Merge branch 'main' of https://github.com/MicrosoftDocs/azure-docs-pr into sla

Author: dlepow

.openpublishing.redirection.json

@@ -4744,6 +4744,16 @@
       "source_path_from_root": "/articles/virtual-network/tutorial-filter-network-traffic-cli.md",
       "redirect_url": "/azure/virtual-network/tutorial-filter-network-traffic",
       "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/virtual-network/virtual-network-service-endpoint-policies-portal.md",
+      "redirect_url": "/azure/virtual-network/virtual-network-service-endpoint-policies",
+      "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/virtual-network/virtual-network-service-endpoint-policies-powershell.md",
+      "redirect_url": "/azure/virtual-network/virtual-network-service-endpoint-policies",
+      "redirect_document_id": false
     }
   ]
 }

articles/advisor/advisor-reference-reliability-recommendations.md

@@ -91,7 +91,7 @@ Manage gateway capacity by manually adding or removing scale units, similar to t
 Workspace gateways need to be in the same Azure region and subscription as the API Management service. 
 
 > [!NOTE]
-> Starting in August 2024, workspace gateway support will be rolled out in the following regions. These regions are a subset of those where API Management is available.
+> These regions are a subset of those where API Management is available.
 
 * West US
 * North Central US

articles/api-management/workspaces-overview.md

@@ -293,11 +293,11 @@ Azure Automation provides scripts for common Azure VM management operations like
 
 |**Azure VM management operations** | **Details**|
 |--- | ---|
-[Stop-Azure-VM-On-Alert](https://github.com/azureautomation/Stop-Azure-VM-On-Alert) | This runbook will stop an Azure Resource Manager VM in response to an Azure alert trigger. </br></br> Input is alert data with information needed to identify which VM to stop.</br></br> The runbook must be called from an Azure alert via a webhook. </br></br> Latest version of Az module should be added to the automation account. </br></br> Managed Identity should be enabled and contributor access to the automation account should be given.
-[Restart-Azure-VM-On-Alert](https://github.com/azureautomation/Restart-Azure-VM-On-Alert) | This runbook will stop an Azure Resource Manager VM in response to an Azure alert trigger. </br></br> Input is alert data with information needed to identify which VM to stop.</br></br> The runbook must be called from an Azure alert via a webhook. </br></br> Latest version of Az module should be added to the automation account. </br></br> Managed Identity should be enabled and contributor access to the automation account should be given.
-[Delete-Azure-VM-On-Alert](https://github.com/azureautomation/Delete-Azure-VM-On-Alert) | This runbook will stop an Azure Resource Manager VM in response to an Azure alert trigger. </br></br> Input is alert data with information needed to identify which VM to stop.</br></br> The runbook must be called from an Azure alert via a webhook. </br></br> Latest version of Az module should be added to the automation account. </br></br> Managed Identity should be enabled and contributor access to the automation account should be given.
-[ScaleDown-Azure-VM-On-Alert](https://github.com/azureautomation/ScaleDown-Azure-VM-On-Alert) | This runbook will stop an Azure Resource Manager VM in response to an Azure alert trigger. </br></br> Input is alert data with information needed to identify which VM to stop.</br></br> The runbook must be called from an Azure alert via a webhook. </br></br> Latest version of Az module should be added to the automation account. </br></br> Managed Identity should be enabled and contributor access to the automation account should be given.
-[ScaleUp-Azure-VM-On-Alert](https://github.com/azureautomation/ScaleUp-Azure-VM-On-Alert) | This runbook will stop an Azure Resource Manager VM in response to an Azure alert trigger. </br></br> Input is alert data with information needed to identify which VM to stop.</br></br> The runbook must be called from an Azure alert via a webhook. </br></br> Latest version of Az module should be added to the automation account. </br></br> Managed Identity should be enabled and contributor access to the automation account should be given.
+|[Stop-Azure-VM-On-Alert](https://github.com/azureautomation/Stop-Azure-VM-On-Alert) | This runbook will stop an Azure Resource Manager VM in response to an Azure alert trigger. </br></br> The target resource of the triggered alert must be the VM to stop. This is passed in an input parameter from the triggered alert payload.</br></br> The runbook must be called from an Azure alert via a webhook. </br></br> Latest version of Az module should be added to the automation account. </br></br> Managed Identity should be enabled and contributor access to the automation account should be given.|
+|[Restart-Azure-VM-On-Alert](https://github.com/azureautomation/Restart-Azure-VM-On-Alert) | This runbook will stop an Azure Resource Manager VM in response to an Azure alert trigger. </br></br> The target resource of the triggered alert must be the VM to restart. This is passed in an input parameter from the triggered alert payload.</br></br> The runbook must be called from an Azure alert via a webhook. </br></br> Latest version of Az module should be added to the automation account. </br></br> Managed Identity should be enabled and contributor access to the automation account should be given.|
+|[Delete-Azure-VM-On-Alert](https://github.com/azureautomation/Delete-Azure-VM-On-Alert) | This runbook will stop an Azure Resource Manager VM in response to an Azure alert trigger. </br></br> The target resource of the triggered alert must be the VM to delete. This is passed in an input parameter from the triggered alert payload.</br></br> The runbook must be called from an Azure alert via a webhook. </br></br> Latest version of Az module should be added to the automation account. </br></br> Managed Identity should be enabled and contributor access to the automation account should be given.|
+|[ScaleDown-Azure-VM-On-Alert](https://github.com/azureautomation/ScaleDown-Azure-VM-On-Alert) | This runbook will stop an Azure Resource Manager VM in response to an Azure alert trigger. </br></br> The target resource of the triggered alert must be the VM to scale down. This is passed in an input parameter from the triggered alert payload.</br></br> The runbook must be called from an Azure alert via a webhook. </br></br> Latest version of Az module should be added to the automation account. </br></br> Managed Identity should be enabled and contributor access to the automation account should be given.|
+|[ScaleUp-Azure-VM-On-Alert](https://github.com/azureautomation/ScaleUp-Azure-VM-On-Alert) | This runbook will stop an Azure Resource Manager VM in response to an Azure alert trigger. </br></br> The target resource of the triggered alert must be the VM to scale up. This is passed in an input parameter from the triggered alert payload.</br></br> The runbook must be called from an Azure alert via a webhook. </br></br> Latest version of Az module should be added to the automation account. </br></br> Managed Identity should be enabled and contributor access to the automation account should be given.|
 
 
 ## Next steps

articles/automation/automation-create-alert-triggered-runbook.md

@@ -63,25 +63,72 @@ The results help you to conclude the learnings and outcomes into actionable item
 
 ## Scenarios for using experimentation
 
-- **Intelligent applications (e.g., AI-based features)**
-Accelerate General AI (Gen AI) adoption and optimize AI models and use cases through rapid experimentation. Use Experimentation to iterate quickly on AI models, test different scenarios, and determine effective approaches.
-It helps enhance agility in adapting AI solutions to evolving user needs and market trends, and facilitate understanding of the most effective approaches for scaling AI initiatives.
+### Release defense
 
-- **CI, CD and continuous experimentation (Gradual feature rollouts and version updates)**
-Ensure seamless transitions and maintain or improve key metrics with each version update while managing feature releases. Utilize experimentation to gradually roll out new features to subsets of users using feature flags, monitor performance metrics, and collect feedback for iterative improvements.
-It's beneficial to reduce the risk of introducing bugs or performance issues to the entire user base. It enables data-driven decision-making during version rollouts and feature flag management, leading to improved product quality and user satisfaction.
+Objective: Ensure smooth transitions and maintain or improve key metrics with each release.
 
-- **User experience optimization (UI A/B testing)**
-Optimize business metrics by comparing different UI variations and determining the most effective design. Conduct A/B tests using experimentation to test UI elements, measure user interactions, and analyze performance metrics.
-The best return here's improved user experience by implementing UI changes based on empirical evidence.
+Approach: Employ experimentation to gradually roll out new features, monitor performance metrics, and collect feedback for iterative improvements.
 
-- **Personalization and targeting experiments**
-Deliver personalized content and experiences tailored to user preferences and behaviors. Use experimentation to test personalized content, measure engagement, and iterate on personalization strategies.
-Results are increased user engagement, conversion rates, and customer loyalty through relevant and personalized experiences. These results, in turn drive revenue growth and customer retention by targeting audiences with tailored messages and offers.
+Benefits:
 
-- **Performance optimization experiments**
-Improve application performance and provide an efficient user experience through performance optimization experiments. Conduct experiments to test performance enhancements, measure key metrics, and implement successful optimizations.
-Here, experimentation enhances application scalability, reliability, and responsiveness through proactive performance improvements. It optimizes resource utilization and infrastructure costs by implementing efficient optimizations.
+* Minimizes the risk of widespread issues by using guardrail metrics to detect and address problems early in the rollout.
+* Helps maintain or improve key performance and user satisfaction metrics by making informed decisions based on real-time data.
+ 
+### Test hypotheses
+
+Objective: Validate assumptions and hypotheses to make informed decisions about product features, user behaviors, or business strategies.
+
+Approach: Use experimentation to test specific hypotheses by creating different feature versions or scenarios, then analyze user interactions and performance metrics to determine outcomes.
+
+Benefits:
+
+* Provides evidence-based insights that reduce uncertainty and guide strategic decision-making.
+* Enables faster iteration and innovation by confirming or refuting hypotheses with real user data.
+* Enhances product development by focusing efforts on ideas that are proven to work, ultimately leading to more successful and user-aligned features.
+  
+### A/B testing
+
+Objective: Optimize business metrics by comparing different UI variations and determining the most effective design.
+
+Approach: Conduct A/B tests using experimentation to test UI elements, measure user interactions, and analyze performance metrics.
+
+Benefits:
+* Improves user experience by implementing UI changes based on empirical evidence.
+* Increases conversion rates, engagement levels, and overall effectiveness of digital products or services.
+ 
+### For intelligent applications (for example, AI-based features)
+
+Objective: Accelerate General AI (Gen AI) adoption and optimize AI models and use cases through rapid experimentation.
+
+Approach: Use experimentation to iterate quickly on AI models, test different scenarios, and determine effective approaches.
+
+Benefits:
+
+* Enhances agility in adapting AI solutions to evolving user needs and market trends.
+* Facilitates understanding of the most effective approaches for scaling AI initiatives.
+* Improves accuracy and performance of AI models based on real-world data and feedback.
+ 
+### Personalization and targeting experiments
+
+Objective: Deliver personalized content and experiences tailored to user preferences and behaviors.
+
+Approach: Leverage experimentation to test personalized content, measure engagement, and iterate on personalization strategies.
+
+Benefits:
+
+* Increases user engagement, conversion rates, and customer loyalty through relevant and personalized experiences.
+* Drives revenue growth and customer retention by targeting audiences with tailored messages and offers.
+ 
+### Performance optimization experiments
+
+Objective: Improve application performance and user experience through performance optimization experiments.
+
+Approach: Conduct experiments to test performance enhancements, measure key metrics, and implement successful optimizations.
+
+Benefits:
+
+* Enhances application scalability, reliability, and responsiveness through proactive performance improvements.
+* Optimizes resource utilization and infrastructure costs by implementing efficient optimizations.
 
 ## Experiment operations
 

articles/azure-app-configuration/concept-experimentation.md

@@ -1,8 +1,8 @@
 ---
 title: Troubleshoot Guest Management Issues
-description: Learn about how to troubleshoot the guest management issues for Arc-enabled VMware vSphere.
+description: Learn how to troubleshoot the guest management issues for Arc-enabled VMware vSphere.
 ms.topic: reference
-ms.date: 08/06/2024
+ms.date: 08/29/2024
 ms.service: azure-arc
 ms.subservice: azure-arc-vmware-vsphere
 ms.custom: linux-related-content
@@ -18,7 +18,11 @@ manager: jsuri
 
 This article provides information on how to troubleshoot and resolve the issues that can occur when you enable guest management on Arc-enabled VMware vSphere virtual machines.
 
-## Troubleshoot issues while enabling Guest Management on a domain-joined Linux VM
+## Troubleshoot issues while enabling Guest Management
+
+**Troubleshoot issues while enabling Guest Management on:**
+
+# [Arc agent installation fails on a domain-joined Linux VM](#tab/linux)
 
 **Error message**: Enabling Guest Management on a domain-joined Linux VM fails with the error message **InvalidGuestLogin: Failed to authenticate to the system with the credentials**.
 
@@ -49,22 +53,12 @@ Default: The default set of PAM service names includes:
 
 #### References
 
-- [Invoke-VMScript to an domain joined Ubuntu VM](https://communities.vmware.com/t5/VMware-PowerCLI-Discussions/Invoke-VMScript-to-an-domain-joined-Ubuntu-VM/td-p/2257554).
-
-
-## Troubleshoot issues while enabling Guest Management on RHEL-based Linux VMs
+- [Invoke VMScript to a domain-joined Ubuntu VM](https://communities.vmware.com/t5/VMware-PowerCLI-Discussions/Invoke-VMScript-to-an-domain-joined-Ubuntu-VM/td-p/2257554).
 
-Applies to:
-
-- RedHat Linux
-- CentOS
-- Rocky Linux
-- Oracle Linux
-- SUSE Linux
-- SUSE Linux Enterprise Server
-- Alma Linux
-- Fedora
+# [Arc agent installation fails on RHEL Linux distros](#tab/rhel)
 
+**Applies to:**<br>
+:heavy_check_mark: RedHat Linux :heavy_check_mark: CentOS :heavy_check_mark: Rocky Linux :heavy_check_mark: Oracle Linux :heavy_check_mark: SUSE Linux :heavy_check_mark: SUSE Linux Enterprise Server :heavy_check_mark: Alma Linux :heavy_check_mark: Fedora
 
 **Error message**: Provisioning of the resource failed with Code: `AZCM0143`; Message: `install_linux_azcmagent.sh: installation error`.
 
@@ -108,6 +102,8 @@ Upon `yum` or `rpm` executing scriptlets, the context is changed to `rpm_script_
 
 - [Executing yum/rpm commands using VMware tools facility (vmrun) fails in error when packages have scriptlets](https://access.redhat.com/solutions/5347781).
 
+---
+
 ## Next steps
 
 If you don't see your problem here or you can't resolve your issue, try one of the following channels for support:

articles/azure-arc/vmware-vsphere/troubleshoot-guest-management-issues.md

@@ -106,10 +106,10 @@ The following table highlights the packages required for [supported Linux distro
 
 |Required package |Description |Minimum version |
 |-----------------|------------|----------------|
-|Glibc |    GNU C library | 2.5-12
+|Glibc |    GNU C library | 2.5-12|
 |Openssl    | OpenSSL libraries | 1.0.x or 1.1.x |
 |Curl | cURL web client | 7.15.5 |
-|Python | | 2.7 or 3.6+
+|Python | | 2.7 or 3.6-3.11|
 |Python-ctypes | |
 |PAM | Pluggable authentication modules | |
 

articles/azure-monitor/agents/agent-linux.md

@@ -25,7 +25,6 @@ Migration is a complex task. Start planning your migration to Azure Monitor Agen
 > - **Installation:** The ability to install the legacy agents will be removed from the Azure Portal and installation policies for legacy agents will be removed. You can still install the MMA agents extension as well as perform offline installations.
 > - **Customer Support:** You will not be able to get support for legacy agent issues.
 > - **OS Support:** Support for new Linux or Windows distros, including service packs, won't be added after the deprecation of the legacy agents.
-> - Log Analytics Agent will continue to function but not be able to connect Log Analytics workspaces.
 > - Log Analytics Agent can coexist with Azure Monitor Agent. Expect to see duplicate data if both agents are collecting the same data.