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Update how-to-identity-based-service-authentication.md
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articles/machine-learning/how-to-identity-based-service-authentication.md

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@@ -470,7 +470,7 @@ If ACR admin user is disallowed by subscription policy, you should first create
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[!INCLUDE [cli v2](includes/machine-learning-cli-v2.md)]
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```azurecli-interactive
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az ml workspace create -w <workspace name> \
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az ml workspace create -n <workspace name> \
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-g <workspace resource group> \
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-l <region> \
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--container-registry /subscriptions/<subscription id>/resourceGroups/<acr resource group>/providers/Microsoft.ContainerRegistry/registries/<acr name>
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[!INCLUDE [cli v2](includes/machine-learning-cli-v2.md)]
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```azurecli-interactive
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az ml compute show --name <cluster name> -w <workspace> -g <resource group>
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az ml compute show --name <cluster name> -n <workspace> -g <resource group>
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```
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Optionally, you can update the compute cluster to assign a user-assigned managed identity:
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[!INCLUDE [cli v2](includes/machine-learning-cli-v2.md)]
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```azurecli-interactive
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az ml workspace show -w <workspace name> -g <resource group> --query identityPrincipalId
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az ml workspace show -n <workspace name> -g <resource group> --query identityPrincipalId
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```
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1. Grant the Managed Identity Operator role:
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The following command demonstrates how to use the YAML file to create a connection with your workspace. Replace `<yaml file>`, `<workspace name>`, and `<resource group>` with the values for your configuration:
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```azurecli-interactive
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az ml connection create --file <yml file> --resource-group <resource group> --workspace-name <workspace>
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az ml connection create --file <yml file> --resource-group <resource group> --name <workspace>
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```
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1. Once the configuration is complete, you can use the base images from private ACR when building environments for training or inference. The following code snippet demonstrates how to specify the base image ACR and image name in an environment definition:

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