Merge pull request #273222 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: Taojunshen

articles/machine-learning/how-to-setup-customer-managed-keys.md

@@ -32,12 +32,11 @@ In the [customer-managed keys concepts article](concept-customer-managed-keys.md
     | Microsoft.MachineLearningServices | Creating the Azure Machine Learning workspace.
     | Microsoft.Storage | Storage Account is used as the default storage for the workspace.
     | Microsoft.KeyVault |Azure Key Vault is used by the workspace to store secrets.
-    | Microsoft.DocumentDB/databaseAccounts | Azure Cosmos DB instance that logs metadata for the workspace.
-    | Microsoft.Search/searchServices | Azure Search provides indexing capabilities for the workspace.
+    | Microsoft.DocumentDB | Azure Cosmos DB instance that logs metadata for the workspace.
+    | Microsoft.Search | Azure AI Search provides indexing capabilities for the workspace.
 
     For information on registering resource providers, see [Resolve errors for resource provider registration](/azure/azure-resource-manager/templates/error-register-resource-provider).
 
-
 ## Limitations
 
 * After workspace creation, the customer-managed encryption key for resources that the workspace depends on can only be updated to another key in the original Azure Key Vault resource..

articles/managed-grafana/how-to-connect-to-data-source-privately.md

@@ -33,6 +33,8 @@ Managed private endpoints work with Azure services that support private link. Us
 - Azure SQL managed instance
 - Azure SQL server
 - Private link services
+- Azure Databricks
+- Azure Database for PostgreSQL flexible servers
 
 ## Prerequisites
 

articles/virtual-desktop/autoscale-create-assign-scaling-plan.md

@@ -239,9 +239,9 @@ Here's how to create a scaling plan using the Az.DesktopVirtualization PowerShel
             RampDownLoadBalancingAlgorithm = 'BreadthFirst'
             RampDownMinimumHostsPct = '20'
             RampDownCapacityThresholdPct = '20'
-            RampDownForceLogoffUser:$true
+            RampDownForceLogoffUser = $true
             RampDownWaitTimeMinute = '30'
-            RampDownNotificationMessage = '"Log out now, please."'
+            RampDownNotificationMessage = 'Log out now, please.'
             RampDownStopHostsWhen = 'ZeroSessions'
             OffPeakStartTimeHour = '22'
             OffPeakStartTimeMinute = '45'

articles/virtual-machines/windows/compute-benchmark-scores.md

@@ -697,7 +697,7 @@ The following CoreMark benchmark scores show compute performance for select Azur
 
 ## About CoreMark
 
-[CoreMark](https://www.eembc.org/coremark/faq.php) is a benchmark that tests the functionality of a microctronoller (MCU) or central processing unit (CPU). CoreMark isn't system dependent, so it functions the same regardless of the platform (for example, big or little endian, high-end or low-end processor).
+[CoreMark](https://www.eembc.org/coremark/faq.php) is a benchmark that tests the functionality of a microcontroller (MCU) or central processing unit (CPU). CoreMark isn't system dependent, so it functions the same regardless of the platform (for example, big or little endian, high-end or low-end processor).
 
 Windows numbers were computed by running CoreMark on Windows Server 2019. CoreMark was configured with the number of threads set to the number of virtual CPUs, and concurrency set to `PThreads`. The target number of iterations was adjusted based on expected performance to provide a runtime of at least 20 seconds (typically much longer). The final score represents the number of iterations completed divided by the number of seconds it took to run the test. Each test was run at least seven times on each VM. Test run dates shown above. Tests run on multiple VMs across Azure public regions the VM was supported in on the date run.