fix: review

Author: santiagxf

articles/ai-foundry/model-inference/includes/configure-entra-id/about-credentials.md

@@ -1,3 +1,12 @@
+---
+manager: nitinme
+author: mrbullwinkle
+ms.author: fasantia 
+ms.service: azure-ai-model-inference
+ms.date: 01/23/2025
+ms.topic: include
+---
+
 ### Options for credential when using Microsoft Entra ID
 
 `DefaultAzureCredential` is an opinionated, ordered sequence of mechanisms for authenticating to Microsoft Entra ID. Each authentication mechanism is a class derived from the `TokenCredential` class and is known as a credential. At runtime, `DefaultAzureCredential` attempts to authenticate using the first credential. If that credential fails to acquire an access token, the next credential in the sequence is attempted, and so on, until an access token is successfully obtained. In this way, your app can use different credentials in different environments without writing environment-specific code.
@@ -11,6 +20,6 @@ When deployed to Azure, this same code can also authenticate your app to other A
 * Use deterministic credentials in production environments: Strongly consider moving from `DefaultAzureCredential` to one of the following deterministic solutions on production environments:
 
   * A specific `TokenCredential` implementation, such as `ManagedIdentityCredential`. See the [Derived list for options](/dotnet/api/azure.core.tokencredential#definition).
-  * A pared-down `ChainedTokenCredential` implementation optimized for the Azure environment in which your app runs. `ChainedTokenCredential` essentially creates a specific allow-list of acceptable credential options, such as `ManagedIdentity` for production and `VisualStudioCredential` for development.
+  * A pared-down `ChainedTokenCredential` implementation optimized for the Azure environment in which your app runs. `ChainedTokenCredential` essentially creates a specific allowlist of acceptable credential options, such as `ManagedIdentity` for production and `VisualStudioCredential` for development.
 
 * Configure system-assigned or user-assigned managed identities to the Azure resources where your code is running if possible. Configure Microsoft Entra ID access to those specific identities. 

articles/ai-foundry/model-inference/includes/configure-entra-id/intro.md

@@ -1,3 +1,12 @@
+---
+manager: nitinme
+author: mrbullwinkle
+ms.author: fasantia 
+ms.service: azure-ai-model-inference
+ms.date: 01/23/2025
+ms.topic: include
+---
+
 [!INCLUDE [Feature preview](../../../../ai-studio/includes/feature-preview.md)]
 
 Models deployed to Azure AI model inference in Azure AI Services support key-less authorization using Microsoft Entra ID. Key-less authorization enhances security, simplifies the user experience, reduces operational complexity, and provides robust compliance support for modern development. It makes it a strong choice for organizations adopting secure and scalable identity management solutions.

articles/ai-foundry/model-inference/includes/configure-entra-id/portal.md

@@ -12,7 +12,7 @@ zone_pivot_groups: azure-ai-models-deployment
 
 ## Configure Microsoft Entra ID for inference
 
-Follow these steps to configure Microsoft Entra ID for inference if you're using **projects or hubs** in Azure AI Foundry. If your are not using them, Start from step 5 using the Azure portal.
+Follow these steps to configure Microsoft Entra ID for inference: 
 
 1. Go to the [Azure portal](https://portal.azure.com) and locate the Azure AI Services resource you're using. If you're using Azure AI Foundry with projects or hubs, you can navigate to it by: