Merge pull request #180007 from MicrosoftDocs/repo_sync_working_branch

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

Author: huypub

articles/active-directory/saas-apps/benq-iam-provisioning-tutorial.md

@@ -16,7 +16,7 @@ ms.author: thwimmer
 
 # Tutorial: Configure BenQ IAM for automatic user provisioning
 
-This tutorial describes the steps you need to perform in both BenQ IAM and Azure Active Directory (Azure AD) to configure automatic user provisioning. When configured, Azure AD automatically provisions and de-provisions users and groups to [BenQ IAM](https://service-portaltest.benq.com/login) using the Azure AD Provisioning service. For important details on what this service does, how it works, and frequently asked questions, see [Automate user provisioning and deprovisioning to SaaS applications with Azure Active Directory](../app-provisioning/user-provisioning.md). 
+This tutorial describes the steps you need to perform in both BenQ IAM and Azure Active Directory (Azure AD) to configure automatic user provisioning. When configured, Azure AD automatically provisions and de-provisions users and groups to [BenQ IAM](https://service-portal.benq.com/login) using the Azure AD Provisioning service. For important details on what this service does, how it works, and frequently asked questions, see [Automate user provisioning and deprovisioning to SaaS applications with Azure Active Directory](../app-provisioning/user-provisioning.md). 
 
 
 ## Supported capabilities

articles/azure-arc/data/create-data-controller-direct-prerequisites.md

@@ -30,7 +30,8 @@ To connect your kubernetes cluster to Azure, use Azure CLI `az` with the followi
 
 ### Install tools
 
-- Install or upgrade to the latest version of Azure CLI ([install](/sql/azdata/install/deploy-install-azdata)) 
+- Helm version 3.3+ ([install](https://helm.sh/docs/intro/install/))
+- Install or upgrade to the latest version of Azure CLI ([download](https://aka.ms/installazurecliwindows))
 
 ### Add extensions for Azure CLI
 

articles/azure-sql/database/recovery-using-backups.md

@@ -207,6 +207,9 @@ You can also use Azure PowerShell or the REST API for recovery. The following ta
 > [!IMPORTANT]
 > The PowerShell Azure Resource Manager module is still supported by SQL Database and SQL Managed Instance, but all future development is for the Az.Sql module. For these cmdlets, see [AzureRM.Sql](/powershell/module/AzureRM.Sql/). Arguments for the commands in the Az module and in Azure Resource Manager modules are to a great extent identical.
 
+> [!NOTE]
+> Restore points represent a period between the earliest restore point and the latest log backup point. Information on latest restore point is currently unavailable on Azure PowerShell.
+
 #### SQL Database
 
 To restore a standalone or pooled database, see [Restore-AzSqlDatabase](/powershell/module/az.sql/restore-azsqldatabase).

articles/cosmos-db/free-tier.md

@@ -25,7 +25,7 @@ You can have up to one free tier Azure Cosmos DB account per an Azure subscripti
 In shared throughput model, when you provision throughput on a database, the throughput is shared across all the containers in the database. When using the free tier, you can provision a shared database with up to 1000 RU/s for free. All containers in the database will share the throughput. 
 
 Just like the regular account, in the free tier account, a shared throughput database can have a max of 25 containers. 
-Any additional databases with shared throughput or containers with dedicated throughput beyond 1000 RU/s are billed at the regular pricing. In a free tier account, you can create a max of 5 shared throughput databases.
+Any additional databases with shared throughput or containers with dedicated throughput beyond 1000 RU/s are billed at the regular pricing.
 
 ## Free tier with Azure discount
 

articles/cosmos-db/sql/index-metrics.md

@@ -131,7 +131,7 @@ Index Utilization Information
     Index Impact Score: High
     ---
 ```
-These index metrics show that the query used the indexed paths `/name/?`, `/age/?`, `/town/?`, and `/timestamp/?`. The index metrics also indicate that there's a high likelihood that adding the composite indexes (`/name` ASC, `(/town ASC, /age ASC)` and `(/name ASC, /town ASC, /timestamp ASC)` will further improve performance.
+These index metrics show that the query used the indexed paths `/name/?`, `/age/?`, `/town/?`, and `/timestamp/?`. The index metrics also indicate that there's a high likelihood that adding the composite indexes `(/name ASC, /town ASC, /age ASC)` and `(/name ASC, /town ASC, /timestamp ASC)` will further improve performance.
 
 ## Next steps
 

articles/data-factory/author-global-parameters.md

@@ -109,5 +109,5 @@ Set-AzDataFactoryV2 -InputObject $dataFactory -Force
 
 ## Next steps
 
-* Learn about Azure Data Factory's [continuous integration and deployment process](continuous-integration-delivery.md)
-* Learn how to use the [control flow expression language](control-flow-expression-language-functions.md)
+* Learn about Azure Data Factory's [continuous integration and deployment process](continuous-integration-delivery-improvements.md)
+* Learn how to use the [control flow expression language](control-flow-expression-language-functions.md)

articles/postgresql/howto-connect-with-managed-identity.md

@@ -39,7 +39,7 @@ Retrieve the application ID for the system-assigned managed identity, which you'
 
 ```azurecli
 # Get the client ID (application ID) of the system-assigned managed identity
-az ad sp list --display-name obs-locdev-wus2 --query [*].appId --out tsv
+az ad sp list --display-name vm-name --query [*].appId --out tsv
 ```
 
 ## Creating a PostgreSQL user for your Managed Identity
@@ -98,12 +98,10 @@ namespace Driver
 {
     class Script
     {
-        // Obtain connection string information from the portal
-        //
+        // Obtain connection string information from the portal for use in the following variables
         private static string Host = "HOST";
         private static string User = "USER";
         private static string Database = "DATABASE";
-        //private static string ClientId = "CLIENT_ID";
 
         static async Task Main(string[] args)
         {

articles/synapse-analytics/machine-learning/tutorial-score-model-predict-spark-pool.md

@@ -51,7 +51,7 @@ Make sure all prerequisites are in place before following these steps for using
 
 1. **Import libraries:** Import the following libraries to use PREDICT in spark session.
 
-   ```PYSPARK
+   ```python
       #Import libraries
       from pyspark.sql.functions import col, pandas_udf,udf,lit
       from azureml.core import Workspace
@@ -65,7 +65,7 @@ Make sure all prerequisites are in place before following these steps for using
    > [!NOTE]
    > Before running this script, update it with the URI for ADLS Gen2 data file along with model output return data type and ADLS/AML URI for the model file.
 
-   ```PYSPARK
+   ```python
       #Set input data path
       DATA_FILE = "abfss://<filesystemname>@<account name>.dfs.core.windows.net/<file path>"
 
@@ -90,7 +90,7 @@ Make sure all prerequisites are in place before following these steps for using
 
    - **Through service principal:** You can use service principal client ID and secret directly to authenticate to AML workspace. Service principal must have "Contributor" access to the AML workspace.
 
-      ```PYSPARK
+      ```python
          #AML workspace authentication using service principal
          AZURE_TENANT_ID = "<tenant_id>"
          AZURE_CLIENT_ID = "<client_id>"
@@ -116,15 +116,15 @@ Make sure all prerequisites are in place before following these steps for using
 
    - **Through linked service:** You can use linked service to authenticate to AML workspace. Linked service can use "service principal" or Synapse workspace's "Managed Service Identity (MSI)" for authentication. "Service principal" or "Managed Service Identity (MSI)" must have "Contributor" access to the AML workspace.
 
-      ```PYSPARK
+      ```python
          #AML workspace authentication using linked service
          from notebookutils.mssparkutils import azureML
          ws = azureML.getWorkspace("<linked_service_name>") #   "<linked_service_name>" is the linked service name, not AML workspace name. Also, linked   service supports MSI and service principal both
       ```
 
 4. **Enable PREDICT in spark session:** Set the spark configuration `spark.synapse.ml.predict.enabled` to `true` to enable the library.
 
-   ```PYSPARK
+   ```python
       #Enable SynapseML predict
       spark.conf.set("spark.synapse.ml.predict.enabled","true")
    ```
@@ -134,7 +134,7 @@ Make sure all prerequisites are in place before following these steps for using
    > [!NOTE]
    > Update model alias and model uri in this script before running it.
 
-   ```PYSPARK
+   ```python
       #Bind model within Spark session
        model = pcontext.bind_model(
         return_types=RETURN_TYPES, 
@@ -150,7 +150,7 @@ Make sure all prerequisites are in place before following these steps for using
    > [!NOTE]
    > Update view name in this script before running it.
 
-   ```PYSPARK
+   ```python
       #Read data from ADLS
       df = spark.read \
        .format("csv") \
@@ -165,7 +165,7 @@ Make sure all prerequisites are in place before following these steps for using
    > [!NOTE]
    > Update the model alias name, view name, and comma separated model input column name in this script before running it. Comma separated model input columns are the same as those used while training the model.
 
-   ```PYSPARK
+   ```python
       #Call PREDICT using Spark SQL API
 
       predictions = spark.sql(
@@ -177,15 +177,15 @@ Make sure all prerequisites are in place before following these steps for using
                  ).show()
    ```
 
-   ```PYSPARK
+   ```python
       #Call PREDICT using user defined function (UDF)
 
       df = df[<comma_separated_model_input_column_name>] # for ex. df["empid","empname"]
 
       df.withColumn("PREDICT",model.udf(lit("<random_alias_name>"),*df.columns)).show()
    ```
 
-   ```PYSPARK
+   ```python
       #Call PREDICT using Transformer API
 
       columns = [<comma_separated_model_input_column_name>] # for ex. df["empid","empname"]
@@ -199,7 +199,7 @@ Make sure all prerequisites are in place before following these steps for using
 
 1. Import libraries and read the training dataset from ADLS.
 
-   ```PYSPARK
+   ```python
       # Import libraries and read training dataset from ADLS
 
       import fsspec
@@ -217,7 +217,7 @@ Make sure all prerequisites are in place before following these steps for using
 
 1. Train model and generate mlflow artifacts.
 
-   ```PYSPARK
+   ```python
       # Train model and generate mlflow artifacts
 
       import os
@@ -309,7 +309,7 @@ Make sure all prerequisites are in place before following these steps for using
 
 1. Store model MLFLOW artifacts in ADLS or register in AML.
 
-   ```PYSPARK
+   ```python
       # Store model MLFLOW artifacts in ADLS
 
       STORAGE_PATH = 'abfs[s]://<container>/<path-to-store-folder>'
@@ -328,7 +328,7 @@ Make sure all prerequisites are in place before following these steps for using
           recursive=True, overwrite=True)
    ```
 
-   ```PYSPARK
+   ```python
       # Register model MLFLOW artifacts in AML
 
       from azureml.core import Workspace, Model
@@ -364,7 +364,7 @@ Make sure all prerequisites are in place before following these steps for using
 
 1. Set required parameters using variables.
 
-   ```PYSPARK
+   ```python
       # If using ADLS uploaded model
 
       import pandas as pd
@@ -379,7 +379,7 @@ Make sure all prerequisites are in place before following these steps for using
       RUNTIME = "mlflow"
    ```
 
-   ```PYSPARK
+   ```python
       # If using AML registered model
 
       from pyspark.sql.functions import col, pandas_udf,udf,lit
@@ -396,13 +396,13 @@ Make sure all prerequisites are in place before following these steps for using
 
 1. Enable SynapseML PREDICT functionality in spark session.
 
-   ```PYSPARK
+   ```python
       spark.conf.set("spark.synapse.ml.predict.enabled","true")
    ```
 
 1. Bind model in spark session.
 
-   ```PYSPARK
+   ```python
       # If using ADLS uploaded model
 
       model = pcontext.bind_model(
@@ -413,7 +413,7 @@ Make sure all prerequisites are in place before following these steps for using
        ).register()
    ```
 
-   ```PYSPARK
+   ```python
       # If using AML registered model
 
       model = pcontext.bind_model(
@@ -427,7 +427,7 @@ Make sure all prerequisites are in place before following these steps for using
 
 1. Load test data from ADLS.
 
-   ```PYSPARK
+   ```python
       # Load data from ADLS
 
       df = spark.read \
@@ -442,7 +442,7 @@ Make sure all prerequisites are in place before following these steps for using
 
 1. Call PREDICT to generate the score.
 
-   ```PYSPARK
+   ```python
       # Call PREDICT
 
       predictions = spark.sql(

articles/virtual-desktop/app-attach-faq.yml

@@ -106,7 +106,7 @@ sections:
           - Antivirus programs
 
       - question: |
-          How many MISX applications can I add to each session host?
+          How many MSIX applications can I add to each session host?
         answer: |
           Each session host has different limits based on their CPU, memory, and OS. Going over these limits can affect application performance and overall user experience. However, MSIX app attach itself has no limit on how many applications it can use.