You train and register an Azure Machine Learning model
You plan to deploy the model to an online endpoint
You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.
Solution:
Create a managed online endpoint with the default authentication settings. Deploy the model to the online endpoint.
Does the solution meet the goal?
Answer : B
You train and register an Azure Machine Learning model
You plan to deploy the model to an online endpoint
You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.
Solution:
Create a managed online endpoint and set the value of its auth.mode parameter to aml.token. Deploy the model to the online endpoint.
Does the solution meet the goal?
Answer : B
You have an Azure Machine Learning workspace. You build a deep learning model.
You need to publish a GPU-enabled model as a web service.
Which two compute targets can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer : A, B
You are using Azure Machine Learning to monitor a trained and deployed model. You implement Event Grid to respond to Azure Machine Learning events.
Model performance has degraded due to model input data changes.
You need to trigger a remediation ML pipeline based on an Azure Machine Learning event.
Which event should you use?
Answer : B
You create an Azure Machine Learning workspace.
You must configure an event handler to send an email notification wten data drift is detected in the workspace datasets. You must minimize development efforts.
You need to configure an Azure service to send the notification.
Which Azure service should you use?
Answer : D
You manage an Azure Machine Learning workspace named workspaces
You must develop Python SDK v2 code to attach an Azure Synapse Spark pool as a compute target in workspaces The code must invoke the constructor of the SynapseSparkCompute class.
You need to invoke the constructor.
What should you use?
Answer : B
You create an Azure Machine Learning pipeline named pipeline1 with two steps that contain Python scripts. Data processed by the first step is passed to the second step.
You must update the content of the downstream data source of pipeline1 and run the pipeline again
You need to ensure the new run of pipeline1 fully processes the updated content.
Solution: Set the allow_reuse parameter of the PythonScriptStep object of both steps to False
Does the solution meet the goal?
Answer : B
