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Merge pull request #120100 from lebaro-msft/patch-2
Update configure-managed-network.md
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articles/ai-studio/how-to/configure-managed-network.md

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@@ -241,7 +241,9 @@ To allow installation of __Python packages for training and deployment__, add ou
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| `*.tensorflow.org` | Used by some examples based on Tensorflow. |
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### Scenario: Use Visual Studio Code
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Visual Studio Code relies on specific hosts and ports to establish a remote connection.
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#### Hosts
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If you plan to use __Visual Studio Code__ with Azure AI, add outbound _FQDN_ rules to allow traffic to the following hosts:
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> [!WARNING]
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* `pkg-containers.githubusercontent.com`
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* `github.com`
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#### Ports
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You must allow network traffic to ports 8704 to 8710. The VS Code server dynamically selects the first available port within this range.
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### Scenario: Use HuggingFace models
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If you plan to use __HuggingFace models__ with Azure AI, add outbound _FQDN_ rules to allow traffic to the following hosts:
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* Managed VNet uses private endpoint connection to access your private resources. You can't have a private endpoint and a service endpoint at the same time for your Azure resources, such as a storage account. We recommend using private endpoints in all scenarios.
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* The managed VNet is deleted when the Azure AI is deleted.
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* Data exfiltration protection is automatically enabled for the only approved outbound mode. If you add other outbound rules, such as to FQDNs, Microsoft can't guarantee that you're protected from data exfiltration to those outbound destinations.
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* Using FQDN outbound rules increases the cost of the managed VNet because FQDN rules use Azure Firewall. For more information, see [Pricing](#pricing).
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* Using FQDN outbound rules increases the cost of the managed VNet because FQDN rules use Azure Firewall. For more information, see [Pricing](#pricing).

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