Merge branch 'master' of https://github.com/MicrosoftDocs/azure-docs-pr into rolyon-rbac-rest-examples

rolyon · rolyon · commit 87b6ae6e939d · 2020-05-06T18:28:46.000-07:00
diff --git a/articles/active-directory/saas-apps/amazon-web-service-tutorial.md b/articles/active-directory/saas-apps/amazon-web-service-tutorial.md
@@ -13,7 +13,7 @@ ms.subservice: saas-app-tutorial
 ms.workload: identity
 ms.tgt_pltfrm: na
 ms.topic: tutorial
-ms.date: 01/31/2020
+ms.date: 04/20/2020
 ms.author: jeedes
 
 ms.collection: M365-identity-device-management
@@ -108,6 +108,18 @@ Follow these steps to enable Azure AD SSO in the Azure portal.
 
     `https://signin.aws.amazon.com/saml#2`
 
+1. AWS application expects the SAML assertions in a specific format, which requires you to add custom attribute mappings to your SAML token attributes configuration. The following screenshot shows the list of default attributes.
+
+	![image](common/default-attributes.png)
+
+1. In addition to above, AWS application expects few more attributes to be passed back in SAML response which are shown below. These attributes are also pre populated but you can review them as per your requirements.
+	
+	| Name  | Source attribute  | Namespace |
+	| --------------- | --------------- | --------------- |
+	| RoleSessionName | user.userprincipalname | https://aws.amazon.com/SAML/Attributes |
+	| Role 			  | user.assignedroles |  https://aws.amazon.com/SAML/Attributes |
+	| SessionDuration 			  | "provide a value between 900 seconds (15 minutes) to 43200 seconds (12 hours)" |  https://aws.amazon.com/SAML/Attributes |
+
 1. On the **Set up single sign-on with SAML** page, in the **SAML Signing Certificate** section,  find **Federation Metadata XML** and select **Download** to download the certificate and save it on your computer.
 
     ![The Certificate download link](common/metadataxml.png)
@@ -397,4 +409,4 @@ When you click the Amazon Web Services (AWS) tile in the Access Panel, you shoul
 [38]: ./media/amazon-web-service-tutorial/tutorial_amazonwebservices_createnewaccesskey.png
 [39]: ./media/amazon-web-service-tutorial/tutorial_amazonwebservices_provisioning_automatic.png
 [40]: ./media/amazon-web-service-tutorial/tutorial_amazonwebservices_provisioning_testconnection.png
-[41]: ./media/amazon-web-service-tutorial/tutorial_amazonwebservices_provisioning_on.png
+[41]: ./media/amazon-web-service-tutorial/tutorial_amazonwebservices_provisioning_on.png
diff --git a/articles/key-vault/general/toc.yml b/articles/key-vault/general/toc.yml
@@ -15,7 +15,7 @@
       href: tutorial-net-windows-virtual-machine.md
     - name: Use Azure Key Vault with a Windows virtual machine in Python
       href: tutorial-python-windows-virtual-machine.md
-  - name: Use Azure Key Vault with an Azure web app in .NET
+  - name:  Connect Key Vault to an Web App with a managed identity and .NET
     href: tutorial-net-create-vault-azure-web-app.md
 
 - name: Concepts 
diff --git a/articles/key-vault/general/tutorial-net-create-vault-azure-web-app.md b/articles/key-vault/general/tutorial-net-create-vault-azure-web-app.md
diff --git a/articles/key-vault/secrets/quick-create-net-v3.md b/articles/key-vault/secrets/quick-create-net-v3.md
@@ -34,7 +34,7 @@ Azure Key Vault helps safeguard cryptographic keys and secrets used by cloud app
 ## Prerequisites
 
 * An Azure subscription - [create one for free](https://azure.microsoft.com/free/?WT.mc_id=A261C142F).
-* The [.NET Core 2.1 SDK or later](https://dotnet.microsoft.com/download/dotnet-core/2.1).
+* The [.NET Core 3.1 SDK or later](https://dotnet.microsoft.com/download/dotnet-core/3.1).
 * [Azure CLI](/cli/azure/install-azure-cli?view=azure-cli-latest) or [Azure PowerShell](/powershell/azure/overview)
 
 This quickstart assumes you are running `dotnet`, [Azure CLI](/cli/azure/install-azure-cli?view=azure-cli-latest), and Windows commands in a Windows terminal (such as [PowerShell Core](/powershell/scripting/install/installing-powershell-core-on-windows?view=powershell-6), [Windows PowerShell](/powershell/scripting/install/installing-windows-powershell?view=powershell-6), or the [Azure Cloud Shell](https://shell.azure.com/)).
diff --git a/articles/key-vault/secrets/quick-create-net.md b/articles/key-vault/secrets/quick-create-net.md
@@ -27,7 +27,7 @@ Azure Key Vault helps safeguard cryptographic keys and secrets used by cloud app
 ## Prerequisites
 
 * An Azure subscription - [create one for free](https://azure.microsoft.com/free/?WT.mc_id=A261C142F).
-* The [.NET Core 2.1 SDK or later](https://dotnet.microsoft.com/download/dotnet-core/2.1).
+* The [.NET Core 3.1 SDK or later](https://dotnet.microsoft.com/download/dotnet-core/2.1).
 * [Azure CLI](/cli/azure/install-azure-cli?view=azure-cli-latest) or [Azure PowerShell](/powershell/azure/overview)
 
 This quickstart assumes you are running `dotnet`, [Azure CLI](/cli/azure/install-azure-cli?view=azure-cli-latest), and Windows commands in a Windows terminal (such as [PowerShell Core](/powershell/scripting/install/installing-powershell-core-on-windows?view=powershell-6), [Windows PowerShell](/powershell/scripting/install/installing-windows-powershell?view=powershell-6), or the [Azure Cloud Shell](https://shell.azure.com/)).
diff --git a/articles/key-vault/secrets/toc.yml b/articles/key-vault/secrets/toc.yml
@@ -15,11 +15,11 @@
     href: quick-create-powershell.md
   - name: Portal
     href: quick-create-portal.md
+  - name: .NET (SDK v4)
+    href: quick-create-net.md
   - name: .NET (SDK v3)
     href: quick-create-net-v3.md
-  - name: .NET (v4)
-    href: quick-create-net.md
-  - name: Node.js (v4)
+  - name: Node.js
     href: quick-create-node.md
   - name: Python
     href: quick-create-python.md
diff --git a/articles/lab-services/classroom-labs/class-type-deep-learning-natural-language-processing.md b/articles/lab-services/classroom-labs/class-type-deep-learning-natural-language-processing.md
@@ -36,7 +36,7 @@ Follow [this tutorial](tutorial-setup-classroom-lab.md) to create a new lab and
 | ------------ | ------------------ |
 | Virtual machine (VM) size | Small GPU (Compute). This size is best suited for compute-intensive and network-intensive applications like Artificial Intelligence and Deep Learning. |
 | VM image | [Data Science Virtual Machine for Linux (Ubuntu)](https://azuremarketplace.microsoft.com/marketplace/apps/microsoft-dsvm.ubuntu-1804). This image provides deep learning frameworks and tools for machine learning and data science. To view the full list of installed tools on this image, see the following article: [What’s included on the DSVM?](../../machine-learning/data-science-virtual-machine/overview.md#whats-included-on-the-dsvm). |
-| Enable remote desktop connection | Enable. <p>Enabling this setting will allow teachers and students to connect to their Virtual Machines (VM) using Remote Desktop (RDP).</p><p>**Important**: RDP is already installed and configured on the Data Science Virtual Machine for Linux image. As a result, teachers/students can connect to VMs via RDP without any additional steps. Also, if you need to connect to the graphical desktop, this image already has [X2Go Server](https://wiki.x2go.org/doku.php/doc:newtox2go) installed on the virtual machine. Students must install X2Go client on their local machines and must use the client for connecting. For more information, see the following guides: <ul><li>[How to access the Data Science Virtual Machine for Linux](../../machine-learning/data-science-virtual-machine/dsvm-ubuntu-intro.md#how-to-access-the-ubuntu-data-science-virtual-machine)</li><li>[Connect to the template VM to install RDP and GUI packages](how-to-enable-remote-desktop-linux.md#connect-to-the-template-vm)</li></ul></p>   |
+| Enable remote desktop connection | <p>Enabling this setting will allow teachers and students to connect to their Virtual Machines (VM) using Remote Desktop (RDP).</p><p>**Important**: Enabling this setting only opens the **RDP** port on Linux machines. If RDP is already installed and configured on the virtual machine image, you/students can connect to VMs via RDP without following any additional steps. <p>If the VM image doesn't have RDP installed and configured, you need to connect to the Linux machine using SSH for the first time, and install RDP and GUI packages so that you/students can connect to the Linux machine using RDP later. For more information, see [Install and configure Remote Desktop to connect to a Linux VM in Azure](../../virtual-machines/linux/use-remote-desktop.md). Then, you publish the image so that students can RDP in to the student Linux VMs.  |
 
 The Data Science Virtual Machine for Linux image provides the necessary deep learning frameworks and tools required for this type of class. As a result, after the template machine creation, you don't need to customize it further. It can be published for students to use. Select the **Publish** button on template page to publish the template to the lab.  
 
diff --git a/articles/machine-learning/concept-plan-manage-cost.md b/articles/machine-learning/concept-plan-manage-cost.md
@@ -9,7 +9,7 @@ ms.reviewer: nigup
 ms.service: machine-learning
 ms.subservice: core
 ms.topic: conceptual
-ms.date: 04/22/2020
+ms.date: 05/08/2020
 ---
 
 # Plan and manage costs for Azure Machine Learning
@@ -58,7 +58,7 @@ View costs in graphs and tables for different time intervals. Some examples are
 
 You won't see a separate service area for Machine Learning.  Instead you'll see the various resources you've added to your Machine Learning workspaces.
 
-## Use AmlCompute
+## Use Azure Machine Learning compute cluster (AmlCompute)
 
 With constantly changing data, you need fast and streamlined model training and retraining to maintain accurate models. However, continuous training comes at a cost, especially for deep learning models on GPUs. 
 
@@ -134,5 +134,7 @@ Azure Machine Learning Compute supports reserved instances inherently. So if you
 
 ## Next steps
 
-* Learn more about managing costs with [cost analysis](../cost-management-billing/costs/quick-acm-cost-analysis.md).
-* Learn more about [Azure Machine Learning compute](how-to-set-up-training-targets.md#amlcompute).
+Learn more about:
+* [Manage and increase resource quotas](how-to-manage-quotas.md)
+* [Managing costs with [cost analysis](../cost-management-billing/costs/quick-acm-cost-analysis.md).
+* [Azure Machine Learning compute](how-to-set-up-training-targets.md#amlcompute).
diff --git a/articles/machine-learning/how-to-manage-quotas.md b/articles/machine-learning/how-to-manage-quotas.md
@@ -10,15 +10,16 @@ ms.topic: conceptual
 ms.reviewer: jmartens
 author: nishankgu
 ms.author: nigup
-ms.date: 03/05/2020
+ms.date: 05/08/2020
+ms.custom: contperfq4 
 ---
 
-# Manage and request quotas for Azure resources
+# Manage & increase quotas for resources with Azure Machine Learning
 [!INCLUDE [applies-to-skus](../../includes/aml-applies-to-basic-enterprise-sku.md)]
 
-This article provides details on preconfigured limits on Azure resources for your subscription. Also included are instructions on how to request quota enhancements for each type of resource. These limits are put in place to prevent budget over-runs due to fraud, and to honor Azure capacity constraints.
+This article provides [Azure Machine Learning](overview-what-is-azure-ml.md) users with details on preconfigured limits on Azure resources for your subscription. Also included are instructions on how to request quota enhancements for each type of resource. These limits are put in place to prevent budget over-runs due to fraud, and to honor Azure capacity constraints.
 
-As with other Azure services, there are limits on certain resources associated with Azure Machine Learning. These limits range from a cap on the number of workspaces to limits on the actual underlying compute that gets used for model training or inference/scoring. 
+As with other Azure services, there are limits on certain resources associated with Azure Machine Learning. These limits range from a cap on the number of [workspaces](concept-workspace.md) to limits on the actual underlying compute that gets used for model training or inference/scoring. 
 
 As you design and scale your Azure Machine Learning resources for production workloads, consider these limits. For example, if your cluster doesn't reach the target number of nodes, then you may have reached an Azure Machine Learning Compute cores limit for your subscription. If you want to raise the limit or quota above the Default Limit, open an online customer support request at no charge. The limits can't be raised above the Maximum Limit value shown in the following tables due to Azure Capacity constraints. If there is no Maximum Limit column, then the resource doesn't have adjustable limits.
 
@@ -44,10 +45,10 @@ Virtual machine cores have a regional total limit and a regional per size series
 
 [!INCLUDE [azure-subscription-limits-azure-resource-manager](../../includes/azure-subscription-limits-azure-resource-manager.md)]
 
-For a more detailed and up-to-date list of quota limits, check the Azure-wide quota article [here](https://docs.microsoft.com/azure/azure-resource-manager/management/azure-subscription-service-limits).
+For a more detailed and up-to-date list of quota limits, check the [Azure-wide quota article](https://docs.microsoft.com/azure/azure-resource-manager/management/azure-subscription-service-limits).
 
 ### Azure Machine Learning Compute
-For Azure Machine Learning Compute, there is a default quota limit on both the number of cores and number of unique compute resources allowed per region in a subscription. This quota is separate from the VM core quota above and the core limits are not shared between the two resource types since AmlCompute is a managed service that deploys resources in a hosted-on-behalf-of model.
+For [Azure Machine Learning Compute](concept-compute-target.md#azure-machine-learning-compute-managed), there is a default quota limit on both the number of cores and number of unique compute resources allowed per region in a subscription. This quota is separate from the VM core quota above and the core limits are not shared between the two resource types since AmlCompute is a managed service that deploys resources in a hosted-on-behalf-of model.
 
 Available resources:
 + Dedicated cores per region have a default limit of 24 - 300 depending on your subscription offer type with higher defaults for EA and CSP offer types.  The number of dedicated cores per subscription can be increased and is different for each VM family. Certain specialized VM families like NCv2, NCv3, or ND series start with a default of zero cores. Contact Azure support by raising a quota request to discuss increase options.
@@ -72,7 +73,7 @@ Available resources:
 <sup>2</sup> Jobs on a Low-Priority node could be preempted anytime there is a capacity constraint. We recommend you implement checkpointing in your job.
 
 ### Azure Machine Learning Pipelines
-For Azure Machine Learning Pipelines, there is a quota limit on the number of steps in a pipeline and on the number of schedule-based runs of published pipelines per region in a subscription.
+For [Azure Machine Learning Pipelines](concept-ml-pipelines.md), there is a quota limit on the number of steps in a pipeline and on the number of schedule-based runs of published pipelines per region in a subscription.
 - Maximum number of steps allowed in a pipeline is 30,000
 - Maximum number of the sum of schedule-based runs and blob pulls for blog-triggered schedules of published pipelines per subscription per month is 100,000
 
@@ -93,15 +94,15 @@ There is a limit on the number of storage accounts per region as well in a given
 
 ## Workspace level quota
 
-To better manage resource allocations for Amlcompute between various workspaces, we have introduced a feature that allows you to distribute subscription level quotas (by VM family) and configure them at the workspace level. The default behavior is that all workspaces have the same quota as the subscription level quota for any VM family. However, as the number of workspaces increases, and workloads of varying priority start sharing the same resources, users want a way to better share capacity and avoid resource contention issues. Azure Machine Learning provides a solution with its managed compute offering by allowing users to set a maximum quota for a particular VM family on each workspace. This is analogous to distributing your capacity between workspaces, and the users can choose to also over-allocate to drive maximum utilization. 
+To better manage resource allocations for Azure Machine Learning Compute target (Amlcompute) between various [workspaces](concept-workspace.md), we have introduced a feature that allows you to distribute subscription level quotas (by VM family) and configure them at the workspace level. The default behavior is that all workspaces have the same quota as the subscription level quota for any VM family. However, as the number of workspaces increases, and workloads of varying priority start sharing the same resources, users want a way to better share capacity and avoid resource contention issues. Azure Machine Learning provides a solution with its managed compute offering by allowing users to set a maximum quota for a particular VM family on each workspace. This is analogous to distributing your capacity between workspaces, and the users can choose to also over-allocate to drive maximum utilization. 
 
 To set quotas at the workspace level, go to any workspace in your subscription, and click on **Usages + quotas** in the left pane. Then select the **Configure quotas** tab to view the quotas, expand any VM family, and set a quota limit on any workspace listed under that VM family. Remember that you cannot set a negative value or a value higher than the subscription level quota. Also, as you would observe, by default all workspaces are assigned the entire subscription quota to allow for full utilization of the allocated quota.
 
 [![Azure Machine Learning workspace level quota](./media/how-to-manage-quotas/azure-machine-learning-workspace-quota.png)](./media/how-to-manage-quotas/azure-machine-learning-workspace-quota.png)
 
 
 > [!NOTE]
-> This is an Enterprise edition feature only. If you have both a Basic and an Enterprise edition workspace in your subscription, you can use this to only set quotas on your Enterprise workspaces. Your Basic workspaces will continue to have the subscription level quota which is the default behavior.
+> This is an Enterprise edition feature only. If you have both a [Basic and an Enterprise edition](overview-what-is-azure-ml.md#sku) workspace in your subscription, you can use this to only set quotas on your Enterprise workspaces. Your Basic workspaces will continue to have the subscription level quota which is the default behavior.
 >
 > You need subscription level permissions to set quota at the workspace level. This is enforced so that individual workspace owners do not edit or increase their quotas and start encroaching onto resources set aside for another workspace. Thus a subscription admin is best suited to allocate and distribute these quotas across workspaces.
 
@@ -132,9 +133,17 @@ Viewing your quota for various resources, such as Virtual Machines, Storage, Net
 
 If you want to raise the limit or quota above the default limit, [open an online customer support request](https://ms.portal.azure.com/#blade/Microsoft_Azure_Support/HelpAndSupportBlade/newsupportrequest/) at no charge.
 
-The limits can't be raised above the maximum limit value shown in the tables. If there is no maximum limit, then the resource doesn't have adjustable limits. [This](https://docs.microsoft.com/azure/azure-resource-manager/resource-manager-quota-errors) article covers the quota increase process in more detail.
+The limits can't be raised above the maximum limit value shown in the tables. If there is no maximum limit, then the resource doesn't have adjustable limits. [See step by step instructions on how to increase your quota](https://docs.microsoft.com/azure/azure-resource-manager/resource-manager-quota-errors).
 
 When requesting a quota increase, you need to select the service you are requesting to raise the quota against, which could be services such as Machine Learning service quota, Container instances or Storage quota. In addition for Azure Machine Learning Compute, you can click on the **Request Quota** button while viewing the quota following the steps above.
 
 > [!NOTE]
 > [Free Trial subscriptions](https://azure.microsoft.com/offers/ms-azr-0044p) are not eligible for limit or quota increases. If you have a [Free Trial subscription](https://azure.microsoft.com/offers/ms-azr-0044p), you can upgrade to a [Pay-As-You-Go](https://azure.microsoft.com/offers/ms-azr-0003p/) subscription. For more information, see [Upgrade Azure Free Trial to Pay-As-You-Go](../billing/billing-upgrade-azure-subscription.md) and  [Free Trial subscription FAQ](https://azure.microsoft.com/free/free-account-faq).
+
+## Next steps
+
+Learn more with these articles:
+
++ [Plan & manage costs for Azure Machine Learning](concept-plan-manage-cost.md)
+
++ [How to increase your quota](https://docs.microsoft.com/azure/azure-resource-manager/resource-manager-quota-errors).
diff --git a/articles/machine-learning/how-to-select-algorithms.md b/articles/machine-learning/how-to-select-algorithms.md
@@ -10,7 +10,7 @@ ms.topic: conceptual
 author: FrancescaLazzeri
 ms.author: lazzeri
 ms.reviewer: cgronlun
-ms.date: 03/05/2020
+ms.date: 05/07/2020
 ---
 # How to select algorithms for Azure Machine Learning
 
diff --git a/articles/machine-learning/resource-known-issues.md b/articles/machine-learning/resource-known-issues.md
diff --git a/articles/search/cognitive-search-custom-skill-interface.md b/articles/search/cognitive-search-custom-skill-interface.md
diff --git a/articles/service-fabric/service-fabric-versions.md b/articles/service-fabric/service-fabric-versions.md
