Merge pull request #96859 from MicrosoftDocs/repo_sync_working_branch

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

Author: huypub

articles/app-service/web-sites-traffic-manager.md

@@ -53,6 +53,4 @@ When using Azure Traffic Manager with Azure, keep in mind the following points:
 ## Next Steps
 For a conceptual and technical overview of Azure Traffic Manager, see [Traffic Manager Overview](../traffic-manager/traffic-manager-overview.md).
 
-For more information about using Traffic Manager with App Service, see the blog posts
-[Using Azure Traffic Manager with Azure Web Sites](https://blogs.msdn.com/b/waws/archive/2014/03/18/using-windows-azure-traffic-manager-with-waws.aspx) and [Azure Traffic Manager can now integrate with Azure Web Sites](https://azure.microsoft.com/blog/2014/03/27/azure-traffic-manager-can-now-integrate-with-azure-web-sites/).
 

articles/cognitive-services/Anomaly-Detector/quickstarts/detect-data-anomalies-csharp-sdk.md

@@ -36,15 +36,15 @@ Use the Anomaly Detector client library for .NET to:
 
 ### Create a new .NET Core application
 
-In a console window (such as cmd, PowerShell, or Bash), use the dotnet `new` command to create a new console app with the name `anomaly-detector-quickstart`. This command creates a simple "Hello World" project with a single C# source file: *Program.cs*. 
+In a console window (such as cmd, PowerShell, or Bash), use the `dotnet new` command to create a new console app with the name `anomaly-detector-quickstart`. This command creates a simple "Hello World" project with a single C# source file: *Program.cs*. 
 
-```console
+```dotnetcli
 dotnet new console -n anomaly-detector-quickstart
 ```
 
 Change your directory to the newly created app folder. You can build the application with:
 
-```console
+```dotnetcli
 dotnet build
 ```
 
@@ -62,7 +62,7 @@ Build succeeded.
 
 Within the application directory, install the Anomaly Detector client library for .NET with the following command:
 
-```console
+```dotnetcli
 dotnet add package Microsoft.Azure.CognitiveServices.AnomalyDetector --version 0.8.0-preview
 ```
 
@@ -126,9 +126,9 @@ Create a method to call the client's [LastDetectAsync()](https://docs.microsoft.
 
 ## Run the application
 
-Run the application with the dotnet `run` command from your application directory.
+Run the application with the `dotnet run` command from your application directory.
 
-```dotnet
+```dotnetcli
 dotnet run
 ```
 

articles/cognitive-services/Face/QuickStarts/csharp-sdk.md

@@ -10,7 +10,6 @@ ms.topic: quickstart
 ms.date: 08/20/2019
 ms.author: pafarley
 ---
-
 # Quickstart: Face client library for .NET
 
 Get started with the Face client library for .NET. Follow these steps to install the package and try out the example code for basic tasks. The Face API service provides you with access to advanced algorithms for detecting and recognizing human faces in images.
@@ -48,13 +47,13 @@ Create a new .NET Core application in your preferred editor or IDE.
 
 In a console window (such as cmd, PowerShell, or Bash), use the `dotnet new` command to create a new console app with the name `face-quickstart`. This command creates a simple "Hello World" C# project with a single source file: *Program.cs*. 
 
-```console
+```dotnetcli
 dotnet new console -n face-quickstart
 ```
 
 Change your directory to the newly created app folder. You can build the application with:
 
-```console
+```dotnetcli
 dotnet build
 ```
 
@@ -80,7 +79,7 @@ In the application's `Main` method, create variables for your resource's Azure e
 
 Within the application directory, install the Face client library for .NET with the following command:
 
-```console
+```dotnetcli
 dotnet add package Microsoft.Azure.CognitiveServices.Vision.Face --version 2.5.0-preview.1
 ```
 
@@ -169,7 +168,7 @@ The following method detects faces in a set of target images and in a single sou
 
 ### Print matches
 
-The following code prints the match details to the console.
+The following code prints the match details to the console:
 
 [!code-csharp[](~/cognitive-services-dotnet-sdk-samples/documentation-samples/quickstarts/Face/Program.cs?name=snippet_find_similar_print)]
 
@@ -282,7 +281,7 @@ At this point, your new **PersonGroup** object should have the same data as the
 
 Run the application from your application directory with the `dotnet run` command.
 
-```dotnet
+```dotnetcli
 dotnet run
 ```
 
@@ -313,4 +312,4 @@ In this quickstart, you learned how to use the Face library for .NET to do basis
 > [Face API reference (.NET)](https://docs.microsoft.com/dotnet/api/overview/azure/cognitiveservices/client/faceapi?view=azure-dotnet)
 
 * [What is the Face API?](../overview.md)
-* The source code for this sample can be found on [GitHub](https://github.com/Azure-Samples/cognitive-services-dotnet-sdk-samples/blob/master/documentation-samples/quickstarts/Face/Program.cs).
+* The source code for this sample can be found on [GitHub](https://github.com/Azure-Samples/cognitive-services-dotnet-sdk-samples/blob/master/documentation-samples/quickstarts/Face/Program.cs).

articles/cognitive-services/LUIS/sdk-csharp-quickstart-authoring-app.md

@@ -81,15 +81,15 @@ Create a new .NET Core application in your preferred editor or IDE.
 
 1. In a console window (such as cmd, PowerShell, or Bash), use the dotnet `new` command to create a new console app with the name `language-understanding-quickstart`. This command creates a simple "Hello World" C# project with a single source file: `Program.cs`. 
 
-    ```console
+    ```dotnetcli
     dotnet new console -n language-understanding-quickstart
     ```
 
 1. Change your directory to the newly created app folder. 
 
 1. You can build the application with:
 
-    ```console
+    ```dotnetcli
     dotnet build
     ```
 
@@ -108,7 +108,7 @@ Create a new .NET Core application in your preferred editor or IDE.
 
 Within the application directory, install the Language Understanding (LUIS) authoring client library for .NET with the following command:
 
-```console
+```dotnetcli
 dotnet add package Microsoft.Azure.CognitiveServices.Language.LUIS.Authoring --version 3.0.0
 ```
 
@@ -143,7 +143,7 @@ These code snippets show you how to do the following with the Language Understan
 
 ## Add the dependencies
 
-From the project directory, open the **Program.cs** file in your preferred editor or IDE. Replace the existing `using` code with the following `using` directives:
+From the project directory, open the *Program.cs* file in your preferred editor or IDE. Replace the existing `using` code with the following `using` directives:
 
 [!code-csharp[Using statements](~/cognitive-services-dotnet-sdk-samples/documentation-samples/quickstarts/LUIS/LUIS.cs?name=Dependencies)]
 
@@ -222,9 +222,9 @@ Publish the LUIS app using the [PublishAsync](https://docs.microsoft.com/dotnet/
 
 ## Run the application
 
-Run the application with the dotnet `run` command from your application directory.
+Run the application with the `dotnet run` command from your application directory.
 
-```console
+```dotnetcli
 dotnet run
 ```
 

articles/cognitive-services/QnAMaker/Quickstarts/create-publish-kb-csharp-sdk.md

@@ -44,15 +44,15 @@ After getting a key and endpoint for your resource, [create environment variable
 
 Create a new .NET Core application in your preferred editor or IDE. 
 
-In a console window (such as cmd, PowerShell, or Bash), use the dotnet `new` command to create a new console app with the name `qna-maker-quickstart`. This command creates a simple "Hello World" C# project with a single source file: `Program.cs`. 
+In a console window (such as cmd, PowerShell, or Bash), use the `dotnet new` command to create a new console app with the name `qna-maker-quickstart`. This command creates a simple "Hello World" C# project with a single source file: *Program.cs*. 
 
-```console
+```dotnetcli
 dotnet new console -n qna-maker-quickstart
 ```
 
 Change your directory to the newly created app folder. You can build the application with:
 
-```console
+```dotnetcli
 dotnet build
 ```
 
@@ -70,7 +70,7 @@ Build succeeded.
 
 Within the application directory, install the QnA Maker client library for .NET with the following command:
 
-```console
+```dotnetcli
 dotnet add package Microsoft.Azure.CognitiveServices.Knowledge.QnAMaker --version 1.0.0
 ```
 
@@ -184,11 +184,11 @@ The _loop_ and _Task.Delay_ in the following code block are used to simulate ret
 
 ## Run the application
 
-Run the application with the dotnet `run` command from your application directory.
+Run the application with the `dotnet run` command from your application directory.
 
 All of the code snippets in this article are [available](https://github.com/Azure-Samples/cognitive-services-qnamaker-python/blob/master/documentation-samples/quickstarts/knowledgebase_quickstart/knowledgebase_quickstart.py) and can be run as a single file.
 
-```console
+```dotnetcli
 dotnet run
 ```
 

articles/databox/data-box-deploy-picked-up.md

@@ -119,28 +119,7 @@ Azure datacenters in Australia have an additional security notification. All the
 2. Make sure that the data copy to device is complete and **Prepare to ship run** is successful.
 3. Power off the device and remove the cables.
 4. Spool and securely place the power cord that was provided with the device in the back of the device.
-5. Email Quantium Solutions to request a pickup. Refer to the service reference number specified on the Azure portal. Use the following email template: - *Request for reverse shipping label with TAU code*. Make sure to include the following details in the email: 
-
-    ```
-    To: [email protected]
-    Subject: Pickup request for Azure｜Reference number：XXX XXX XXX
-    Body: 
-    - Company name：
-    - Address:
-    - Contact name:
-    - Contact number:
-    - Requested pickup date: mm/dd
-    ```
-6. Quantium Solutions Australia will email you a return shipping label.
-7. Print the return label and affix it on the shipping box.
-8. Hand over the package to the courier.
-
-If needed, you can email Quantium Solution Support at [email protected] or phone.
-
-For inquiry regarding your order via the phone:
-
-- Send an email for pickup first.
-- Provide your order name on the phone.
+5. Book a pick-up online at the [DHL Link](https://mydhl.express.dhl/au/en/schedule-pickup.html#/schedule-pickup#label-reference).
 
 ::: zone target="chromeless"
 

articles/search/search-filters.md

@@ -81,7 +81,7 @@ POST https://[service name].search.windows.net/indexes/hotels/docs/search?api-ve
     parameters =
         new SearchParameters()
         {
-            Filter = "Rooms/BaseRate lt 150.0",
+            Filter = "Rooms/any(room: room/BaseRate lt 150.0)",
             Select = new[] { "HotelId", "HotelName", "Rooms/Description" ,"Rooms/BaseRate"}
         };
 
@@ -108,10 +108,10 @@ The following examples illustrate several usage patterns for filter scenarios. F
 
    ```
    # Match on hostels rated higher than 4 OR 5-star motels.
-   $filter=search.ismatchscoring('hostel') and rating ge 4 or search.ismatchscoring('motel') and rating eq 5
+   $filter=search.ismatchscoring('hostel') and Rating ge 4 or search.ismatchscoring('motel') and Rating eq 5
 
    # Match on 'luxury' or 'high-end' in the description field OR on category exactly equal to 'Luxury'.
-   $filter=search.ismatchscoring('luxury | high-end', 'description') or category eq 'Luxury'
+   $filter=search.ismatchscoring('luxury | high-end', 'Description') or Category eq 'Luxury'&$count=true
    ```
 
   It is also possible to combine full-text search via `search.ismatchscoring` with filters using `and` instead of `or`, but this is functionally equivalent to using the `search` and `$filter` parameters in a search request. For example, the following two queries produce the same result:

articles/service-fabric/configure-container-repository-credentials.md

@@ -109,7 +109,7 @@ Service Fabric supports using tokens as credentials to download images for your
 
 3.  Once the above steps are completed, modify your applicationmanifest.xml file.  Find the tag labeled “ContainerHostPolicies” and add the attribute `‘UseTokenAuthenticationCredentials=”true”`.
 
-    ```json
+    ```xml
       <ServiceManifestImport>
           <ServiceManifestRef ServiceManifestName="NodeServicePackage" ServiceManifestVersion="1.0"/>
       <Policies>

articles/virtual-network/virtual-networks-static-private-ip-arm-cli.md

@@ -247,7 +247,7 @@ To change the NIC for the VM used in the previous commands, complete the followi
 2. Run the **azure vm set** command to change the NIC used by the VM.
    
     ```azurecli
-    azure vm set -g TestRG -n DNS01 -N TestNIC2
+   az vm nic set --resource-group TestRG --vm-name DNS01 --nics TestNIC2
     ```
 
     Expected output:

articles/web-application-firewall/afds/afds-overview.md

@@ -88,16 +88,16 @@ Default action is to BLOCK. In addition, custom rules can be configured in the s
 Custom rules are always applied before rules in the Default Rule Set are evaluated. If a request matches a custom rule, corresponding rule action is applied, and the request is either blocked or passed through to back-end, without invocation of any further custom rules or the rules in the Default Rule Set. Furthermore, you have the option to remove Default Rule Set from your WAF policies.
 
 
-### Bot protection rule (preview)
+### Bot protection rule set (preview)
 
-A managed Bot protection rule set can be enabled for your WAF to take custom actions on requests from known malicious IP addresses. The IP addresses are sourced from the Microsoft Threat Intelligence feed. [Intelligent Security Graph](https://www.microsoft.com/security/operations/intelligence) powers Microsoft threat intelligence and is used by multiple services including Azure Security Center.
+A managed bot protection rule set can be enabled for your WAF to take custom actions on requests from known bot categories. There are three bot categories supported: Bad Bots, Good Bots, and Unknown Bots. Bot signatures are managed and dynamically updated by the WAF platform. Malicious IP addresses for Bad Bots are sourced from the Microsoft Threat Intelligence feed. [Intelligent Security Graph](https://www.microsoft.com/security/operations/intelligence) powers Microsoft Threat Intelligence and is used by multiple services including Azure Security Center. Good Bots include validated search engines. Unknown categories include additional bot groups. You may set custom actions to block, allow, log, or redirect for different types of bots.
 
 ![Bot Protection Rule Set](../media/afds-overview/botprotect2.png)
 
 > [!IMPORTANT]
 > The Bot protection rule set is currently in public preview and is provided with a preview service level agreement. Certain features may not be supported or may have constrained capabilities.  See the [Supplemental Terms of Use for Microsoft Azure Previews](https://azure.microsoft.com/support/legal/preview-supplemental-terms/) for details.
 
-If Bot Protection is enabled, incoming requests that match Malicious Bots client IPs are logged at FrontdoorWebApplicationFirewallLog log. You may access WAF logs from storage account, event hub or log analytics. 
+If bot protection is enabled, incoming requests that match bot rules are logged at the FrontdoorWebApplicationFirewallLog log. You may access WAF logs from a storage account, event hub, or log analytics.
 
 ## Configuration
 
@@ -109,4 +109,4 @@ Monitoring for WAF at Front Door is integrated with Azure Monitor to track alert
 
 ## Next steps
 
-- Learn about [Web Application Firewall on Azure Application Gateway](../ag/ag-overview.md)
+- Learn about [Web Application Firewall on Azure Application Gateway](../ag/ag-overview.md)