You create an Azure Machine Learning workspace named woricspace1. The workspace contains a Python SDK v2 notebook that uses MLflow to collect model training metrics and artifacts from your local computer.You must reuse the notebook to run on Azure Machine Learning compute instance in workspace1.You need to continue to log metrics and artifacts from your data science code.What should you do?

Instantiate the MLCIient class.

You manage an Azure Machine Learning workspace. You design a training job that is configured with a serverless compute. The serverless compute must have a specific instance type and countYou need to configure the serverless compute by using Azure Machine Learning Python SDK v2. What should you do?

Initialize and specify the ResourceConfiguration class

Specify the compute name by using the compute parameter of the command job

Configure the tier parameter to Dedicated VM.

Initialize and specify the ResourceConfiguration class

Initialize AmICompute class with size and type specification.

Add a linked service to Workspace1.

Upload a json file to Workspace1.

Upload a yaml file to Workspace1.

Deploy the model to a managed online endpoint. During deployment, set the auth_mode parameter to configure the authentication type.

Deploy the model lo Azure Kubernetes Service (AKS). During deployment, set the token_auth_mode parameter of the target configuration object to true.

Deploy the model to a managed online endpoint. During deployment, set the token_auth_mode parameter of the target configuration object to true.

Deploy the model to Azure Kubernetes Service (AKS). During deployment, set the auth.mode parameter to configure the authentication type.

Deploy the model to a managed online endpoint. During deployment, set the auth_mode parameter to configure the authentication type.

mlflow.log_metric('my_metric', my_dict)

You have an Azure Machine Learning workspace named Workspace 1 Workspace! has a registered Mlflow model named model 1 with PyFunc flavor

You plan to deploy model1 to an online endpoint named endpointl without egress connectivity by using Azure Machine learning Python SDK vl

You have the following code:

Microsoft DP-100 image Question 166 89447 10022024015826000000

You need to add a parameter to the ManagedOnllneDeployment object to ensure the model deploys successfully

Solution: Add the with_package parameter.

Does the solution meet the goal?

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