You must monitor cost at the endpoint and deployment level.
You have a trained model that must be deployed as an online endpoint. Users must authenticate by using Microsoft Entra ID.
What should you do?
A.
Deploy the model to Azure Kubernetes Service (AKS). During deployment, set the token_auth_mode parameter of the target configuration object to true.
B.
Deploy the model to Azure Kubernetes Service (AKS). During deployment, set the auth_mode parameter to configure the authentication type.
C.
Deploy the model to a managed online endpoint. During deployment, set the auth_mode parameter to configure the authentication type.
D.
Deploy the model to a managed online endpoint. During deployment, set the token_auth_mode parameter of the target configuration object to true.
C.
Attributes: Diagnostics and Monitoring and Cost
Managed online endpoints (v2):
- Local endpoint debugging possible with Docker and Visual Studio Code
- Advanced metrics and logs analysis with chart/query to compare between deployments
- Cost breakdown down to deployment level
-Azure Monitor and Log Analytics powered (includes key metrics and log tables for endpoints and deployments)
ACI or AKS(v1): No easy local debugging
https://learn.microsoft.com/en-us/azure/machine-learning/concept-endpoints-online?view=azureml-api-2
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