Merge pull request #212711 from MicrosoftDocs/repo_sync_working_branch

Confirm merge from repo_sync_working_branch to main to sync with https://github.com/MicrosoftDocs/azure-docs (branch main)

Author: rmca14

articles/active-directory/enterprise-users/directory-delete-howto.md

@@ -30,6 +30,8 @@ You can't delete a organization in Azure AD until it passes several checks. Thes
 * There can be no multifactor authentication providers linked to the organization.
 * There can be no subscriptions for any Microsoft Online Services such as Microsoft Azure, Microsoft 365, or Azure AD Premium associated with the organization. For example, if a default Azure AD tenant was created for you in Azure, you can't delete this organization if your Azure subscription still relies on it for authentication. You also can't delete a tenant if another user has associated an Azure subscription with it.
 
+[!NOTE] Microsoft is aware that customers with certain tenant configurations may be unable to successfully delete their Azure AD organization. We are working to address this problem. In the meantime, if needed, you can contact Microsoft support for details about the issue.
+
 ## Delete the organization
 
 1. Sign in to the [Azure AD admin center](https://aad.portal.azure.com) with an account that is the Global Administrator for your organization.

articles/active-directory/standards/memo-22-09-enterprise-wide-identity-management-system.md

@@ -33,18 +33,20 @@ Memorandum 22-09 requires agencies to develop a plan to consolidate their identi
 
 ## Why Azure Active Directory?
 
-Azure Active Directory (Azure AD) provides the capabilities necessary to implement the recommendations from memorandum 22-09. It also provides broad identity controls that support Zero Trust initiatives. If your agency uses Microsoft Office 365, you already have an Azure AD back end to which you can consolidate.
+Azure Active Directory (Azure AD) provides the capabilities necessary to implement the recommendations from memorandum 22-09. It also provides broad identity controls that support Zero Trust initiatives. Today, If your agency uses Microsoft Office 365 or Azure, you already have Azure AD as an identity provider (IdP) and you can connect your applications and resources to Azure AD as your enterprise-wide identity system. 
 
 ## Single sign-on requirements
 
 The memo requires that users sign in once and then directly access applications. Microsoft's robust single sign-on (SSO) capabilities enable users to sign in once and then access cloud and other applications. For more information, see [Azure Active Directory single sign-on](../hybrid/how-to-connect-sso.md).
 
 ## Integration across agencies
 
-[Azure AD B2B collaboration](../external-identities/what-is-b2b.md) helps you meet the requirement to facilitate integration among agencies. It does this by:
+[Azure AD B2B collaboration](../external-identities/what-is-b2b.md) helps you meet the requirement to facilitate integration/collaboration among agencies. Whether the users reside in different Microsoft tenant in the same cloud, [tenant on another microsoft cloud](../external-identities/b2b-government-national-clouds.md), or a [non Azure AD tenant (SAML/WS-Fed identity provider)](..//external-identities/direct-federation.md).
+
+Azure AD cross-tenant access settings allow agencies to manage how they collaborate with other Azure AD organizations and other Microsoft Azure clouds. It does this by:
 
 - Limiting what other Microsoft tenants your users can access.
-- Enabling you to allow access to users whom you don't have to manage in your own tenant, but whom you can subject to your multifactor authentication (MFA) and other access requirements.
+- Granular settings to control access for external users including enforcement of multifactor authentication (MFA) and device signal.
 
 ## Connecting applications
 
@@ -119,6 +121,8 @@ Devices integrated with Azure AD can be either [hybrid joined devices](../device
 
 * [Azure Linux virtual machines](../devices/howto-vm-sign-in-azure-ad-linux.md)
 
+* [Azure Virtual Desktop](https://learn.microsoft.com/azure/architecture/example-scenario/wvd/azure-virtual-desktop-azure-active-directory-join)
+
 * [Virtual desktop infrastructure](../devices/howto-device-identity-virtual-desktop-infrastructure.md)
 
 ## Next steps

articles/analysis-services/analysis-services-manage-users.md

@@ -71,9 +71,13 @@ Roles at this level apply to users or accounts that need to perform tasks that c
 
  By default, when you create a new tabular model project, the model project does not have any roles. Roles can be defined by using the Role Manager dialog box in Visual Studio. When roles are defined during model project design, they are applied only to the model workspace database. When the model is deployed, the same roles are applied to the deployed model. After a model has been deployed, server and database administrators can manage roles and members by using SSMS. To learn more, see [Manage database roles and users](analysis-services-database-users.md).
 
+## Considerations and limitations
+
+* Azure Analysis Services does not support the use of One-Time Password for B2B users
+  
 ## Next steps
 
 [Manage access to resources with Azure Active Directory groups](../active-directory/fundamentals/active-directory-manage-groups.md)   
 [Manage database roles and users](analysis-services-database-users.md)  
 [Manage server administrators](analysis-services-server-admins.md)  
-[Azure role-based access control (Azure RBAC)](../role-based-access-control/overview.md)
+[Azure role-based access control (Azure RBAC)](../role-based-access-control/overview.md)

articles/azure-resource-manager/bicep/bicep-functions-lambda.md

@@ -9,7 +9,7 @@ ms.date: 09/20/2022
 ---
 # Lambda functions for Bicep
 
-This article describes the lambda functions to use in Bicep. Lambda expressions (or lambda functions) are essentially blocks of code that can be passed as an argument. In Bicep, lambda expression is in this format:
+This article describes the lambda functions to use in Bicep. [Lambda expressions (or lambda functions)](https://learn.microsoft.com/dotnet/csharp/language-reference/operators/lambda-expressions) are essentially blocks of code that can be passed as an argument. They can take multiple parameters, but are resticted to a single line of code. In Bicep, lambda expression is in this format:
 
 ```bicep
 <lambda variable> => <expression>

articles/azure-resource-manager/management/overview.md

@@ -84,7 +84,7 @@ There are some important factors to consider when defining your resource group:
 
   The resource group stores metadata about the resources. When you specify a location for the resource group, you're specifying where that metadata is stored. For compliance reasons, you may need to ensure that your data is stored in a particular region.
 
-  If a resource group's region is temporarily unavailable, you can't update resources in the resource group because the metadata is unavailable. The resources in other regions will still function as expected, but you can't update them. This condition doesn't apply to global resources like Azure Content Delivery Network, Azure DNS, Azure Traffic Manager, and Azure Front Door.
+  If a resource group's region is temporarily unavailable, you can't update resources in the resource group because the metadata is unavailable. The resources in other regions will still function as expected, but you can't update them. This condition doesn't apply to global resources like Azure Content Delivery Network, Azure DNS, Azure DNS Private Zones, Azure Traffic Manager, and Azure Front Door.
 
   For more information about building reliable applications, see [Designing reliable Azure applications](/azure/architecture/checklist/resiliency-per-service).
 

articles/azure-vmware/concepts-storage.md

@@ -70,7 +70,7 @@ Now that you've covered Azure VMware Solution storage concepts, you may want to
 
 - [Scale clusters in the private cloud][tutorial-scale-private-cloud] - You can scale the clusters and hosts in a private cloud as required for your application workload. Performance and availability limitations for specific services should be addressed on a case by case basis.
 
-- [Azure NetApp Files with Azure VMware Solution](netapp-files-with-azure-vmware-solution.md) - You can use Azure NetApp Files to migrate and run the most demanding enterprise file-workloads in the cloud: databases, SAP, and high-performance computing applications, with no code changes. Azure NetApp Files volumes can be attached to virtual machines and can also be connected as data stores directly to Azure VMware Solution. This functionality is in preview.
+- [Azure NetApp Files with Azure VMware Solution](netapp-files-with-azure-vmware-solution.md) - You can use Azure NetApp Files to migrate and run the most demanding enterprise file-workloads in the cloud: databases, and general purpose computing applications, with no code changes. Azure NetApp Files volumes can be attached to virtual machines and can also be connected as data stores directly to Azure VMware Solution. This functionality is in preview.
 
 - [vSphere role-based access control for Azure VMware Solution](concepts-identity.md) - You use vCenter Server to manage VM workloads and NSX-T Manager to manage and extend the private cloud. Access and identity management use the CloudAdmin role for vCenter Server and restricted administrator rights for NSX-T Manager.
 

articles/cognitive-services/personalizer/toc.yml

@@ -66,7 +66,7 @@
     displayName: best practice, design
   - name: Personalizer fundamentals
     items:
-    - name: "Features Action and Context"
+    - name: "Context and Action Features"
       href: concepts-features.md
       displayName: action, context, feature, namespace, JSON, best practice, set, ID, guid, event id,
     - name: Learning policy
@@ -98,11 +98,11 @@
     href: concepts-scalability-performance.md
 - name: Tutorials
   items:
-  - name: Use Web app
+  - name: Web app integration tutorial
     href: tutorial-use-personalizer-web-app.md
-  - name: Use Chat bot
+  - name: Chat bot integration tutorial
     href: tutorial-use-personalizer-chat-bot.md
-  - name: Use Azure Notebook
+  - name: Jupyter notebook walkthrough
     href: tutorial-use-azure-notebook-generate-loop-data.md
     displayName: jupyter, notebook, python, cell, graph, chart, aggregate, model update frequency
 - name: Reference

articles/event-grid/communication-services-voice-video-events.md

@@ -246,22 +246,22 @@ This section contains an example of what that data would look like for each even
   {
     "id": "d5546be8-227a-4db8-b2c3-4f06fd675fd6",
     "topic": "/subscriptions/{subscription-id}/resourcegroups/{group-name}/providers/microsoft.communication/communicationservices/{communication-services-resource-name}",
-    "subject": "/caller/4:+16041234567/recipient/8:acs:ff4181e1-324c-4cd1-9c4f-bda3e5d348f5_00000000-0000-0000-0000-000000000000",
+    "subject": "/caller/8:acs:98e4cbef-70e7-4733-8594-063c4a72d711_00000014-2033-438d-1000-343a0d006e10/recipient/8:acs:98e4cbef-70e7-4733-8594-063c4a72d711_00000014-1889-f3a7-6a0b-343a0d0061f3",
     "data": {
-      "to": {
-        "kind": "PhoneNumber",
-        "rawId": "4:+18331234567",
-        "phoneNumber": {
-          "value": "+18331234567"
-          }
-        },
+	  "to": {
+       "kind": "communicationUser",
+        "rawId": "8:acs:98e4cbef-70e7-4733-8594-063c4a72d711_00000014-1889-f3a7-6a0b-343a0d0061f3",
+        "communicationUser": {
+          "id": "8:acs:98e4cbef-70e7-4733-8594-063c4a72d711_00000014-1889-f3a7-6a0b-343a0d0061f3"
+        }
+	  },
       "from": {
-        "kind": "PhoneNumber",
-        "rawId": "4:+16041234567",
-        "phoneNumber": {
-          "value": "+16041234567"
-          }
-        },
+        "kind": "communicationUser",
+        "rawId": "8:acs:98e4cbef-70e7-4733-8594-063c4a72d711_00000014-2033-438d-1000-343a0d006e10",
+        "communicationUser": {
+          "id": "8:acs:98e4cbef-70e7-4733-8594-063c4a72d711_00000014-2033-438d-1000-343a0d006e10"
+		  }
+      },
       "callerDisplayName": "",
       "incomingCallContext": "eyJhbGciOiJub25lIiwidHliSldUIn0.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.",
       "correlationId": "d732db64-4803-462d-be9c-518943ea2b7a"

articles/machine-learning/how-to-datastore.md

@@ -124,19 +124,20 @@ ml_client.create_or_update(store)
 
 ```python
 from azure.ai.ml.entities import AzureBlobDatastore
+from azure.ai.ml.entities._datastore.credentials import AccountKeyCredentials
 from azure.ai.ml import MLClient
 
 ml_client = MLClient.from_config()
 
 store = AzureBlobDatastore(
-    name="blob-protocol-example",
+    name="blob_protocol_example",
     description="Datastore pointing to a blob container using wasbs protocol.",
     account_name="mytestblobstore",
     container_name="data-container",
     protocol="wasbs",
-    credentials={
-        "account_key": "XXXxxxXXXxXXXXxxXXXXXxXXXXXxXxxXxXXXxXXXxXXxxxXXxxXXXxXxXXXxxXxxXXXXxxxxxXXxxxxxxXXXxXXX"
-    },
+    credentials=AccountKeyCredentials(
+        account_key="XXXxxxXXXxXXXXxxXXXXXxXXXXXxXxxXxXXXxXXXxXXxxxXXxxXXXxXxXXXxxXxxXXXXxxxxxXXxxxxxxXXXxXXX"
+    ),
 )
 
 ml_client.create_or_update(store)
@@ -146,18 +147,19 @@ ml_client.create_or_update(store)
 
 ```python
 from azure.ai.ml.entities import AzureBlobDatastore
+from azure.ai.ml.entities._datastore.credentials import SasTokenCredentials
 from azure.ai.ml import MLClient
 
 ml_client = MLClient.from_config()
 
 store = AzureBlobDatastore(
-    name="blob-sas-example",
+    name="blob_sas_example",
     description="Datastore pointing to a blob container using SAS token.",
     account_name="mytestblobstore",
     container_name="data-container",
-    credentials={
-        "sas_token": "?xx=XXXX-XX-XX&xx=xxxx&xxx=xxx&xx=xxxxxxxxxxx&xx=XXXX-XX-XXXXX:XX:XXX&xx=XXXX-XX-XXXXX:XX:XXX&xxx=xxxxx&xxx=XXxXXXxxxxxXXXXXXXxXxxxXXXXXxxXXXXXxXXXXxXXXxXXxXX"
-    },
+    credentials=SasTokenCredentials(
+        sas_token= "?xx=XXXX-XX-XX&xx=xxxx&xxx=xxx&xx=xxxxxxxxxxx&xx=XXXX-XX-XXXXX:XX:XXX&xx=XXXX-XX-XXXXX:XX:XXX&xxx=xxxxx&xxx=XXxXXXxxxxxXXXXXXXxXxxxXXXXXxxXXXXXxXXXXxXXXxXXxXX"
+    ),
 )
 
 ml_client.create_or_update(store)
@@ -230,20 +232,22 @@ ml_client.create_or_update(store)
 
 ```python
 from azure.ai.ml.entities import AzureDataLakeGen2Datastore
+from azure.ai.ml.entities._datastore.credentials import ServicePrincipalCredentials
+
 from azure.ai.ml import MLClient
 
 ml_client = MLClient.from_config()
 
 store = AzureDataLakeGen2Datastore(
-    name="adls-gen2-example",
+    name="adls_gen2_example",
     description="Datastore pointing to an Azure Data Lake Storage Gen2.",
     account_name="mytestdatalakegen2",
     filesystem="my-gen2-container",
-    credentials={
-        "tenant_id": "XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX",
-        "client_id": "XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX",
-        "client_secret": "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX",
-    },
+     credentials=ServicePrincipalCredentials(
+        tenant_id= "XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX",
+        client_id= "XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX",
+        client_secret= "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX",
+    ),
 )
 
 ml_client.create_or_update(store)
@@ -298,18 +302,19 @@ az ml datastore create --file my_files_datastore.yml
 
 ```python
 from azure.ai.ml.entities import AzureFileDatastore
+from azure.ai.ml.entities._datastore.credentials import AccountKeyCredentials
 from azure.ai.ml import MLClient
 
 ml_client = MLClient.from_config()
 
 store = AzureFileDatastore(
-    name="file-example",
+    name="file_example",
     description="Datastore pointing to an Azure File Share.",
     account_name="mytestfilestore",
     file_share_name="my-share",
-    credentials={
-        "account_key": "XXXxxxXXXxXXXXxxXXXXXxXXXXXxXxxXxXXXxXXXxXXxxxXXxxXXXxXxXXXxxXxxXXXXxxxxxXXxxxxxxXXXxXXX"
-    },
+    credentials=AccountKeyCredentials(
+        account_key= "XXXxxxXXXxXXXXxxXXXXXxXXXXXxXxxXxXXXxXXXxXXxxxXXxxXXXxXxXXXxxXxxXXXXxxxxxXXxxxxxxXXXxXXX"
+    ),
 )
 
 ml_client.create_or_update(store)
@@ -319,18 +324,19 @@ ml_client.create_or_update(store)
 
 ```python
 from azure.ai.ml.entities import AzureFileDatastore
+from azure.ai.ml.entities._datastore.credentials import SasTokenCredentials
 from azure.ai.ml import MLClient
 
 ml_client = MLClient.from_config()
 
 store = AzureFileDatastore(
-    name="file-sas-example",
+    name="file_sas_example",
     description="Datastore pointing to an Azure File Share using SAS token.",
     account_name="mytestfilestore",
     file_share_name="my-share",
-    credentials={
-        "sas_token": "?xx=XXXX-XX-XX&xx=xxxx&xxx=xxx&xx=xxxxxxxxxxx&xx=XXXX-XX-XXXXX:XX:XXX&xx=XXXX-XX-XXXXX:XX:XXX&xxx=xxxxx&xxx=XXxXXXxxxxxXXXXXXXxXxxxXXXXXxxXXXXXxXXXXxXXXxXXxXX"
-    },
+    credentials=SasTokenCredentials(
+        sas_token="?xx=XXXX-XX-XX&xx=xxxx&xxx=xxx&xx=xxxxxxxxxxx&xx=XXXX-XX-XXXXX:XX:XXX&xx=XXXX-XX-XXXXX:XX:XXX&xxx=xxxxx&xxx=XXxXXXxxxxxXXXXXXXxXxxxXXXXXxxXXXXXxXXXXxXXXxXXxXX"
+    ),
 )
 
 ml_client.create_or_update(store)
@@ -400,19 +406,20 @@ ml_client.create_or_update(store)
 
 ```python
 from azure.ai.ml.entities import AzureDataLakeGen1Datastore
+from azure.ai.ml.entities._datastore.credentials import ServicePrincipalCredentials
 from azure.ai.ml import MLClient
 
 ml_client = MLClient.from_config()
 
 store = AzureDataLakeGen1Datastore(
-    name="adls-gen1-example",
+    name="adls_gen1_example",
     description="Datastore pointing to an Azure Data Lake Storage Gen1.",
     store_name="mytestdatalakegen1",
-    credentials={
-        "tenant_id": "XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX",
-        "client_id": "XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX",
-        "client_secret": "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX",
-    },
+    credentials=ServicePrincipalCredentials(
+        tenant_id= "XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX",
+        client_id= "XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX",
+        client_secret= "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX",
+    ),
 )
 
 ml_client.create_or_update(store)