Move MCR restriction under relevant section

Author: dem108

articles/machine-learning/how-to-secure-online-endpoint.md

@@ -62,8 +62,6 @@ The following diagram shows how communications flow through private endpoints to
 
 * If your Azure Machine Learning workspace has a private endpoint that was created before May 24, 2022, you must recreate the workspace's private endpoint before configuring your online endpoints to use a private endpoint. For more information on creating a private endpoint for your workspace, see [How to configure a private endpoint for Azure Machine Learning workspace](how-to-configure-private-link.md).
 
-* For online deployments with egress public network access parameter set to disabled, access from the deployments to Microsoft Container Registry (MCR) is restricted. If you want to leverage container images from MCR (such as when using curated environment or mlflow no-code deployment), recommendation is to push the images into the Azure Container Registry (ACR) which is attached with the workspace. The images in this ACR is accessible to secured deployments via the private endpoints which are automatically created on behalf of you when you set egress public network access parameter to disabled. For a quick example, please refer to this [custom container example](https://github.com/Azure/azureml-examples/tree/main/cli/endpoints/online/custom-container/minimal/single-model).
-
 * Secure outbound communication creates three private endpoints per deployment. One to the Azure Blob storage, one to the Azure Container Registry, and one to your workspace.
 
 * When you use network isolation with a deployment, Azure Log Analytics is partially supported. All metrics and the `AMLOnlineEndpointTrafficLog` table are supported via Azure Log Analytics. `AMLOnlineEndpointConsoleLog` and `AMLOnlineEndpointEventLog` tables are currently not supported. As a workaround, you can use the [az ml online-deployment get_logs](/cli/azure/ml/online-deployment#az-ml-online-deployment-get-logs) CLI command, the [OnlineDeploymentOperations.get_logs()](/python/api/azure-ai-ml/azure.ai.ml.operations.onlinedeploymentoperations#azure-ai-ml-operations-onlinedeploymentoperations-get-logs) Python SDK, or the Deployment log tab in the Azure Machine Learning studio instead. For more information, see [Monitoring online endpoints](how-to-monitor-online-endpoints.md).
@@ -119,11 +117,14 @@ When `public_network_access` is `Disabled`, inbound scoring requests are receive
 
 ## Outbound (resource access)
 
-To restrict communication between a deployment and external resources, including the Azure resources it uses, set the deployment's `egress_public_network_access` flag to `disabled`. Use this flag to ensure that the download of the model, code, and images needed by your deployment are secured with a private endpoint. Note that disabling the flag alone is not enough—your workspace must also have a private link that allows access to Azure resources via a private endpoint. See the [Prerequisites](#prerequisites) for more details.
+To restrict communication between a deployment and external resources, including the Azure resources it uses, set the deployment's `egress_public_network_access` flag to `disabled`. Use this flag to ensure that the download of the model, code, and images needed by your deployment are secured with a private endpoint. Note that disabling the flag alone is not enough — your workspace must also have a private link that allows access to Azure resources via a private endpoint. See the [Prerequisites](#prerequisites) for more details.
 
 > [!WARNING]
 > You cannot update (enable or disable) the `egress_public_network_access` flag after creating the deployment. Attempting to change the flag while updating the deployment will fail with an error.
 
+> [!NOTE]
+> For online deployments with `egress_public_network_access` flag set to `disabled`, access from the deployments to Microsoft Container Registry (MCR) is restricted. If you want to leverage container images from MCR (such as when using curated environment or mlflow no-code deployment), recommendation is to push the images into the Azure Container Registry (ACR) which is attached with the workspace. The images in this ACR is accessible to secured deployments via the private endpoints which are automatically created on behalf of you when you set `egress_public_network_access` flag to `disabled`. For a quick example, please refer to this [custom container example](https://github.com/Azure/azureml-examples/tree/main/cli/endpoints/online/custom-container/minimal/single-model).
+
 # [Azure CLI](#tab/cli)
 
 ```azurecli