION: 1
A layer 2 LAN switch generates CAM table entries according to the ( ) of the received frame.
A. Source MAC address
B. Destination IP address
C. Source IP address
D. Destination MAC address
Answer: A

NEW QUESTION: 2
VM1という名前のAzure仮想マシンとadatum.comという名前のAzure Active Directory（Azure AD）テナントがあります。
D18912E1457D5D1DDCBD40AB3BF70D5D
VM1には次の設定があります。
* IPアドレス：10.10.0.10
*システム割り当ての管理対象ID：オン
VM1の認証トークンを取得するには、VM1内から実行するスクリプトを作成する必要があります。
スクリプトでどのアドレスを使用する必要がありますか？
A. 10.10.0.10
B. vm1.adatum.com
C. 169.254.169.254
D. vm1.adatum.com.onmicrosoft.com
Answer: C
Explanation:
Your code that's running on the VM can request a token from the Azure Instance Metadata Service identity endpoint, accessible only from within the VM: http://169.254.169.254/metadata/identity/oauth2/token Reference:
https://docs.microsoft.com/en-us/azure/active-directory/managed-identities-azure-resources/overview

NEW QUESTION: 3
You create a new Azure subscription. No resources are provisioned in the subscription.
You need to create an Azure Machine Learning workspace.
What are three possible ways to achieve this goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. Use an Azure Resource Management template that includes a Microsoft.MachineLearningServices/ workspaces resource and its dependencies.
B. Navigate to Azure Machine Learning studio and create a workspace.
C. Run Python code that uses the Azure ML SDK library and calls the Workspace.create method with name, subscription_id, resource_group, and location parameters.
D. Use the Azure Command Line Interface (CLI) with the Azure Machine Learning extension to call the az group create function with --name and --location parameters, and then the az ml workspace create function, specifying -w and -g parameters for the workspace name and resource group.
E. Run Python code that uses the Azure ML SDK library and calls the Workspace.get method with name, subscription_id, and resource_group parameters.
Answer: A,B,D
Explanation:
Explanation
B: You can use an Azure Resource Manager template to create a workspace for Azure Machine Learning.
Example:
{"type": "Microsoft.MachineLearningServices/workspaces",
...
C: You can create a workspace for Azure Machine Learning with Azure CLI Install the machine learning extension.
Create a resource group: az group create --name <resource-group-name> --location <location> To create a new workspace where the services are automatically created, use the following command: az ml workspace create -w <workspace-name> -g <resource-group-name> D: You can create and manage Azure Machine Learning workspaces in the Azure portal.
* Sign in to the Azure portal by using the credentials for your Azure subscription.
* In the upper-left corner of Azure portal, select + Create a resource.
* Use the search bar to find Machine Learning.
* Select Machine Learning.
* In the Machine Learning pane, select Create to begin.

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-workspace-template
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-manage-workspace-cli
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-manage-workspace

NEW QUESTION: 4
ある会社がクラウドで稼働している仮想データベースサーバーを持っており、その会社が新しく購入したブレードサーバーで社内でホスティングを開始したいと考えています。次の仮想化ツールのどれをこれを達成するために使用しますか？
A. V2V
B. P2V
C. P2P
D. V2P
Answer: D