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articles/azure-monitor/agents/azure-monitor-agent-send-data-to-event-hubs-and-storage.md

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"authentication": {
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"managedIdentity": {
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"identifier-type": "mi_res_id",
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"identifier-name": "mi_res_id",
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"identifier-value": "[resourceID('Microsoft.ManagedIdentity/userAssignedIdentities/',parameters('identityName'))]"
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}
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}
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"authentication": {
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"managedIdentity": {
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"identifier-type": "mi_res_id",
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"identifier-name": "mi_res_id",
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"identifier-value": "[resourceID('Microsoft.ManagedIdentity/userAssignedIdentities/',parameters('identityName'))]"
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}
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}

articles/copilot/analyze-cost-management.md

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- "Can you provide an estimate of our expected expenses for the next 6 months?"
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- "Show me the resource group with the highest spending in the last 6 months."
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- "How can we reduce our costs?"
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- "Which resources are covered by savings plans?
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- "Which resources are covered by savings plans?"
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## Examples
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articles/frontdoor/how-to-configure-endpoints.md

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## Prerequisites
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Before you can create a new endpoint with Front Door manager, you must have an Azure Front Door profile created. To create an Azure Front Door profile, see [create a Azure Front Door](create-front-door-portal.md). The profile must have at least one endpoint.
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Before you can create a new endpoint with Front Door manager, you must have an Azure Front Door profile created. To create an Azure Front Door profile, see [create an Azure Front Door](create-front-door-portal.md). The profile must have at least one endpoint.
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## Create a new Front Door endpoint
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articles/governance/policy/concepts/initiative-definition-structure.md

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initiative definition, you can reuse that parameter in the included policies.
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> [!NOTE]
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> Once an initiative is assigned, initative level parameters can't be altered. Due to this, the
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> Once an initiative is assigned, initiative level parameters can't be altered. Due to this, the
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> recommendation is to set a **defaultValue** when defining the parameter.
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### Parameter properties

articles/machine-learning/concept-mlflow-models.md

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## The MLmodel format
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MLflow adopts the MLmodel format as a way to create a contract between the artifacts and what they represent. The MLmodel format stores assets in a folder. Among these assets, there's a file named `MLmodel`. this file is the single source of truth about how a model can be loaded and used.
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MLflow adopts the MLmodel format as a way to create a contract between the artifacts and what they represent. The MLmodel format stores assets in a folder. Among these assets, there's a file named `MLmodel`. This file is the single source of truth about how a model can be loaded and used.
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The following screenshot shows a sample MLflow model's folder in the Azure Machine Learning studio. The model is placed in a folder called `credit_defaults_model`. There is no specific requirement on the naming of this folder. The folder contains the `MLmodel` file among other model artifacts.
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articles/role-based-access-control/built-in-roles.md

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> | **DataActions** | |
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> | [Microsoft.Compute](resource-provider-operations.md#microsoftcompute)/virtualMachines/login/action | Log in to a virtual machine as a regular user |
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> | [Microsoft.Compute](resource-provider-operations.md#microsoftcompute)/virtualMachines/loginAsAdmin/action | Log in to a virtual machine with Windows administrator or Linux root user privileges |
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> | [Microsoft.HybridCompute](resource-provider-operations.md#microsofthybridcompute)/machines/login/action | Log in to a Azure Arc machine as a regular user |
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> | [Microsoft.HybridCompute](resource-provider-operations.md#microsofthybridcompute)/machines/loginAsAdmin/action | Log in to a Azure Arc machine with Windows administrator or Linux root user privilege |
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> | [Microsoft.HybridCompute](resource-provider-operations.md#microsofthybridcompute)/machines/login/action | Log in to an Azure Arc machine as a regular user |
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> | [Microsoft.HybridCompute](resource-provider-operations.md#microsofthybridcompute)/machines/loginAsAdmin/action | Log in to an Azure Arc machine with Windows administrator or Linux root user privilege |
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> | **NotDataActions** | |
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> | *none* | |
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articles/virtual-machines/image-version.md

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# Create an image definition and an image version
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A [Azure Compute Gallery](shared-image-galleries.md) (formerly known as Shared Image Gallery) simplifies custom image sharing across your organization. Custom images are like marketplace images, but you create them yourself. Images can be created from a VM, VHD, snapshot, managed image, or another image version.
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An [Azure Compute Gallery](shared-image-galleries.md) (formerly known as Shared Image Gallery) simplifies custom image sharing across your organization. Custom images are like marketplace images, but you create them yourself. Images can be created from a VM, VHD, snapshot, managed image, or another image version.
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The Azure Compute Gallery lets you share your custom VM images with others in your organization, within or across regions, within a Microsoft Entra tenant, or publicly using a [community gallery](azure-compute-gallery.md#community). Choose which images you want to share, which regions you want to make them available in, and who you want to share them with. You can create multiple galleries so that you can logically group images. Many new features like ARM64, Accelerated Networking and TrustedVM are only supported through Azure Compute Gallery and not available for managed images.
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