Merging changes synced from https://github.com/MicrosoftDocs/azure-docs-pr (branch live)

Author: Banani-Rath

.openpublishing.redirection.azure-monitor.json

@@ -10,11 +10,6 @@
             "redirect_url": "/azure/azure-monitor/app/performance-counters",
             "redirect_document_id": true
         },
-        {
-            "source_path_from_root": "/articles/application-insights/cloudservices.md",
-            "redirect_url": "/azure/azure-monitor/app/azure-web-apps-net",
-            "redirect_document_id": false
-        },
         {
             "source_path_from_root": "/articles/azure-monitor/insights/service-bus-insights.md",
             "redirect_url": "/azure/service-bus-messaging/service-bus-insights",

articles/active-directory/authentication/concept-fido2-hardware-vendor.md

@@ -29,8 +29,6 @@ You can become a Microsoft-compatible FIDO2 security key vendor through the foll
         - Receive an overview of the device from the vendor
     - Microsoft will share our test scripts with you. Our engineering team will be able to answer questions if you have any specific needs.
     - You will complete and send all passed results to Microsoft Engineering team
-    - Once Microsoft confirms, you will send multiple hardware/solution samples of each device to Microsoft Engineering team
-        - Upon receipt Microsoft Engineering team will conduct test script verification and user experience flow
 4. Upon successful passing of all tests by Microsoft Engineering team, Microsoft will confirm vendor's device is listed in [the FIDO MDS](https://fidoalliance.org/metadata/).
 5. Microsoft will add your FIDO2 Security Key on Azure AD backend and to our list of approved FIDO2 vendors.
 

articles/active-directory/enterprise-users/licensing-service-plan-reference.md

@@ -0,0 +1,107 @@
+---
+title: 'Quickstart: Direct web traffic using Bicep'
+titleSuffix: Azure Application Gateway
+description: In this quickstart, you learn how to use Bicep to create an Azure Application Gateway that directs web traffic to virtual machines in a backend pool.
+services: application-gateway
+author: schaffererin
+ms.author: v-eschaffer
+ms.date: 04/14/2022
+ms.topic: quickstart
+ms.service: application-gateway
+ms.custom: devx-track-azurepowershell, mvc, subject-armqs, mode-arm
+---
+
+# Quickstart: Direct web traffic with Azure Application Gateway - Bicep
+
+In this quickstart, you use Bicep to create an Azure Application Gateway. Then you test the application gateway to make sure it works correctly.
+
+[!INCLUDE [About Bicep](../../includes/resource-manager-quickstart-bicep-introduction.md)]
+
+## Prerequisites
+
+- An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/free/?WT.mc_id=A261C142F).
+
+## Review the Bicep file
+
+This Bicep file creates a simple setup with a public front-end IP address, a basic listener to host a single site on the application gateway, a basic request routing rule, and two virtual machines in the backend pool.
+
+The Bicep file used in this quickstart is from [Azure Quickstart Templates](https://azure.microsoft.com/resources/templates/ag-docs-qs/)
+
+:::code language="bicep" source="~/quickstart-templates/demos/ag-docs-qs/main.bicep":::
+
+Multiple Azure resources are defined in the Bicep file:
+
+- [**Microsoft.Network/applicationgateways**](/azure/templates/microsoft.network/applicationgateways)
+- [**Microsoft.Network/publicIPAddresses**](/azure/templates/microsoft.network/publicipaddresses) : one for the application gateway, and two for the virtual machines.
+- [**Microsoft.Network/networkSecurityGroups**](/azure/templates/microsoft.network/networksecuritygroups)
+- [**Microsoft.Network/virtualNetworks**](/azure/templates/microsoft.network/virtualnetworks)
+- [**Microsoft.Compute/virtualMachines**](/azure/templates/microsoft.compute/virtualmachines) : two virtual machines
+- [**Microsoft.Network/networkInterfaces**](/azure/templates/microsoft.network/networkinterfaces) : two for the virtual machines
+- [**Microsoft.Compute/virtualMachine/extensions**](/azure/templates/microsoft.compute/virtualmachines/extensions) : to configure IIS and the web pages
+
+## Deploy the Bicep file
+
+1. Save the Bicep file as **main.bicep** to your local computer.
+1. Deploy the Bicep file using either Azure CLI or Azure PowerShell.
+
+    # [CLI](#tab/CLI)
+
+    ```azurecli
+    az group create --name myResourceGroupAG --location eastus
+    az deployment group create --resource-group myResourceGroupAG --template-file main.bicep --parameters adminUsername=<admin-username>
+    ```
+
+    # [PowerShell](#tab/PowerShell)
+
+    ```azurepowershell
+    New-AzResourceGroup -Name myResourceGroupAG -Location eastus
+    New-AzResourceGroupDeployment -ResourceGroupName myResourceGroupAG -TemplateFile ./main.bicep -adminUsername "<admin-username>"
+    ```
+
+    ---
+
+    > [!NOTE]
+    > Replace **\<admin-username\>** with the admin username for the backend servers. You'll also be prompted to enter **adminPassword**.
+
+    When the deployment finishes, you should see a message indicating the deployment succeeded.
+
+## Validate the deployment
+
+Use the Azure portal, Azure CLI, or Azure PowerShell to list the deployed resources in the resource group.
+
+# [CLI](#tab/CLI)
+
+```azurecli-interactive
+az resource list --resource-group myResourceGroupAG
+```
+
+# [PowerShell](#tab/PowerShell)
+
+```azurepowershell-interactive
+Get-AzResource -ResourceGroupName myResourceGroupAG
+```
+
+---
+
+## Clean up resources
+
+When no longer needed, use the Azure portal, Azure CLI, or Azure PowerShell to delete the resource group and its resources.
+
+# [CLI](#tab/CLI)
+
+```azurecli-interactive
+az group delete --name myResourceGroupAG
+```
+
+# [PowerShell](#tab/PowerShell)
+
+```azurepowershell-interactive
+Remove-AzResourceGroup -Name myResourceGroupAG
+```
+
+---
+
+## Next steps
+
+> [!div class="nextstepaction"]
+> [Manage web traffic with an application gateway using the Azure CLI](./tutorial-manage-web-traffic-cli.md)

articles/application-gateway/quick-create-bicep.md

@@ -17,6 +17,9 @@
     href: quick-create-powershell.md
   - name: Create Application Gateway - Azure CLI
     href: quick-create-cli.md
+  - name: Create Application Gateway - Bicep
+    displayName: ARM, Resource Manager, Template
+    href: quick-create-bicep.md
   - name: Create Application Gateway - ARM template
     displayName: Resource Manager
     href: quick-create-template.md

articles/application-gateway/toc.yml

@@ -3,19 +3,17 @@ author: laujan
 ms.service: applied-ai-services
 ms.subservice: forms-recognizer
 ms.topic: include
-ms.date: 09/22/2021
+ms.date: 04/14/2022
 ms.author: lajanuar
 ms.custom: ignite-fall-2021
 ---
+<!-- markdownlint-disable MD041 -->
 
 * For best results, provide one clear photo or high-quality scan per document.
 * Supported file formats: JPEG, PNG, BMP, TIFF, and PDF (text-embedded or scanned). Text-embedded PDFs are best to eliminate the possibility of error in character extraction and location.
 * For PDF and TIFF, up to 2000 pages can be processed (with a free tier subscription, only the first two pages are processed).
 * The file size must be less than 50 MB.
-* Image dimensions must be between 50 x 50 pixels and 10000 x 10000 pixels.
+* Image dimensions must be between 50 x 50 pixels and 10,000 x 10,000 pixels.
 * PDF dimensions are up to 17 x 17 inches, corresponding to Legal or A3 paper size, or smaller.
 * The total size of the training data is 500 pages or less.
 * If your PDFs are password-locked, you must remove the lock before submission.
-* For unsupervised learning (without labeled data):
-  * Data must contain keys and values.
-  * Keys must appear above or to the left of the values; they can't appear below or to the right.

articles/applied-ai-services/form-recognizer/includes/input-requirements.md

@@ -19,7 +19,7 @@ keywords: document processing
 <!-- markdownlint-disable MD029 -->
 # Get started with the Form Recognizer Sample Labeling tool
 
-Azure Form Recognizer is a cloud-based Azure Applied AI Service that uses machine-learning models to extract key-value pairs, text, and tables from your documents. You can use Form Recognizer to automate your data processing in applications and workflows, enhance data-driven strategies, and enrich document search capabilities. 
+Azure Form Recognizer is a cloud-based Azure Applied AI Service that uses machine-learning models to extract key-value pairs, text, and tables from your documents. You can use Form Recognizer to automate your data processing in applications and workflows, enhance data-driven strategies, and enrich document search capabilities.
 
 The Form Recognizer Sample Labeling tool is an open source tool that enables you to test the latest features of Azure Form Recognizer and Optical Character Recognition (OCR) services:
 
@@ -31,7 +31,7 @@ The Form Recognizer Sample Labeling tool is an open source tool that enables you
 
 ## Prerequisites
 
-You will need the following to get started:
+You'll need the following to get started:
 
 * An Azure subscription—you can [create one for free](https://azure.microsoft.com/free/cognitive-services/)
 
@@ -57,11 +57,11 @@ Form Recognizer offers several prebuilt models to choose from. Each model has it
 
 1. Navigate to the [Form Recognizer Sample Tool](https://fott-2-1.azurewebsites.net/).
 
-1. On the sample tool home page select **Use prebuilt model to get data**.
+1. On the sample tool home page, select **Use prebuilt model to get data**.
 
     :::image type="content" source="../media/label-tool/prebuilt-1.jpg" alt-text="Analyze results of Form Recognizer Layout":::
 
-1. Select the **Form Type**  your would like to analyze from the dropdown window.
+1. Select the **Form Type**  to analyze from the dropdown window.
 
 1. Choose a URL for the file you would like to analyze from the below options:
 
@@ -97,7 +97,7 @@ Azure the Form Recognizer Layout API extracts text, tables, selection marks, and
 
 1. Navigate to the [Form Recognizer Sample Tool](https://fott-2-1.azurewebsites.net/).
 
-1. On the sample tool home page select **Use Layout to get text, tables and selection marks**.
+1. On the sample tool home page, select **Use Layout to get text, tables and selection marks**.
 
      :::image type="content" source="../media/label-tool/layout-1.jpg" alt-text="Connection settings for Layout Form Recognizer tool.":::
 
@@ -130,31 +130,36 @@ Train a custom model to analyze and extract data from forms and documents specif
 
 * Configure CORS
 
-    [CORS (Cross Origin Resource Sharing)](/rest/api/storageservices/cross-origin-resource-sharing--cors--support-for-the-azure-storage-services) needs to be configured on your Azure storage account for it to be accessible from the Form Recognizer Studio. To configure CORS in the Azure portal, you will need access to the CORS blade of your storage account.
-
-    :::image type="content" source="../media/quickstarts/storage-cors-example.png" alt-text="Screenshot that shows CORS configuration for a storage account.":::
+    [CORS (Cross Origin Resource Sharing)](/rest/api/storageservices/cross-origin-resource-sharing--cors--support-for-the-azure-storage-services) needs to be configured on your Azure storage account for it to be accessible from the Form Recognizer Studio. To configure CORS in the Azure portal, you'll need access to the CORS blade of your storage account.
 
     1. Select the CORS blade for the storage account.
 
+       :::image type="content" source="../media/quickstarts/cors-setting-menu.png" alt-text="Screenshot of the CORS setting menu in the Azure portal.":::
+
     1. Start by creating a new CORS entry in the Blob service.
 
-    1. Set the **Allowed origins** to **https://fott-2-1.azurewebsites.net**.
+    1. Set the **Allowed origins** to **<https://fott-2-1.azurewebsites.net>**.
+
+       :::image type="content" source="../media/quickstarts/storage-cors-example.png" alt-text="Screenshot that shows CORS configuration for a storage account.":::
+
+       > [!TIP]
+       > You can use the wildcard character '*' rather than a specified domain to allow all origin domains to make requests via CORS.
 
     1. Select all the available 8 options for **Allowed methods**.
 
     1. Approve all **Allowed headers** and **Exposed headers** by entering an * in each field.
 
     1. Set the **Max Age** to 120 seconds or any acceptable value.
 
-    1. Click the save button at the top of the page to save the changes.
+    1. Select the save button at the top of the page to save the changes.
 
     CORS should now be configured to use the storage account from Form Recognizer Studio.
 
 ### Use the Sample Labeling tool
 
 1. Navigate to the [Form Recognizer Sample Tool](https://fott-2-1.azurewebsites.net/).
 
-1. On the sample tool home page select **Use custom form to train a model with labels and get key-value pairs**.
+1. On the sample tool home page, select **Use custom form to train a model with labels and get key-value pairs**.
 
     :::image type="content" source="../media/label-tool/custom-1.jpg" alt-text="Train a custom model.":::
 
@@ -180,7 +185,7 @@ Configure the **Project Settings** fields with the following values:
     > * **Description**. Add a brief description.
     > * **SAS URL**. Paste the shared access signature (SAS) URL for your Azure Blob Storage container.
 
-    * To retrieve the SAS URL for your custom model training data, go to your storage resource in the Azure portal and select the **Storage Explorer** tab. Navigate to your container, right-click, and select **Get shared access signature**. It's important to get the SAS for your container, not for the storage account itself. Make sure the **Read**, **Write**, **Delete** and **List** permissions are checked, and click **Create**. Then copy the value in the **URL** section to a temporary location. It should have the form: `https://<storage account>.blob.core.windows.net/<container name>?<SAS value>`.
+    * To retrieve the SAS URL for your custom model training data, go to your storage resource in the Azure portal and select the **Storage Explorer** tab. Navigate to your container, right-click, and select **Get shared access signature**. It's important to get the SAS for your container, not for the storage account itself. Make sure the **Read**, **Write**, **Delete** and **List** permissions are checked, and select **Create**. Then copy the value in the **URL** section to a temporary location. It should have the form: `https://<storage account>.blob.core.windows.net/<container name>?<SAS value>`.
 
        :::image type="content" source="../media/quickstarts/get-sas-url.png" alt-text="SAS location.":::
 
@@ -210,13 +215,13 @@ When you create or open a project, the main tag editor window opens. The tag edi
 
 Select **Run OCR on all files** on the left pane to get the text and table layout information for each document. The labeling tool will draw bounding boxes around each text element.
 
-The labeling tool will also show which tables have been automatically extracted. Select the table/grid icon on the left hand of the document to see the extracted table. Because the table content is automatically extracted, we will not be labeling the table content, but rather rely on the automated extraction.
+The labeling tool will also show which tables have been automatically extracted. Select the table/grid icon on the left hand of the document to see the extracted table. Because the table content is automatically extracted, we won't label the table content, but rather rely on the automated extraction.
 
   :::image type="content" source="../media/label-tool/table-extraction.png" alt-text="Table visualization in Sample Labeling tool.":::
 
 ##### Apply labels to text
 
-Next, you will create tags (labels) and apply them to the text elements that you want the model to analyze. Note the sample label data set includes already labeled fields; we will add another field.
+Next, you'll create tags (labels) and apply them to the text elements that you want the model to analyze. Note the sample label data set includes already labeled fields; we'll add another field.
 
 Use the tags editor pane to create a new tag you'd like to identify:
 
@@ -261,9 +266,9 @@ Choose the Train icon on the left pane to open the Training page. Then select th
 
 #### Analyze a custom form
 
-1. Select the **Analyze** (light bulb) icon on the left to test your model. 
+1. Select the **Analyze** (light bulb) icon on the left to test your model.
 
-1. Select source **Local file** and  browse for a file to select from the sample dataset that you unzipped in the test folder. 
+1. Select source **Local file** and  browse for a file to select from the sample dataset that you unzipped in the test folder.
 
 1. Choose the **Run analysis** button to get key/value pairs, text and tables predictions for the form. The tool will apply tags in bounding boxes and will report the confidence of each tag.