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NEW QUESTION: 1
You use Azure Machine Learning to train and register a model.
You must deploy the model into production as a real-time web service to an inference cluster named service-compute that the IT department has created in the Azure Machine Learning workspace.
Client applications consuming the deployed web service must be authenticated based on their Azure Active Directory service principal.
You need to write a script that uses the Azure Machine Learning SDK to deploy the model. The necessary modules have been imported.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
Box 1: AksCompute
Example:
aks_target = AksCompute(ws,"myaks")
# If deploying to a cluster configured for dev/test, ensure that it was created with enough
# cores and memory to handle this deployment configuration. Note that memory is also used by
# things such as dependencies and AML components.
deployment_config = AksWebservice.deploy_configuration(cpu_cores = 1, memory_gb = 1) service = Model.deploy(ws, "myservice", [model], inference_config, deployment_config, aks_target) Box 2: AksWebservice Box 3: token_auth_enabled=Yes Whether or not token auth is enabled for the Webservice.
Note: A Service principal defined in Azure Active Directory (Azure AD) can act as a principal on which authentication and authorization policies can be enforced in Azure Databricks.
The Azure Active Directory Authentication Library (ADAL) can be used to programmatically get an Azure AD access token for a user.
Incorrect Answers:
auth_enabled (bool): Whether or not to enable key auth for this Webservice. Defaults to True.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-azure-kubernetes-service
https://docs.microsoft.com/en-us/azure/databricks/dev-tools/api/latest/aad/service-prin-aad-token

NEW QUESTION: 2
管理者が、リソースの枯渇を防ぐために、Palo Alto Networks NGFWで保護設定を定義しています。 プラットフォーム使用率を考慮した場合、管理者はパケットバッファ保護を設定して適用するためにどの手順を実行する必要がありますか。
A. パケットバッファしきい値を有効にしてから設定するインターフェイスバッファ保護を有効にします。
B. パケットバッファ保護しきい値を有効にして設定します。入力ゾーンごとにパケットバッファ保護を有効にします。
C. すべての入力ゾーンにゾーン保護プロファイルを作成して適用します。入力ゾーンごとにパケットバッファ保護を有効にします。
D. すべての出力ゾーンに対してゾーン保護プロファイルを設定および適用します。出力ゾーンの前にパケットバッファ保護を有効にします。
E. vsysごとのセッションしきい値アラートを有効にし、パケットバッファ制限をトリガーします。ゾーンごとにゾーンバッファ保護を有効にします。
Answer: B

NEW QUESTION: 3
A company is developing a solution that allows smart refrigerators to send temperature information to a central location. You have an existing Service Bus.
The solution must receive and store message until they can be processed. You create an Azure Service Bus Instance by providing a name, pricing tier, subscription, resource group, and location.
You need to complete the configuration.
Which Azure CLI or PowerShell command should you run?
A.
B.
C.
D.
Answer: C
Explanation:
A service bus instance has already been created (Step 2 below). Next is step 3, Create a Service Bus queue.
Note:
Steps:
Step 1: # Create a resource group
resourceGroupName="myResourceGroup"
az group create --name $resourceGroupName --location eastus
Step 2: # Create a Service Bus messaging namespace with a unique name
namespaceName=myNameSpace$RANDOM
az servicebus namespace create --resource-group $resourceGroupName --name
$namespaceName --location eastus
Step 3: # Create a Service Bus queue
az servicebus queue create --resource-group $resourceGroupName --namespace-name
$namespaceName --name BasicQueue
Step 4: # Get the connection string for the namespace
connectionString=$(az servicebus namespace authorization-rule keys list --resource-group
$resourceGroupName --namespace-name $namespaceName --name
RootManageSharedAccessKey --query primaryConnectionString --output tsv) Reference:
https://docs.microsoft.com/en-us/azure/service-bus-messaging/service-bus-quickstart-cli