Merge pull request #275516 from v-savila/0515-validation

Bulk update: Global effort to fix validation errors

Author: denrea

articles/app-service/includes/deploy-intelligent-apps/deploy-intelligent-apps-linux-dotnet-pivot.md

@@ -11,7 +11,7 @@ You can use Azure App Service to work with popular AI frameworks like LangChain
 
 #### Prerequisites
 
-- An [Azure OpenAI resource](https://learn.microsoft.com/azure/ai-services/openai/quickstart?pivots=programming-language-csharp&tabs=command-line%2Cpython#set-up) or an [OpenAI account](https://platform.openai.com/overview).
+- An [Azure OpenAI resource](../../../ai-services/openai/quickstart.md?pivots=programming-language-csharp&tabs=command-line%2Cpython#set-up) or an [OpenAI account](https://platform.openai.com/overview).
 - A .NET 8 Blazor Web App.  Create the application with a template [here](https://dotnet.microsoft.com/learn/aspnet/blazor-tutorial/intro).
 
 ### Setup Blazor web app
@@ -63,7 +63,7 @@ After the Navigation is updated, we can start preparing to build the OpenAI clie
 
 In order to make calls to OpenAI with your client, you need to first grab the Keys and Endpoint values from Azure OpenAI, or OpenAI and add them as secrets for use in your application. Retrieve and save the values for later use.
 
-For Azure OpenAI, see [this documentation](https://learn.microsoft.com/azure/ai-services/openai/quickstart?pivots=programming-language-csharp&tabs=command-line%2Cpython#retrieve-key-and-endpoint) to retrieve the key and endpoint values. For our application, you need the following values:
+For Azure OpenAI, see [this documentation](../../../ai-services/openai/quickstart.md?pivots=programming-language-csharp&tabs=command-line%2Cpython#retrieve-key-and-endpoint) to retrieve the key and endpoint values. For our application, you need the following values:
 
 - `deploymentName`
 - `endpoint`
@@ -74,8 +74,8 @@ For OpenAI, see this [documentation](https://platform.openai.com/docs/api-refere
 - `apiKey`
 - `modelId`
 
-Since we are deploying to App Service, we can secure these secrets in **Azure Key Vault** for protection. Follow the [Quickstart](https://learn.microsoft.com/azure/key-vault/secrets/quick-create-cli#create-a-key-vault) to set up your Key Vault and add the secrets you saved from earlier.
-Next, we can use Key Vault references as app settings in our App Service resource to reference in our application. Follow the instructions in the [documentation](https://learn.microsoft.com/azure/app-service/app-service-key-vault-references?source=recommendations&tabs=azure-cli) to grant your app access to your Key Vault and to set up Key Vault references.
+Since we are deploying to App Service, we can secure these secrets in **Azure Key Vault** for protection. Follow the [Quickstart](../../../key-vault/secrets/quick-create-cli.md#create-a-key-vault) to set up your Key Vault and add the secrets you saved from earlier.
+Next, we can use Key Vault references as app settings in our App Service resource to reference in our application. Follow the instructions in the [documentation](../../app-service-key-vault-references.md?source=recommendations&tabs=azure-cli) to grant your app access to your Key Vault and to set up Key Vault references.
 Then, go to the portal Environment Variables blade in your resource and add the following app settings:
 
 For Azure OpenAI, use the following settings:
@@ -133,7 +133,7 @@ Semantic Kernel is an open-source SDK that enables you to easily develop AI agen
 
 To create the OpenAI client, we'll first start by installing Semantic Kernel.   
 
-To install Semantic Kernel, browse the NuGet package manager in Visual Studio and install the **Microsoft.SemanticKernel** package. For NuGet Package Manager instructions, see [here](https://learn.microsoft.com/nuget/consume-packages/install-use-packages-visual-studio#find-and-install-a-package). For CLI instructions, see [here](https://learn.microsoft.com/nuget/consume-packages/install-use-packages-dotnet-cli).
+To install Semantic Kernel, browse the NuGet package manager in Visual Studio and install the **Microsoft.SemanticKernel** package. For NuGet Package Manager instructions, see [here](/nuget/consume-packages/install-use-packages-visual-studio#find-and-install-a-package). For CLI instructions, see [here](/nuget/consume-packages/install-use-packages-dotnet-cli).
 Once the Semantic Kernel package is installed, you can now initialize the kernel. 
 
 ### Initialize the kernel
@@ -189,7 +189,7 @@ var kernel = builder.Build();
 
 ### Configure prompt and create semantic function
 
-Now that our chosen OpenAI service client is created with the correct keys we can add a function to handle the prompt. With Semantic Kernel you can handle prompts by the use of a semantic function, which turn the prompt and the prompt configuration settings into a function the Kernel can execute. Learn more on configuring prompts [here](https://learn.microsoft.com/semantic-kernel/prompts/configure-prompts?tabs=Csharp).
+Now that our chosen OpenAI service client is created with the correct keys we can add a function to handle the prompt. With Semantic Kernel you can handle prompts by the use of a semantic function, which turn the prompt and the prompt configuration settings into a function the Kernel can execute. Learn more on configuring prompts [here](/semantic-kernel/prompts/configure-prompts?tabs=Csharp).
 
 First, we create a variable that holds the user's prompt. Then add a function with execution settings to handle and configure the prompt. Add the following code to the *OpenAI.razor* file:
 
@@ -317,6 +317,6 @@ If you have followed the steps above, you're ready to deploy to App Service. If
 
 ### Authentication
 
-Although optional, it's highly recommended that you also add authentication to your web app when using an Azure OpenAI or OpenAI service. This can add a level of security with no other code. Learn how to enable authentication for your web app [here](https://learn.microsoft.com/azure/app-service/scenario-secure-app-authentication-app-service).
+Although optional, it's highly recommended that you also add authentication to your web app when using an Azure OpenAI or OpenAI service. This can add a level of security with no other code. Learn how to enable authentication for your web app [here](../../scenario-secure-app-authentication-app-service.md).
 
 Once deployed, browse to the web app and navigate to the OpenAI tab. Enter a query to the service and you should see a populated response from the server. The tutorial is now complete and you now know how to use OpenAI services to create intelligent applications.

articles/app-service/includes/deploy-intelligent-apps/deploy-intelligent-apps-linux-java-pivot.md

@@ -11,12 +11,12 @@ You can use Azure App Service to create applications using Azure OpenAI and Open
 
 #### Prerequisites
 
-- An [Azure OpenAI resource](https://learn.microsoft.com/azure/ai-services/openai/quickstart?pivots=programming-language-csharp&tabs=command-line%2Cpython#set-up) or an [OpenAI account](https://platform.openai.com/overview).
-- A Java spring boot application. Create the application using this [quickstart](https://learn.microsoft.com/azure/app-service/quickstart-java?tabs=springboot&pivots=java-maven-javase).
+- An [Azure OpenAI resource](../../../ai-services/openai/quickstart.md?pivots=programming-language-csharp&tabs=command-line%2Cpython#set-up) or an [OpenAI account](https://platform.openai.com/overview).
+- A Java spring boot application. Create the application using this [quickstart](../../quickstart-java.md?tabs=springboot&pivots=java-maven-javase).
 
 ### Setup web app
 
-For this Spring Boot application, we are building off the [quickstart](https://learn.microsoft.com/azure/app-service/quickstart-java?tabs=springboot&pivots=java-maven-javase) app and adding an extra feature to make a request to an Azure OpenAI or OpenAI service. Add the following code to your application:
+For this Spring Boot application, we are building off the [quickstart](../../quickstart-java.md?tabs=springboot&pivots=java-maven-javase) app and adding an extra feature to make a request to an Azure OpenAI or OpenAI service. Add the following code to your application:
 
 ```bash
   @RequestMapping("/")
@@ -32,7 +32,7 @@ For this Spring Boot application, we are building off the [quickstart](https://l
 
 First, you need to grab the keys and endpoint values from Azure OpenAI, or OpenAI and add them as secrets for use in your application. Retrieve and save the values for later use to build the client.
 
-For Azure OpenAI, see [this documentation](https://learn.microsoft.com/azure/ai-services/openai/quickstart?pivots=programming-language-csharp&tabs=command-line%2Cpython#retrieve-key-and-endpoint) to retrieve the key and endpoint values. For our application, you need the following values:
+For Azure OpenAI, see [this documentation](../../../ai-services/openai/quickstart.md?pivots=programming-language-csharp&tabs=command-line%2Cpython#retrieve-key-and-endpoint) to retrieve the key and endpoint values. For our application, you need the following values:
 
 - `endpoint`
 - `apiKey`
@@ -43,9 +43,9 @@ For OpenAI, see this [documentation](https://platform.openai.com/docs/api-refere
 - `apiKey`
 - `modelName`
 
-Since we are deploying to App Service, we can secure these secrets in **Azure Key Vault** for protection. Follow the [Quickstart](https://learn.microsoft.com/azure/key-vault/secrets/quick-create-cli#create-a-key-vault) to set up your Key Vault and add the secrets you saved from earlier.
+Since we are deploying to App Service, we can secure these secrets in **Azure Key Vault** for protection. Follow the [Quickstart](../../../key-vault/secrets/quick-create-cli.md#create-a-key-vault) to set up your Key Vault and add the secrets you saved from earlier.
 
-Next, we can use Key Vault references as app settings in our App Service resource to reference in our application. Follow the instructions in the [documentation](https://learn.microsoft.com/azure/app-service/app-service-key-vault-references?source=recommendations&tabs=azure-cli) to grant your app access to your Key Vault and to set up Key Vault references.
+Next, we can use Key Vault references as app settings in our App Service resource to reference in our application. Follow the instructions in the [documentation](../../app-service-key-vault-references.md?source=recommendations&tabs=azure-cli) to grant your app access to your Key Vault and to set up Key Vault references.
 
 Then, go to the portal Environment Variables blade in your resource and add the following app settings:
 
@@ -251,6 +251,6 @@ Once deployed, you can visit your site URL and you are greeted with the text tha
 
 ### Authentication
 
-Although optional, it's highly recommended that you also add authentication to your web app when using an Azure OpenAI or OpenAI service. This can add a level of security with no other code. Learn how to enable authentication for your web app [here](https://learn.microsoft.com/azure/app-service/scenario-secure-app-authentication-app-service).
+Although optional, it's highly recommended that you also add authentication to your web app when using an Azure OpenAI or OpenAI service. This can add a level of security with no other code. Learn how to enable authentication for your web app [here](../../scenario-secure-app-authentication-app-service.md).
 
 Once deployed, browse to the web app and navigate to the OpenAI tab. Enter a query to the service and you should see a populated response from the server. The tutorial is now complete and you now know how to use OpenAI services to create intelligent applications.

articles/app-service/includes/deploy-intelligent-apps/deploy-intelligent-apps-linux-python-pivot.md

@@ -11,12 +11,12 @@ You can use Azure App Service to work with popular AI frameworks like LangChain
 
 #### Prerequisites
 
-- An [Azure OpenAI resource](https://learn.microsoft.com/azure/ai-services/openai/quickstart?pivots=programming-language-csharp&tabs=command-line%2Cpython#set-up) or an [OpenAI account](https://platform.openai.com/overview).
-- A Flask web application. Create the sample app using our [quickstart](https://learn.microsoft.com/azure/app-service/quickstart-python?tabs=flask%2Cwindows%2Cazure-cli%2Cvscode-deploy%2Cdeploy-instructions-azportal%2Cterminal-bash%2Cdeploy-instructions-zip-azcli#1---sample-application).
+- An [Azure OpenAI resource](../../../ai-services/openai/quickstart.md?pivots=programming-language-csharp&tabs=command-line%2Cpython#set-up) or an [OpenAI account](https://platform.openai.com/overview).
+- A Flask web application. Create the sample app using our [quickstart](../../quickstart-python.md?tabs=flask%2Cwindows%2Cazure-cli%2Cvscode-deploy%2Cdeploy-instructions-azportal%2Cterminal-bash%2Cdeploy-instructions-zip-azcli#1---sample-application).
 
 ### Setup flask web app
 
-For this Flask web application, we are building off the [quickstart](https://learn.microsoft.com/azure/app-service/quickstart-python?tabs=flask%2Cwindows%2Cazure-cli%2Cvscode-deploy%2Cdeploy-instructions-azportal%2Cterminal-bash%2Cdeploy-instructions-zip-azcli#1---sample-application) app and updating the *app.py* file to send and receive requests to an Azure OpenAI OR OpenAI service using LangChain.
+For this Flask web application, we are building off the [quickstart](../../quickstart-python.md?tabs=flask%2Cwindows%2Cazure-cli%2Cvscode-deploy%2Cdeploy-instructions-azportal%2Cterminal-bash%2Cdeploy-instructions-zip-azcli#1---sample-application) app and updating the *app.py* file to send and receive requests to an Azure OpenAI OR OpenAI service using LangChain.
 
 First, copy and replace the *index.html* file with the following code:
 
@@ -85,7 +85,7 @@ After the files are updated, we can start preparing our environment variables to
 
 In order to make calls to OpenAI with your client, you need to first grab the Keys and Endpoint values from Azure OpenAI, or OpenAI and add them as secrets for use in your application. Retrieve and save the values for later use.
 
-For Azure OpenAI, see [this documentation](https://learn.microsoft.com/azure/ai-services/openai/quickstart?pivots=programming-language-csharp&tabs=command-line%2Cpython#retrieve-key-and-endpoint) to retrieve the key and endpoint values. For our application, you need the following values:
+For Azure OpenAI, see [this documentation](../../../ai-services/openai/quickstart.md?pivots=programming-language-csharp&tabs=command-line%2Cpython#retrieve-key-and-endpoint) to retrieve the key and endpoint values. For our application, you need the following values:
 
 - `api_key`
 - `api_version`
@@ -96,9 +96,9 @@ For OpenAI, see this [documentation](https://platform.openai.com/docs/api-refere
 
 - `apiKey`
 
-Since we are deploying to App Service, we can secure these secrets in **Azure Key Vault** for protection. Follow the [Quickstart](https://learn.microsoft.com/azure/key-vault/secrets/quick-create-cli#create-a-key-vault) to set up your Key Vault and add the secrets you saved from earlier.
+Since we are deploying to App Service, we can secure these secrets in **Azure Key Vault** for protection. Follow the [Quickstart](../../../key-vault/secrets/quick-create-cli.md#create-a-key-vault) to set up your Key Vault and add the secrets you saved from earlier.
 
-Next, we can use Key Vault references as app settings in our App Service resource to reference in our application. Follow the instructions in the [documentation](https://learn.microsoft.com/azure/app-service/app-service-key-vault-references?source=recommendations&tabs=azure-cli) to grant your app access to your Key Vault and to set up Key Vault references.
+Next, we can use Key Vault references as app settings in our App Service resource to reference in our application. Follow the instructions in the [documentation](../../app-service-key-vault-references.md?source=recommendations&tabs=azure-cli) to grant your app access to your Key Vault and to set up Key Vault references.
 
 Then, go to the portal Environment Variables blade in your resource and add the following app settings:
 
@@ -272,6 +272,6 @@ If you have followed the steps above, you're ready to deploy to App Service and
 
 ### Authentication
 
-Although optional, it's highly recommended that you also add authentication to your web app when using an Azure OpenAI or OpenAI service. This can add a level of security with no other code. Learn how to enable authentication for your web app [here](https://learn.microsoft.com/azure/app-service/scenario-secure-app-authentication-app-service).
+Although optional, it's highly recommended that you also add authentication to your web app when using an Azure OpenAI or OpenAI service. This can add a level of security with no other code. Learn how to enable authentication for your web app [here](../../scenario-secure-app-authentication-app-service.md).
 
 Once deployed, browse to the web app and navigate to the OpenAI tab. Enter a query to the service and you should see a populated response from the server. The tutorial is now complete and you now know how to use OpenAI services to create intelligent applications.

articles/energy-data-services/release-notes.md

@@ -26,7 +26,7 @@ This page is updated with the details about the upcoming release approximately a
 ## April 2024
 
 ### Azure Data Manager for Energy in Qatar Central Region
-Azure Data Manager for Energy is now available in the Qatar Central Region. This new region is enabled for both the Standard and Developer tiers of Azure Data Manager for Energy, and is available for select customers and partners only. Please reach out to your designated Microsoft account team member to unlock access. Once access is provided, you can select "Qatar" as your preferred region when creating Azure Data Manager for Energy resource, using the [Azure portal](https://ms.portal.azure.com/#create/Microsoft.AzureDataManagerforEnergy) or your preferred provisioning method. Qatar Central region supports zone-redundant storage (ZRS) with 3 availabilty zones for disaster recovery. Data is stored at rest in Qatar in compliance with data residency requirements. For more details on zonal replication, please review the [documentation](https://learn.microsoft.com/azure/site-recovery/azure-to-azure-how-to-enable-zone-to-zone-disaster-recovery) page. Note that the default maximum ingress per geneneral purpose v2 and Blob storage accounts in Qatar Central is 25 Gbps. For more details, please review scalability and performance [targets](https://learn.microsoft.com/azure/storage/common/scalability-targets-standard-account#scale-targets-for-standard-storage-accounts).
+Azure Data Manager for Energy is now available in the Qatar Central Region. This new region is enabled for both the Standard and Developer tiers of Azure Data Manager for Energy, and is available for select customers and partners only. Please reach out to your designated Microsoft account team member to unlock access. Once access is provided, you can select "Qatar" as your preferred region when creating Azure Data Manager for Energy resource, using the [Azure portal](https://ms.portal.azure.com/#create/Microsoft.AzureDataManagerforEnergy) or your preferred provisioning method. Qatar Central region supports zone-redundant storage (ZRS) with 3 availability zones for disaster recovery. Data is stored at rest in Qatar in compliance with data residency requirements. For more details on zonal replication, please review the [documentation](../site-recovery/azure-to-azure-how-to-enable-zone-to-zone-disaster-recovery.md) page. Note that the default maximum ingress per general purpose v2 and Blob storage accounts in Qatar Central is 25 Gbps. For more details, please review scalability and performance [targets](../storage/common/scalability-targets-standard-account.md#scale-targets-for-standard-storage-accounts).
 
 ## March 2024
 

articles/machine-learning/how-to-auto-train-forecast.md

@@ -52,7 +52,7 @@ AutoML forecasting jobs require that your training data is represented as an **M
 
 # [Python SDK](#tab/python)
 
-You can create an MLTable using the [mltable Python SDK](/python/api/mltable) as in the following example:
+You can create an MLTable using the [mltable Python SDK](/python/api/mltable/mltable) as in the following example:
 
 ```python
 import mltable

articles/machine-learning/how-to-configure-auto-train.md

@@ -99,7 +99,7 @@ Training data must be accessible from the remote compute. Automated ML v2 (Pytho
 
 # [Python SDK](#tab/python)
 
-You can create an MLTable using the [mltable Python SDK](/python/api/mltable) as in the following example:
+You can create an MLTable using the [mltable Python SDK](/python/api/mltable/mltable) as in the following example:
 
 ```python
 import mltable