Merge branch 'master' of https://github.com/MicrosoftDocs/azure-docs-pr into rolyon-rbac-resource-access-link

Author: rolyon

articles/hdinsight/hdinsight-autoscale-clusters.md

@@ -7,7 +7,7 @@ ms.reviewer: jasonh
 ms.service: hdinsight
 ms.topic: conceptual
 ms.custom: hdinsightactive
-ms.date: 03/05/2020
+ms.date: 04/07/2020
 ---
 
 # Automatically scale Azure HDInsight clusters
@@ -34,9 +34,9 @@ The following table describes the cluster types and versions that are compatible
 
 ## How it works
 
-You can choose load-based scaling or schedule-based scaling for your HDInsight cluster. Load-based scaling changes the number of nodes in your cluster, within a range that you set, to ensure optimal CPU utilization and minimize running cost.
+You can choose load-based scaling or schedule-based scaling for your HDInsight cluster. Load-based scaling changes the number of nodes in your cluster, within a range that you set, to ensure optimal CPU usage and minimize running cost.
 
-Schedule-based scaling changes the number of nodes in your cluster based on conditions that take effect at specific times. These conditions scale the cluster to a desired number of nodes.
+Schedule-based scaling changes the number of nodes in your cluster based on conditions that take effect at specific times. These conditions scale the cluster to an intended number of nodes.
 
 ### Metrics monitoring
 
@@ -51,7 +51,7 @@ Autoscale continuously monitors the cluster and collects the following metrics:
 |Used Memory per Node|The load on a worker node. A worker node on which 10 GB of memory is used, is considered under more load than a worker with 2 GB of used memory.|
 |Number of Application Masters per Node|The number of Application Master (AM) containers running on a worker node. A worker node that is hosting two AM containers, is considered more important than a worker node that is hosting zero AM containers.|
 
-The above metrics are checked every 60 seconds. Autoscale makes scale-up and scale-down decisions based on these metrics.
+The above metrics are checked every 60 seconds. Autoscale makes decisions based on these metrics.
 
 ### Load-based scale conditions
 
@@ -62,9 +62,9 @@ When the following conditions are detected, Autoscale will issue a scale request
 |Total pending CPU is greater than total free CPU for more than 3 minutes.|Total pending CPU is less than total free CPU for more than 10 minutes.|
 |Total pending memory is greater than total free memory for more than 3 minutes.|Total pending memory is less than total free memory for more than 10 minutes.|
 
-For scale-up, the HDInsight service calculates how many new worker nodes are needed to meet the current CPU and memory requirements, and then issues a scale-up request to add the required number of nodes.
+For scale-up, Autoscale issues a scale-up request to add the required number of nodes. The scale-up is based on how many new worker nodes are needed to meet the current CPU and memory requirements.
 
-For scale-down, based on the number of AM containers per node and the current CPU and memory requirements, Autoscale issues a request to remove a certain number of nodes. The service also detects which nodes are candidates for removal based on current job execution. The scale down operation first decommissions the nodes, and then removes them from the cluster.
+For scale-down, Autoscale issues a request to remove a certain number of nodes. The scale-down is based on the number of AM containers per node. And the current CPU and memory requirements. The service also detects which nodes are candidates for removal based on current job execution. The scale down operation first decommissions the nodes, and then removes them from the cluster.
 
 ## Get started
 
@@ -74,7 +74,7 @@ To enable the Autoscale feature with load-based scaling, complete the following
 
 1. On the **Configuration + pricing** tab, select the **Enable autoscale** checkbox.
 1. Select **Load-based** under **Autoscale type**.
-1. Enter the desired values for the following properties:  
+1. Enter the intended values for the following properties:  
 
     * Initial **Number of nodes** for **Worker node**.
     * **Min** number of worker nodes.
@@ -103,11 +103,11 @@ The number of nodes must be between 3 and the maximum number of worker nodes tha
 
 ### Final creation steps
 
-For both load-based and schedule-based scaling, select the VM type for worker nodes by selecting a VM from the drop-down list under **Node size**. After you choose the VM type for each node type, you can see the estimated cost range for the whole cluster. Adjust the VM types to fit your budget.
+Select the VM type for worker nodes by selecting a VM from the drop-down list under **Node size**. After you choose the VM type for each node type, you can see the estimated cost range for the whole cluster. Adjust the VM types to fit your budget.
 
 ![Enable worker node schedule-based autoscale node size](./media/hdinsight-autoscale-clusters/azure-portal-cluster-configuration-pricing-vmsize.png)
 
-Your subscription has a capacity quota for each region. The total number of cores of your head nodes combined with the maximum number of worker nodes can't exceed the capacity quota. However, this quota is a soft limit; you can always create a support ticket to get it increased easily.
+Your subscription has a capacity quota for each region. The total number of cores of your head nodes and the maximum worker nodes can't exceed the capacity quota. However, this quota is a soft limit; you can always create a support ticket to get it increased easily.
 
 > [!Note]  
 > If you exceed the total core quota limit, You will receive an error message saying 'the maximum node exceeded the available cores in this region, please choose another region or contact the support to increase the quota.'
@@ -144,8 +144,6 @@ You can create an HDInsight cluster with load-based Autoscaling an Azure Resourc
 }
 ```
 
-For more information on creating clusters with Resource Manager templates, see [Create Apache Hadoop clusters in HDInsight by using Resource Manager templates](hdinsight-hadoop-create-linux-clusters-arm-templates.md).  
-
 #### Schedule-based autoscaling
 
 You can create an HDInsight cluster with schedule-based Autoscaling an Azure Resource Manager template, by adding an `autoscale` node to the `computeProfile` > `workernode` section. The `autoscale` node contains a `recurrence` that has a `timezone` and `schedule` that describes when the change will take place.
@@ -188,7 +186,7 @@ To enable Autoscale on a running cluster, select **Cluster size** under **Settin
 
 #### Using the REST API
 
-To enable or disable Autoscale on a running cluster using the REST API, make a POST request to the Autoscale endpoint as shown in the code snippet below:
+To enable or disable Autoscale on a running cluster using the REST API, make a POST request to the Autoscale endpoint:
 
 ```
 https://management.azure.com/subscriptions/{subscription Id}/resourceGroups/{resourceGroup Name}/providers/Microsoft.HDInsight/clusters/{CLUSTERNAME}/roles/workernode/autoscale?api-version=2018-06-01-preview
@@ -202,7 +200,7 @@ Use the appropriate parameters in the request payload. The json payload below co
 
 See the previous section on [enabling load-based autoscale](#load-based-autoscaling) for a full description of all payload parameters.
 
-## Best practices
+## Guidelines
 
 ### Choosing load-based or schedule-based scaling
 
@@ -219,9 +217,9 @@ It can take 10 to 20 minutes for a scaling operation to complete. When setting u
 
 ### Preparation for scaling down
 
-During cluster scaling down process, Autoscale will decommission the nodes to meet the target size. If there are running tasks on those nodes, Autoscale will wait until the tasks are completed. Since each worker node also serves a role in HDFS, the temp data will be shifted to the remaining nodes. So you should make sure there's enough space on the remaining nodes to host all the temp data.
+During cluster scaling down process, Autoscale will decommission the nodes to meet the target size. If tasks are running on those nodes, Autoscale will wait until the tasks are completed. Since each worker node also serves a role in HDFS, the temp data will be shifted to the remaining nodes. So you should make sure there's enough space on the remaining nodes to host all the temp data.
 
-The running jobs will continue to run and finish. The pending jobs will wait to be scheduled as normal with fewer available worker nodes.
+The running jobs will continue. The pending jobs will wait for scheduling with fewer available worker nodes.
 
 ### Minimum cluster size
 
@@ -242,10 +240,10 @@ All of the cluster status messages that you might see are explained in the list
 | Running | The cluster is operating normally. All of the previous Autoscale activities have completed successfully. |
 | Updating  | The cluster Autoscale configuration is being updated.  |
 | HDInsight configuration  | A cluster scale up or scale down operation is in progress.  |
-| Updating Error  | HDInsight encountered issues during the Autoscale configuration update. Customers can choose to either retry the update or disable autoscale.  |
+| Updating Error  | HDInsight met issues during the Autoscale configuration update. Customers can choose to either retry the update or disable autoscale.  |
 | Error  | Something is wrong with the cluster, and it isn't usable. Delete this cluster and create a new one.  |
 
-To view the current number of nodes in your cluster, go to the **Cluster size** chart on the **Overview** page for your cluster, or select **Cluster size** under **Settings**.
+To view the current number of nodes in your cluster, go to the **Cluster size** chart on the **Overview** page for your cluster. Or select **Cluster size** under **Settings**.
 
 ### Operation history
 
@@ -257,4 +255,4 @@ Select **Metrics** under **Monitoring**. Then select **Add metric** and **Number
 
 ## Next steps
 
-Read about best practices for scaling clusters manually in [Scaling best practices](hdinsight-scaling-best-practices.md)
+Read about guidelines for scaling clusters manually in [Scaling guidelines](hdinsight-scaling-best-practices.md)

articles/role-based-access-control/elevate-access-global-admin.md

@@ -13,20 +13,20 @@ ms.devlang: na
 ms.topic: conceptual
 ms.tgt_pltfrm: na
 ms.workload: identity
-ms.date: 12/03/2019
+ms.date: 04/17/2020
 ms.author: rolyon
 ms.reviewer: bagovind
 
 ---
-# Elevate access to manage all Azure subscriptions and management Groups
+# Elevate access to manage all Azure subscriptions and management groups
 
 As a Global Administrator in Azure Active Directory (Azure AD), you might not have access to all subscriptions and management groups in your directory. This article describes the ways that you can elevate your access to all subscriptions and management groups.
 
 [!INCLUDE [gdpr-dsr-and-stp-note](../../includes/gdpr-dsr-and-stp-note.md)]
 
 ## Why would you need to elevate your access?
 
-If you are a Global Administrator, there might be times when you want to do the following:
+If you are a Global Administrator, there might be times when you want to do the following actions:
 
 - Regain access to an Azure subscription or management group when a user has lost access
 - Grant another user or yourself access to an Azure subscription or management group
@@ -51,9 +51,7 @@ Follow these steps to elevate access for a Global Administrator using the Azure
 
 1. Sign in to the [Azure portal](https://portal.azure.com) or the [Azure Active Directory admin center](https://aad.portal.azure.com) as a Global Administrator.
 
-1. Search for and select **Azure Active Directory**.
-
-   ![Select Azure Active Directory - screenshot](./media/elevate-access-global-admin/search-for-azure-active-directory.png)
+1. Open **Azure Active Directory**.
 
 1. Under **Manage**, select **Properties**.
 
@@ -94,7 +92,7 @@ To remove the User Access Administrator role assignment at root scope (`/`), fol
 
 1. Set the **Access management for Azure resources** toggle back to **No**. Since this is a per-user setting, you must be signed in as the same user as was used to elevate access.
 
-    If you try to remove the User Access Administrator role assignment on the Access control (IAM) pane, you'll see the following message. To remove the role assignment, you must set set the toggle back to **No** or use Azure PowerShell, Azure CLI, or the REST API.
+    If you try to remove the User Access Administrator role assignment on the Access control (IAM) pane, you'll see the following message. To remove the role assignment, you must set the toggle back to **No** or use Azure PowerShell, Azure CLI, or the REST API.
 
     ![Remove role assignments with root scope](./media/elevate-access-global-admin/iam-root-remove.png)
 
@@ -229,7 +227,7 @@ You can list all of the deny assignments for a user at root scope (`/`).
 
 ### Remove elevated access
 
-When you call `elevateAccess`, you create a role assignment for yourself, so to revoke those privileges you need to remove the User Access Administrator role assignment for yourself at root scope (`/`)
+When you call `elevateAccess`, you create a role assignment for yourself, so to revoke those privileges you need to remove the User Access Administrator role assignment for yourself at root scope (`/`).
 
 1. Call [GET roleDefinitions](/rest/api/authorization/roledefinitions/get) where `roleName` equals User Access Administrator to determine the name ID of the User Access Administrator role.