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Copy file name to clipboardExpand all lines: articles/ai-services/openai/overview.md
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## Responsible AI
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At Microsoft, we're committed to the advancement of AI driven by principles that put people first. Generative models such as the ones available in Azure OpenAI have significant potential benefits, but without careful design and thoughtful mitigations, such models have the potential to generate incorrect or even harmful content. Microsoft has made significant investments to help guard against abuse and unintended harm, which includes requiring applicants to show well-defined use cases, incorporating Microsoft’s <ahref="https://www.microsoft.com/ai/responsible-ai?activetab=pivot1:primaryr6"target="_blank">principles for responsible AI use</a>, building content filters to support customers, and providing responsible AI implementation guidance to onboarded customers.
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At Microsoft, we're committed to the advancement of AI driven by principles that put people first. Generative models such as the ones available in Azure OpenAI have significant potential benefits, but without careful design and thoughtful mitigations, such models have the potential to generate incorrect or even harmful content. Microsoft has made significant investments to help guard against abuse and unintended harm, which includes incorporating Microsoft’s <a href="https://www.microsoft.com/ai/responsible-ai?activetab=pivot1:primaryr6" target="_blank">principles for responsible AI use</a>, adopting a [Code of Conduct](/legal/cognitive-services/openai/code-of-conduct?context=/azure/ai-services/openai/context/context) for use of the service, building [content filters](/azure/ai-services/content-safety/overview) to support customers, and providing responsible AI [information and guidance](/legal/cognitive-services/openai/transparency-note?context=%2Fazure%2Fai-services%2Fopenai%2Fcontext%2Fcontext&tabs=image) that customers should consider when using Azure OpenAI.
Copy file name to clipboardExpand all lines: articles/app-service/app-service-configuration-references.md
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title: Use App Configuration references (Preview)
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title: Use App Configuration references
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description: Learn how to set up Azure App Service and Azure Functions to use Azure App Configuration references. Make App Configuration key-values available to your application code without changing it.
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author: muksvso
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# Use App Configuration references for App Service and Azure Functions (preview)
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# Use App Configuration references for App Service and Azure Functions
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This topic shows you how to work with configuration data in your App Service or Azure Functions application without requiring any code changes. [Azure App Configuration](../azure-app-configuration/overview.md) is a service to centrally manage application configuration. Additionally, it's an effective audit tool for your configuration values over time or releases.
Copy file name to clipboardExpand all lines: articles/azure-app-configuration/feature-management-dotnet-reference.md
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In previous versions, the primary schema for the feature management library was the [`.NET feature management schema`](https://github.com/microsoft/FeatureManagement-Dotnet/blob/main/schemas/FeatureManagement.Dotnet.v1.0.0.schema.json). Starting from v4.0.0, new features including variants and telemetry won't be supported for the .NET feature management schema.
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> [!NOTE]
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> If a feature flag written with `Microsoft Feature Management schema`can be found in the configuration, any feature flag written with `.NET feature management schema`will be ignored.
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> If there is a feature flag declaration that can be found in both the `feature_management` and `FeatureManagement` sections, the one from the `feature_management` section will be adopted.
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:::zone-end
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To begin using the `TargetingFilter`in an application, it must be added to the application's service collection just as any other feature filter. Unlike other built-in filters, the `TargetingFilter` relies on another service to be added to the application's service collection. That service is an `ITargetingContextAccessor`.
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The implementation type used for the `ITargetingContextAccessor` service must be implemented by the application that is using the targeting filter. Here's an example setting up feature management in a web application to use the `TargetingFilter` with an implementation of `ITargetingContextAccessor` called `HttpContextTargetingContextAccessor`.
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`Microsoft.FeatureManagement.AspNetCore` provides a [default implementation](https://github.com/microsoft/FeatureManagement-Dotnet/blob/main/src/Microsoft.FeatureManagement.AspNetCore/DefaultHttpTargetingContextAccessor.cs) of `ITargetingContextAccessor` which will extract targeting info from a request's `HttpContext`. You can use the default targeting context accessor when setting up targeting by using the non-generic `WithTargeting` overload on the `IFeatureManagementBuilder`.
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The default targeting context accessor and `TargetingFilter` are registered by calling `WithTargeting` on the `IFeatureManagementBuilder`.
The targeting context accessor and `TargetingFilter` are registered by calling `WithTargeting<T>` on the `IFeatureManagementBuilder`.
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You can also register a customized implementation for`ITargetingContextAccessor` and `TargetingFilter` by calling `WithTargeting<T>`. Here's an example setting up feature management in a web application to use the `TargetingFilter` with an implementation of `ITargetingContextAccessor` called `ExampleTargetingContextAccessor`.
To use the `TargetingFilter` in a web application, an implementation of `ITargetingContextAccessor` is required. This is because when a targeting evaluation is being performed, information such as what user is currently being evaluated is needed. This information is known as the targeting context. Different web applications may extract this information from different places. Some common examples of where an application may pull the targeting context are the request's HTTP context or a database.
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To use the `TargetingFilter` in a web application, an implementation of `ITargetingContextAccessor` is required. This is because when a targeting evaluation is being performed, contextual information such as what user is currently being evaluated is needed. This information is known as the [`TargetingContext`](https://github.com/microsoft/FeatureManagement-Dotnet/blob/main/src/Microsoft.FeatureManagement/Targeting/TargetingContext.cs). Different applications may extract this information from different places. Some common examples of where an application may pull the targeting context are the request's HTTP context or a database.
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An example that extracts targeting context information from the application's HTTP context is included in the [FeatureFlagDemo](https://github.com/microsoft/FeatureManagement-Dotnet/blob/main/examples/FeatureFlagDemo/HttpContextTargetingContextAccessor.cs) example project. This method relies on the use of `IHttpContextAccessor`, which is discussed [here](#using-httpcontext).
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An example that extracts targeting context information from the application's HTTP context is the [`DefaultHttpTargetingContextAccessor`](https://github.com/microsoft/FeatureManagement-Dotnet/blob/main/src/Microsoft.FeatureManagement.AspNetCore/DefaultHttpTargetingContextAccessor.cs) provided by the `Microsoft.FeatureManagement.AspNetCore` package. It will extract targeting info from `HttpContext.User`. `UserId` information will be extracted from from the `Identity.Name` field and `Groups` information will be extracted from claims of type [`Role`](/dotnet/api/system.security.claims.claimtypes.role). This implementation relies on the use of `IHttpContextAccessor`, which is discussed [here](#using-httpcontext).
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### Targeting in a Console Application
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* Which variant is a particular user seeing?
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These types of questions can be answered through the emission and analysis of feature flag evaluation events. This library supports emitting these events through telemetry publishers. One or many telemetry publishers can be registered to publish events whenever feature flags are evaluated.
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These types of questions can be answered through the emission and analysis of feature flag evaluation events. This library uses the [`System.Diagnostics.Activity`](/dotnet/api/system.diagnostics.activity) API to produce tracing telemetry during feature flag evaluation.
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### Enabling Telemetry
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|`enabled`| Specifies whether telemetry should be published for the feature flag. |
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|`metadata`|A collection of key-value pairs, modeled as a dictionary, that can be used to attach custom metadata about the feature flag to evaluation events. |
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### Custom Telemetry Publishers
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### Custom Telemetry Publishing
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The feature manager has its own `ActivitySource` named "Microsoft.FeatureManagement". If`telemetry` is enabled for a feature flag, whenever the evaluation of the feature flag is started, the feature manager will start an `Activity`. When the feature flag evaluation is finished, the feature manager will add an `ActivityEvent` named `"FeatureFlag"` to the current activity. The`"FeatureFlag"`event will have tags which include the information about the feature flag evaluation. Specifically, the tags will include the following fields:
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Custom handling of feature flag telemetry is made possible by implementing an `ITelemetryPublisher` and registering it in the feature manager. Whenever a feature flag that has telemetry enabled is evaluated, the registered telemetry publisher gets a chance to publish the corresponding evaluation event.
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| Tag | Description |
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|----------------|----------------|
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|`FeatureName`| The feature flag name. |
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|`Enabled`| Whether the feature flag is evaluated as enabled. |
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|`Variant`| The assigned variant. |
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|`VariantAssignmentReason`| The reason why the variant is assigned. |
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|`TargetingId`| The user id used for targeting. |
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> [!NOTE]
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> All key value pairs specified in`telemetry.metadata`of the feature flag will also be included in the tags.
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To enable custom telemetry publishing, you can create an [`ActivityListener`](/dotnet/api/system.diagnostics.activitylistener) and listen to the `Microsoft.FeatureManagement` activity source. Here is an example showing how to listen to the feature management activity source and add a callback when a feature is evaluated.
if (evaluationEvent.HasValue && evaluationEvent.Value.Tags.Any())
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{
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// Do something.
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}
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}
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});
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```
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The `EvaluationEvent` type can be found [here](https://github.com/microsoft/FeatureManagement-Dotnet/blob/preview/src/Microsoft.FeatureManagement/Telemetry/EvaluationEvent.cs) for reference.
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For more information, please go to [Collect a distributed trace](/dotnet/core/diagnostics/distributed-tracing-collection-walkthroughs).
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Registering telemetry publishers is done when calling `AddFeatureManagement()`. Here's an example setting up feature management to emit telemetry with an implementation of `ITelemetryPublisher` called `MyTelemetryPublisher`.
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``` C#
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builder.services
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.AddFeatureManagement()
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.AddTelemetryPublisher<MyTelemetryPublisher>();
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```
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### Application Insights Telemetry Publisher
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The `Microsoft.FeatureManagement.Telemetry.ApplicationInsights` package provides a built-in telemetry publisher implementation that sends feature flag evaluation data to [Application Insights](/azure/azure-monitor/app/app-insights-overview). To take advantage of this, add a reference to the package and register the Application Insights telemetry publisher as shown below.
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The `Microsoft.FeatureManagement.Telemetry.ApplicationInsights`package provides a built-in telemetry publisher that sends feature flag evaluation data to [Application Insights](/azure/azure-monitor/app/app-insights-overview). To take advantage ofthis, add a reference to the package and register the Application Insights telemetry publisher as shown below.
The `Microsoft.FeatureManagement.Telemetry.ApplicationInsights`package provides a telemetry initializer that automatically tags all events with`TargetingId` so that events may be linked to flag evaluations. To use the telemetry initializer, [`TargetingTelemetryInitializer`](https://github.com/microsoft/FeatureManagement-Dotnet/blob/preview/src/Microsoft.FeatureManagement.Telemetry.ApplicationInsights/TargetingTelemetryInitializer.cs), add it into the application's service collection.
> The base `Microsoft.FeatureManagement` package doesn't include this telemetry publisher.
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> To ensure that `TargetingTelemetryInitializer` works as expected, the `TargetingHttpContextMiddleware` described below should be used.
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To enable persistance of targeting context in the current activity, you can use the [`TargetingHttpContextMiddleware`](https://github.com/microsoft/FeatureManagement-Dotnet/blob/preview/src/Microsoft.FeatureManagement.AspNetCore/TargetingHttpContextMiddleware.cs).
An example of its usage can be found in the [EvaluationDataToApplicationInsights](https://github.com/microsoft/FeatureManagement-Dotnet/tree/preview/examples/EvaluationDataToApplicationInsights) example.
Copy file name to clipboardExpand all lines: articles/azure-arc/servers/prerequisites.md
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title: Connected Machine agent prerequisites
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description: Learn about the prerequisites for installing the Connected Machine agent for Azure Arc-enabled servers.
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ms.date: 06/19/2024
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ms.date: 07/29/2024
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ms.topic: conceptual
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* Restored from backup as a second instance of the server
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* Used to create a "golden image" from which other virtual machines are created
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If two agents use the same configuration, you will encounter inconsistent behaviors when both agents try to act as one Azure resource. The best practice for these situations is to use an automation tool or script to onboard the server to Azure Arc after it has been cloned, restored from backup, or created from a golden image.
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If two agents use the same configuration, you'll encounter inconsistent behaviors when both agents try to act as one Azure resource. The best practice for these situations is to use an automation tool or script to onboard the server to Azure Arc after its cloned, restored from backup, or created from a golden image.
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> [!NOTE]
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> For additional information on using Azure Arc-enabled servers in VMware environments, see the [VMware FAQ](vmware-faq.md).
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## Supported operating systems
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Azure Arc supports the following Windows and Linux operating systems. Only x86-64 (64-bit) architectures are supported. The Azure Connected Machine agent does not run on x86 (32-bit) or ARM-based architectures.
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Azure Arc supports the following Windows and Linux operating systems. Only x86-64 (64-bit) architectures are supported. The Azure Connected Machine agent doesn't run on x86 (32-bit) or ARM-based architectures.
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* AlmaLinux 9
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* Amazon Linux 2 and 2023
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* Azure Linux (CBL-Mariner) 1.0, 2.0
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* Azure Linux (CBL-Mariner) 2.0
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* Azure Stack HCI
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* Debian 10, 11, and 12
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* Debian 11, and 12
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* Oracle Linux 7, 8, and 9
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* Red Hat Enterprise Linux (RHEL) 7, 8 and 9
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* Rocky Linux 8 and 9
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* SUSE Linux Enterprise Server (SLES) 12 SP3-SP5 and 15
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* Ubuntu 18.04, 20.04, and 22.04 LTS
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* Windows 10, 11 (see [client operating system guidance](#client-operating-system-guidance))
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* Windows IoT Enterprise
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* Windows Server 2012, 2012 R2, 2016, 2019, and 2022
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* Both Desktop and Server Core experiences are supported
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* Azure Editions are supported on Azure Stack HCI
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The Azure Connected Machine agent hasn't been tested on operating systems hardened by the Center for Information Security (CIS) Benchmark.
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The Azure Connected Machine agent isn't tested on operating systems hardened by the Center for Information Security (CIS) Benchmark.
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> [!NOTE]
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> [Azure Connected Machine agent version 1.44](agent-release-notes.md#version-144---july-2024) is the last version to officially support Debian 10, Ubuntu 16.04, and Azure Linux (CBL-Mariner) 1.0.
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| -- | -- | -- | -- |
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| Windows Server 2008 R2 SP1 | 1.39 [Download](https://aka.ms/AzureConnectedMachineAgent-1.39)| 03/31/2025 | Windows Server 2008 and 2008 R2 reached End of Support in January 2020. See [End of support for Windows Server 2008 and Windows Server 2008 R2](/troubleshoot/windows-server/windows-server-eos-faq/end-of-support-windows-server-2008-2008r2). |
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| CentOS 7 and 8 | 1.42 | 05/31/2025 | See the [CentOS End Of Life guidance](~/articles/virtual-machines/workloads/centos/centos-end-of-life.md). |
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| Debian 10 | 1.44 | 07/15/2025 ||
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| Ubuntu 16.04 | 1.44 | 07/15/2025 ||
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| Azure Linux (CBL-Mariner) 1.0 | 1.44 | 07/15/2025 ||
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