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articles/active-directory/user-help/multi-factor-authentication-end-user-troubleshoot.md

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ms.workload: identity
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ms.service: active-directory
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ms.subservice: user-help
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ms.topic: troubleshooting
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ms.topic: conceptual
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ms.date: 02/20/2020
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ms.author: curtand
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ms.reviewer: kexia

articles/automation/automation-runbook-output-and-messages.md

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articles/azure-netapp-files/azure-netapp-files-faqs.md

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ms.tgt_pltfrm: na
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ms.devlang: na
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ms.topic: conceptual
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ms.date: 03/04/2020
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ms.date: 03/06/2020
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ms.author: b-juche
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---
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# FAQs About Azure NetApp Files
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### Why does the available space on my SMB client not show the provisioned size?
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The volume size reported by the SMB client is the maximum size the Azure NetApp Files volume can grow to. The size of the Azure NetApp Files volume as shown on the SMB client is not reflective of the quota or size of the volume. You can get the Azure NetApp Files volume size or quota through the Azure portal or the API.
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<!--
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### Does Azure NetApp Files support LDAP signing?
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Yes, Azure NetApp Files supports LDAP signing by default. This functionality enables secure LDAP lookups between the Azure NetApp Files service and the user-specified [Active Directory Domain Services domain controllers](https://docs.microsoft.com/windows/win32/ad/active-directory-domain-services). For more information, see [ADV190023 | Microsoft Guidance for Enabling LDAP Channel Binding and LDAP Signing](https://portal.msrc.microsoft.com/en-us/security-guidance/advisory/ADV190023).
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-->
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## Capacity management FAQs
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---
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title: "Select a domain for a Custom Vision project - Computer Vision"
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titleSuffix: Azure Cognitive Services
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description: This article will show you how to select a domain for your project in the Custom Vision Service.
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services: cognitive-services
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author: shonohs
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manager: nitinme
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ms.service: cognitive-services
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ms.subservice: custom-vision
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ms.topic: conceptual
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ms.date: 03/06/2020
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ms.author: shono
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---
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# Select a domain for a Custom Vision project
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From the settings blade for your Custom Vision project, you can select a domain for your project. Choose the domain that is closest to your scenario.
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## Image Classification
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|Domain|Purpose|
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|---|---|
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|__Generic__| Optimized for a broad range of image classification tasks. If none of the other domains are appropriate, or you're unsure of which domain to choose, select the Generic domain.|
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|__Food__|Optimized for photographs of dishes as you would see them on a restaurant menu. If you want to classify photographs of individual fruits or vegetables, use the Food domain.|
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|__Landmarks__|Optimized for recognizable landmarks, both natural and artificial. This domain works best when the landmark is clearly visible in the photograph. This domain works even if the landmark is slightly obstructed by people in front of it.|
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|__Retail__|Optimized for images that are found in a shopping catalog or shopping website. If you want high precision classifying between dresses, pants, and shirts, use this domain.|
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|__Compact domains__| Optimized for the constraints of real-time classification on edge devices.|
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## Object Detection
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|Domain|Purpose|
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|---|---|
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|__General__| Optimized for a broad range of object detection tasks. If none of the other domains are appropriate, or you are unsure of which domain to choose, select the Generic domain.|
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|__Logo__|Optimized for finding brand logos in images.|
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|__Products on shelves__|Optimized for detecting and classifying products on shelves.|
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|__Compact domains__| Optimized for the constraints of real-time object detection on edge devices.|
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## Compact domains
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The models generated by compact domains can be exported to run locally. Model performance varies by selected domain. In the table below, we report the model size and inference time on Intel Desktop CPU and NVidia GPU \[1\].
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> [!NOTE]
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> These numbers don't include preprocessing and postprocessing time.
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|Task|Domain|Model Size|CPU inference time|GPU inference time|
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|---|---|---|---|---|
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|Classification|General (compact)|5 MB|13 ms|5 ms|
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|Object Detection|General (compact)|45 MB|35 ms|5 ms|
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|Object Detection|General (compact) [S1]|14 MB|27 ms|7 ms|
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## VAIDK (Vision AI Dev Kit)
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When a compact domain is selected an extra option "Export Capabilities" is provided allowing for distinguishing between "Basic Platforms" and "Vision AI Dev Kit".
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Under _Export Capabilities_ the two options are:
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- Basic platforms (Tensorflow, CoreML, ONNX, etc.)
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- Vision AI Dev Kit.
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When _Vision AI Dev Kit_ is selected the _Generic_, _Landmarks_, and _Retail_ but not the _Food_ compact domains are available for Image Classification while both _General (compact)_ and _General (compact) [S1]_ are available for object detection.
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>[!NOTE]
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>__General (compact)__ domain for Object Detection requires special postprocessing logic. For the detail, please see an example script in the exported zip package. If you need a model without the postprocessing logic, use __General (compact) [S1]__.
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>[!IMPORTANT]
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>There is no guarantee that the exported models give the exactly same result as the prediction API on the cloud. Slight difference in the running platform or the preprocessing implementation can cause larger difference in the model outputs. For the detail of the preprocessing logic, please see [this document](python-tutorial.md).
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\[1\] Intel Xeon E5-2690 CPU and NVIDIA Tesla M60

articles/cognitive-services/Custom-Vision-Service/toc.yml

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href: test-your-model.md
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- name: Improve your classifier
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href: getting-started-improving-your-classifier.md
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- name: Select a domain
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href: select-domain.md
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- name: Use the prediction API
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href: use-prediction-api.md
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- name: Use Smart Labeler

articles/cognitive-services/Face/Tutorials/FaceAPIinCSharpTutorial.md

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1. In Visual Studio, open the New Project dialog. Expand **Installed**, then **Visual C#**, then select **WPF App (.NET Framework)**.
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1. Name the application **FaceTutorial**, then click **OK**.
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1. Get the required NuGet packages. Right-click on your project in the Solution Explorer and select **Manage NuGet Packages**; then, find and install the following package:
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- [Microsoft.Azure.CognitiveServices.Vision.Face 2.2.0-preview](https://www.nuget.org/packages/Microsoft.Azure.CognitiveServices.Vision.Face/2.2.0-preview)
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- [Microsoft.Azure.CognitiveServices.Vision.Face 2.5.0-preview.1](https://www.nuget.org/packages/Microsoft.Azure.CognitiveServices.Vision.Face/2.5.0-preview.1)
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## Add the initial code
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articles/dev-spaces/how-to/install-dev-spaces.md

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This article shows you several ways to enable Azure Dev Spaces on an AKS cluster as well as install the client-side tools.
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## Enable Azure Dev Spaces using the CLI
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## Enable or remove Azure Dev Spaces using the CLI
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Before you can enable Dev Spaces using the CLI, you need:
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* An Azure subscription. If you don't have an Azure subscription, you can create a [free account][az-portal-create-account].
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The `use-dev-spaces` command also installs the Azure Dev Spaces CLI.
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## Enable Azure Dev Spaces using the Azure portal
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To remove Azure Dev Spaces from your AKS cluster, use the `azds remove` command. For example:
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```azurecli
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$ azds remove -g MyResourceGroup -n MyAKS
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Azure Dev Spaces Controller 'MyAKS' in resource group 'MyResourceGroup' that targets resource 'MyAKS' in resource group 'MyResourceGroup' will be deleted. This will remove Azure Dev Spaces instrumentation from the target resource for new workloads. Continue? (y/N): y
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Deleting Azure Dev Spaces Controller 'MyAKS' in resource group 'MyResourceGroup' that targets resource 'MyAks' in resource group 'MyResourceGroup' (takes a few minutes)...
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```
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The above command removes Azure Dev Spaces from the *MyAKS* cluster in *MyResourceGroup*. Any namespaces you created with Azure Dev Spaces will remain along with their workloads, but new workloads in those namespaces will not be instrumented with Azure Dev Spaces. In addition, if you restart any existing pods instrumented with Azure Dev Spaces, you may see errors. Those pods must be redeployed without Azure Dev Spaces tooling. To fully remove Azure Dev Spaces from your cluster, delete all pods in all namespaces where Azure Dev Spaces was enabled.
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## Enable or remove Azure Dev Spaces using the Azure portal
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Enabling Azure Dev Spaces using the Azure portal **does not** install any client-side tools for Azure Dev Spaces.
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To remove Azure Dev Spaces from your AKS cluster, change *Enable Dev Spaces* to *No* and click *Save*. Any namespaces you created with Azure Dev Spaces will remain along with their workloads, but new workloads in those namespaces will not be instrumented with Azure Dev Spaces. In addition, if you restart any existing pods instrumented with Azure Dev Spaces, you may see errors. Those pods must be redeployed without Azure Dev Spaces tooling. To fully remove Azure Dev Spaces from your cluster, delete all pods in all namespaces where Azure Dev Spaces was enabled.
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## Install the client-side tools
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You can use the Azure Dev Spaces client-side tools to interact with dev spaces on an AKS cluster from your local machine. There are several ways to install the client-side tools:

articles/kinect-dk/hardware-specification.md

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Azure Kinect DK is a USB3 composite device that exposes the following hardware endpoints to the operating system:
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Vendor ID is 0x045E (Microsoft), Product ID table below:
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Vendor ID is 0x045E (Microsoft). Product ID table is shown below:
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| USB Interface | PNP IP | Notes |
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|-------------------------|--------------|----------------------|

articles/service-health/azure-status-overview.md

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While the Azure status page always shows the latest health information, you can view older events using the [Azure status history page](https://status.azure.com/status/history/).
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There you can see events up to 90 days in the past, including a preliminary root cause, mitigation, and next steps for Azure.
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Starting with events that were published in November 2019, the status history page shows events up to 365 days in the past, including a preliminary root cause, mitigation, and next steps for Azure.
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## RSS Feed
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articles/service-health/resource-health-checks-resource-types.md

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## Microsoft.classiccompute/virtualmachines
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|Executed Checks|
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|<ul><li>Is the host server up and running?</li><li>Has the host OS booting completed?</li><li>Is the virtual machine container provisioned and powered up?</li><li>Is there network connectivity between the host and the storage account?</li><li>Has the booting of the guest OS completed?</li><li>Is there ongoing planned maintenance?</li></ul>|
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|<ul><li>Is the host server up and running?</li><li>Has the host OS booting completed?</li><li>Is the virtual machine container provisioned and powered up?</li><li>Is there network connectivity between the host and the storage account?</li><li>Has the booting of the guest OS completed?</li><li>Is there ongoing planned maintenance?</li><li>Is the host hardware degraded and predicted to fail soon?</li></ul>|
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|Executed Checks|
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## Microsoft.compute/virtualmachines
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|<ul><li>Is the server hosting this virtual machine up and running?</li><li>Has the host OS booting completed?</li><li>Is the virtual machine container provisioned and powered up?</li><li>Is there network connectivity between the host and the storage account?</li><li>Has the booting of the guest OS completed?</li><li>Is there ongoing planned maintenance?</li></ul>|
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|<ul><li>Is the server hosting this virtual machine up and running?</li><li>Has the host OS booting completed?</li><li>Is the virtual machine container provisioned and powered up?</li><li>Is there network connectivity between the host and the storage account?</li><li>Has the booting of the guest OS completed?</li><li>Is there ongoing planned maintenance?</li><li>Is the host hardware degraded and predicted to fail soon?</li></ul>|
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## Microsoft.datafactory/factories
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|Executed Checks|

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