revising slightly

Author: Larry Franks

articles/machine-learning/how-to-access-azureml-behind-firewall.md

@@ -79,7 +79,7 @@ __Outbound traffic__
 | `AzureFrontDoor.FrontEnd`</br>* Not needed in Azure China. | 443 | Global entry point for [Azure Machine Learning studio](https://ml.azure.com). Store images and environments for AutoML. |
 | `MicrosoftContainerRegistry.<region>` | 443 | Access docker images provided by Microsoft. |
 | `Frontdoor.FirstParty` | 443 | Access docker images provided by Microsoft. |
-| `AzureMonitor` | 443 | Used to log monitoring and metrics to Azure Monitor. Only needed if you haven't [secured Azure Monitor](how-to-secure-workspace-vnet.md#secure-azure-monitor-and-application-insights) for the workspace. |
+| `AzureMonitor` | 443 | Used to log monitoring and metrics to Azure Monitor. Only needed if you haven't [secured Azure Monitor](how-to-secure-workspace-vnet.md#secure-azure-monitor-and-application-insights) for the workspace. </br>* This outbound is also used to log information for support incidents. |
 
 > [!IMPORTANT]
   > If a compute instance or compute cluster is configured for no public IP, they can't access the public internet by default. However, they do need to communicate with the resources listed above. To enable outbound communication, you have two possible options:

includes/machine-learning-public-internet-access.md

@@ -34,7 +34,7 @@ Azure Machine Learning requires both inbound and outbound access to the public i
 | Inbound | TCP: 44224 | `AzureMachineLearning` | Create, update, and delete of Azure Machine Learning compute instance/cluster. **Required if instance/cluster configured with a Public IP option.**|
 | Outbound | TCP: 8787 | `AzureMachineLearning` | Using Azure Machine Learning services.<br> **Port 8787 is required if you use RStudio.** |
 | Outbound | TCP: 445 | `Storage.region` | Access data stored in the Azure Storage Account for compute cluster and compute instance. For information on preventing data exfiltration over this outbound, see [Data exfiltration protection](../articles/machine-learning/how-to-prevent-data-loss-exfiltration.md).<br>**445 is only required if you have a firewall between your virtual network for Azure ML and a private endpoint for your storage accounts.**|
-| Outbound | TCP: 443 | `AzureMonitor` | Used to log monitoring and metrics to App Insights and Azure Monitor. Only needed if you haven't [secured Azure Monitor](../articles/machine-learning/how-to-secure-workspace-vnet.md#secure-azure-monitor-and-application-insights) for the workspace. |
+| Outbound | TCP: 443 | `AzureMonitor` | Used to log monitoring and metrics to App Insights and Azure Monitor. Only needed if you haven't [secured Azure Monitor](../articles/machine-learning/how-to-secure-workspace-vnet.md#secure-azure-monitor-and-application-insights) for the workspace. </br>* This outbound is also used to log information for support incidents. |
 | Outbound | TCP: 443 | `Keyvault.region` | Access the key vault for the Azure Batch service. Only needed if you enabled the [hbi_workspace](/python/api/azureml-core/azureml.core.workspace%28class%29#create-name--auth-none--subscription-id-none--resource-group-none--location-none--create-resource-group-true--sku--basic---friendly-name-none--storage-account-none--key-vault-none--app-insights-none--container-registry-none--cmk-keyvault-none--resource-cmk-uri-none--hbi-workspace-false--default-cpu-compute-target-none--default-gpu-compute-target-none--exist-ok-false--show-output-true-) flag when creating the workspace. |
 
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