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articles/applied-ai-services/form-recognizer/concept-custom.md

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* [**JavaScript**](https://github.com/Azure/azure-sdk-for-js/blob/main/sdk/formrecognizer/ai-form-recognizer/samples/v3/javascript/createComposedModel.js).
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* [**Python**](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/formrecognizer/azure-ai-formrecognizer/samples/sample_create_composed_model.py)
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* [**Python**](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/formrecognizer/azure-ai-formrecognizer/samples/v3.2-beta/sample_create_composed_model.py)
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---
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* [**JavaScript**](https://github.com/Azure/azure-sdk-for-js/blob/main/sdk/formrecognizer/ai-form-recognizer/samples/v3/javascript/recognizeCustomForm.js)
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* [**Python**](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/formrecognizer/azure-ai-formrecognizer/samples/sample_recognize_custom_forms.py)
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* [**Python**](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/formrecognizer/azure-ai-formrecognizer/samples/v3.1/sample_recognize_custom_forms.py)
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articles/applied-ai-services/form-recognizer/generate-sas-tokens.md

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:::image type="content" source="media/sas-tokens/upload-blob-window.png" alt-text="Screenshot: upload blob window in the Azure portal.":::
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> [!NOTE]
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> By default, the REST API will use form documents that are located at the root of your container. However, you can use data organized in subfolders if specified in the API call. *See* [**Organize your data in subfolders**](/azure/applied-ai-services/form-recognizer/build-training-data-set.md#organize-your-data-in-subfolders-optional)
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> By default, the REST API will use form documents that are located at the root of your container. However, you can use data organized in subfolders if specified in the API call. *See* [**Organize your data in subfolders**](/azure/applied-ai-services/form-recognizer/build-training-data-set#organize-your-data-in-subfolders-optional)
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## Create a SAS with the Azure portal
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articles/azure-arc/servers/plan-evaluate-on-azure-virtual-machine.md

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---
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title: How to evaluate Azure Arc-enabled servers with an Azure VM
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description: Learn how to evaluate Azure Arc-enabled servers using an Azure virtual machine.
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ms.date: 09/20/2021
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ms.date: 10/01/2021
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ms.topic: conceptual
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---
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While you cannot install Azure Arc-enabled servers on an Azure VM for production scenarios, it is possible to configure Azure Arc-enabled servers to run on an Azure VM for *evaluation and testing purposes only*. This article will help you set up an Azure VM before you can enable Azure Arc-enabled servers on it.
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> [!NOTE]
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> The steps in this article are intended for virtual machines hosted in the Azure cloud. Azure Arc-enabled servers is not supported on virtual machines running on Azure Stack Hub or Azure Stack Edge.
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## Prerequisites
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* Your account is assigned to the [Virtual Machine Contributor](../../role-based-access-control/built-in-roles.md#virtual-machine-contributor) role.

articles/azure-government/compare-azure-government-global-azure.md

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articles/azure-resource-manager/bicep/bicep-functions-string.md

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author: mumian
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ms.topic: conceptual
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ms.date: 10/01/2021
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# String functions for Bicep
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## concat
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Instead of using the concat function, use string interpolation.
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Instead of using the concat function, use string interpolation.
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```bicep
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param prefix string = 'prefix'
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```
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If you need to create a new unique name each time you deploy a Bicep file, and don't intend to update the resource, you can use the [utcNow](./bicep-functions-date.md#utcnow) function with uniqueString. You could use this approach in a test environment. For an example, see [utcNow](./bicep-functions-date.md#utcnow).
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If you need to create a new unique name each time you deploy a Bicep file, and don't intend to update the resource, you can use the [utcNow](./bicep-functions-date.md#utcnow) function with uniqueString. You could use this approach in a test environment. For an example, see [utcNow](./bicep-functions-date.md#utcnow). Note the utcNow function can only be used within an expression for the default value of a parameter.
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### Return value
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articles/azure-resource-manager/templates/deploy-to-resource-group.md

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title: Deploy resources to resource groups
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description: Describes how to deploy resources in an Azure Resource Manager template. It shows how to target more than one resource group.
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ms.topic: conceptual
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ms.date: 10/01/2021
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ms.custom: devx-track-azurepowershell
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---
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-Name demoRGDeployment `
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-ResourceGroupName ExampleGroup `
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-TemplateUri https://raw.githubusercontent.com/Azure/azure-quickstart-templates/master/quickstarts/microsoft.storage/storage-account-create/azuredeploy.json `
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-storageAccountType Standard_GRS `
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-storageAccountType Standard_GRS
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```
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articles/azure-video-analyzer/video-analyzer-docs/access-public-endpoints-networking.md

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* **Authentication and Authorization**: initial authentication is done through a short-lived Provisioning Token issued by the Video Analyzer management APIs. Once the initial handshake is completed, the module and service exchange a set of auto-rotating authorization keys that are used from this point forward.
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* **Requirement**: access to this endpoint is required for the correct functioning of the Video Analyzer Edge module. The edge module will stop functioning if this endpoint cannot be reached within a period of 36 hours.
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## Telemetry:
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## Telemetry
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* **Purpose**: optional periodic submission of telemetry data which enables Microsoft to better understand how the Video Analyzer edge module is used and proactively identify future improvements that can be done on compatibility, performance, and other product areas.
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> [!NOTE]
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> The list of endpoints described in this article, is not meant to be a comprehensive list of the associated service endpoints. It is an informative list of the endpoints which are required for the normal operation of Video Analyzer. Refer to each individual Azure service documentation for a complete list of the endpoints exposed by each respective service.
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## Azure Storage
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* **Purpose**: to record audio, video, and inference data when pipelines(TODO: link) are configured to store video on the cloud via the Video Sink (TODO: link to section in pipeline.md) node.
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* **Authentication and Authorization**: authorization is performed by standard Azure Storage service authentication and authorization enforcement. In this case, storage is accessed through container specific SAS URLs.
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* **Requirement**: access to this endpoint is only required when a Video Analyzer edge pipeline is configured to archive the video to the cloud.
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## IoT Hub
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* **Purpose**: control and data plane for Azure IoT Hub and Edge Devices.
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* **Authentication and Authorization**: please refer to the Azure IoT Hub documentation.
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* **Requirement**: Properly configured and functioning edge device with Azure IoT Edge Runtime is required to ensure that the Azure Video Analyzer edge module operates correctly.
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## 1.1.4 TLS encryption
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* **Encryption and Server Authentication**: all Video Analyzer endpoints are exposed through TLS 1.2 compliant endpoints.
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## 1.1.5 References
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Public:
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* [Azure Resource Manager overview](../../azure-resource-manager/management/overview.md)
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* [Understand Azure IoT Hub endpoints](../../iot-hub/iot-hub-devguide-endpoints.md)
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* [What is Azure Private Link?](../../private-link/private-link-overview.md)
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* [Azure service tags overview](../../virtual-network/service-tags-overview.md)
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## Next steps
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[Access Policies](access-policies.md)
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