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

Author: Learn Build Service GitHub App

.openpublishing.redirection.json

@@ -65613,6 +65613,66 @@
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       "redirect_url": "https://learn.microsoft.com/training/modules/evaluate-generative-ai-apps/",
       "redirect_document_id": false
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 }

learn-pr/azure/aks-governance-azure-policy/includes/1-introduction.md

@@ -20,5 +20,5 @@ By the end of this module, you're able to:
 ## Prerequisites
 
 - Familiarity with Kubernetes concepts. If you're new to Kubernetes, start with the [basics of Kubernetes](https://azure.microsoft.com/topic/what-is-kubernetes).
-- An active [Azure subscription](https://azure-int.microsoft.com/free/kubernetes-service/)
+- An active [Azure subscription](https://azure.microsoft.com/free/?cid=msft_learn)
 - Ability to use the [Azure CLI](/cli/azure/)

learn-pr/wwl-azure/automate-azure-virtual-desktop-management-tasks/includes/7-summary.md

@@ -7,6 +7,6 @@ In this module, you learned how to:
 
 ## Learn more
 
- -  [Azure free account](https://azure.microsoft.com/pricing/purchase-options/azure-account?cid=msft_learn) \| [Azure free account FAQ](https://azure.microsoft.com/free/free-account-faq/?cid=msft_learn)
+ -  [Azure free account](https://azure.microsoft.com/pricing/purchase-options/azure-account?cid=msft_learn) \| [Azure free account FAQ](https://azure.microsoft.com/pricing/purchase-options/azure-account#FAQ?cid=msft_learn)
  -  [Free account for Students](https://azure.microsoft.com/free/students/?cid=msft_learn) \| [Azure for students FAQ](/azure/education-hub/azure-dev-tools-teaching/program-faq#azure-for-students/)
  -  [Create an Azure account](/learn/modules/create-an-azure-account/) module on Learn.

learn-pr/wwl-azure/azure-stack-hub/includes/9-summary.md

@@ -7,6 +7,6 @@ In this module, you learned how to:
 
 ## Learn more
 
- -  [Azure free account](https://azure.microsoft.com/pricing/purchase-options/azure-account?cid=msft_learn) \| [Azure free account FAQ](https://azure.microsoft.com/free/free-account-faq/?cid=msft_learn)
+ -  [Azure free account](https://azure.microsoft.com/pricing/purchase-options/azure-account?cid=msft_learn) \| [Azure free account FAQ](https://azure.microsoft.com/pricing/purchase-options/azure-account#FAQ?cid=msft_learn)
  -  [Free account for Students](https://azure.microsoft.com/free/students/?cid=msft_learn) \| [Azure for students FAQ](/azure/education-hub/azure-dev-tools-teaching/program-faq#azure-for-students/)
  -  [Create an Azure account](/training/modules/create-an-azure-account/) module on Learn.

learn-pr/wwl-azure/microsoft-defender-cloud-threat-protection/includes/31-enable-threat-protection-for-ai-workloads.md

@@ -7,7 +7,7 @@ Threat protection for AI workloads in Microsoft Defender for Cloud protects AI w
 ## Prerequisites
 - Read the [Overview - AI threat protection](/azure/defender-for-cloud/ai-threat-protection).
 
-- You need a Microsoft Azure subscription. If you don't have an Azure subscription, you can [sign up for a free subscription](https://azure.microsoft.com/pricing/free-trial/).
+- You need a Microsoft Azure subscription. If you don't have an Azure subscription, you can [sign up for a free subscription](https://azure.microsoft.com/pricing/purchase-options/azure-account?cid=msft_learn/).
 
 - [Enable Defender for Cloud on your Azure subscription](/azure/defender-for-cloud/get-started#enable-defender-for-cloud-on-your-azure-subscription).
 

learn-pr/wwl-data-ai/foundry-sdk/includes/02-chat-playground.md

@@ -8,6 +8,7 @@
 ::: zone-end
 
 ::: zone pivot="text"
+
 Before you write code to build a generative AI chat application, it's helpful to explore what your project can do through the Foundry portal. The portal provides interactive tools for testing models and generating code samples that you can use as starting points for your applications.
 
 [![Screenshot of the Model playground in Microsoft Foundry portal.](../media/foundry-playground.png)](../media/foundry-playground.png#lightbox)

learn-pr/wwl-data-ai/foundry-sdk/includes/03-microsoft-foundry-sdk.md

@@ -8,6 +8,7 @@
 ::: zone-end
 
 ::: zone pivot="text"
+
 Microsoft Foundry provides flexibility for developing generative AI chat applications. Before you start development, it's important to understand the options that are available, and how to decide which of them to use. Some considerations for developing an application include:
 
 - **Endpoints**: Microsoft Foundry projects provide two endpoints that you can use to connect to and consume project assets, such as model deployments, from client applications. Each project has both a *Project endpoint* and an *Azure OpenAI endpoint*.

learn-pr/wwl-data-ai/foundry-sdk/includes/05-openai-api.md

@@ -8,6 +8,7 @@
 ::: zone-end
 
 ::: zone pivot="text"
+
 The OpenAI *ChatCompletions* API is commonly used across generative AI models and platforms. Although the *Responses* API is recommended for new project development, it's likely that you'll encounter scenarios where the *ChatCompletions* API is useful for code maintenance or cross-platform compatibility.
 
 ## Submitting a prompt

learn-pr/wwl-data-ai/model-catalog-evaluate/includes/3-select-models-benchmarks.md

@@ -8,6 +8,7 @@
 ::: zone-end
 
 ::: zone pivot="text"
+
 Before deploying a model, you want to understand how it performs across different dimensions. Model benchmarks provide objective, measurable data to help you compare models and make informed selection decisions. The Microsoft Foundry portal offers comprehensive benchmarking tools organized into quality, safety, cost, and performance metrics.
 
 ## Access model benchmarks

learn-pr/wwl-data-ai/prepare-azure-ai-development/includes/6-responsible-ai.md

@@ -8,6 +8,7 @@
 ::: zone-end
 
 ::: zone pivot="text"
+
 It's important for software engineers to consider the impact of their software on users, and society in general; including considerations for its responsible use. When the application is imbued with artificial intelligence, these considerations are particularly important due to the nature of how AI systems work and inform decisions; often based on probabilistic models, which are in turn dependent on the data with which they were trained.
 
 The human-like nature of AI solutions is a significant benefit in making applications user-friendly, but it can also lead users to place a great deal of trust in the application's ability to make correct decisions. The potential for harm to individuals or groups through incorrect predictions or misuse of AI capabilities is a major concern, and software engineers building AI-enabled solutions should apply due consideration to mitigate risks and ensure fairness, reliability, and adequate protection from harm or discrimination.