Merge branch 'main' of https://github.com/MicrosoftDocs/azure-docs-pr into crossUpdates

Author: roygara

articles/aks/concepts-network.md

@@ -145,7 +145,7 @@ The following behavior differences exist between kubenet and Azure CNI:
 
 Regarding DNS, with both kubenet and Azure CNI plugins DNS are offered by CoreDNS, a deployment running in AKS with its own autoscaler. For more information on CoreDNS on Kubernetes, see [Customizing DNS Service](https://kubernetes.io/docs/tasks/administer-cluster/dns-custom-nameservers/). CoreDNS by default is configured to forward unknown domains to the DNS functionality of the Azure Virtual Network where the AKS cluster is deployed. Hence, Azure DNS and Private Zones will work for pods running in AKS.
 
-For more information on Azure CNI and kubenet and to help determine which option is best for you, see [Configure Azure CNI networking in AKS][azure-cni-aks] and [Use kubenet networking in AKS][kubenet-aks].
+For more information on Azure CNI and kubenet and to help determine which option is best for you, see [Configure Azure CNI networking in AKS][azure-cni-aks] and [Use kubenet networking in AKS][aks-configure-kubenet-networking].
 
 ### Support scope between network models
 
@@ -269,4 +269,3 @@ For more information on core Kubernetes and AKS concepts, see the following arti
 [ip-preservation]: https://techcommunity.microsoft.com/t5/fasttrack-for-azure/how-client-source-ip-preservation-works-for-loadbalancer/ba-p/3033722#:~:text=Enable%20Client%20source%20IP%20preservation%201%20Edit%20loadbalancer,is%20the%20same%20as%20the%20source%20IP%20%28srjumpbox%29.
 [nsg-traffic]: ../virtual-network/network-security-group-how-it-works.md
 [azure-cni-aks]: configure-azure-cni.md
-[kubenet-aks]: configure-kubenet.md

articles/aks/concepts-storage.md

@@ -103,7 +103,7 @@ Like using a secret:
 
 Volumes defined and created as part of the pod lifecycle only exist until you delete the pod. Pods often expect their storage to remain if a pod is rescheduled on a different host during a maintenance event, especially in StatefulSets. A *persistent volume* (PV) is a storage resource created and managed by the Kubernetes API that can exist beyond the lifetime of an individual pod.
 
-You can use Azure Disks or Files to provide the PersistentVolume. As noted in the [Volumes](#volumes) section, the choice of Disks or Files is often determined by the need for concurrent access to the data or the performance tier.
+You can use [Azure Disks](/azure-csi-disk-storage-provision.md) or [Azure Files](/azure-csi-files-storage-provision.md) to provide the PersistentVolume. As noted in the [Volumes](#volumes) section, the choice of Disks or Files is often determined by the need for concurrent access to the data or the performance tier.
 
 ![Persistent volumes in an Azure Kubernetes Services (AKS) cluster](media/concepts-storage/persistent-volumes.png)
 
@@ -114,9 +114,9 @@ A PersistentVolume can be *statically* created by a cluster administrator, or *d
 
 ## Storage classes
 
-To define different tiers of storage, such as Premium and Standard, you can create a *StorageClass*. 
+To define different tiers of storage, such as Premium and Standard, you can create a *StorageClass*.
 
-The StorageClass also defines the *reclaimPolicy*. When you delete the pod and the persistent volume is no longer required, the reclaimPolicy controls the behavior of the underlying Azure storage resource. The underlying storage resource can either be deleted or kept for use with a future pod.
+The StorageClass also defines the *reclaimPolicy*. When you delete the persistent volume, the reclaimPolicy controls the behavior of the underlying Azure storage resource. The underlying storage resource can either be deleted or kept for use with a future pod.
 
 For clusters using the [Container Storage Interface (CSI) drivers][csi-storage-drivers] the following extra `StorageClasses` are created:
 
@@ -154,6 +154,8 @@ allowVolumeExpansion: true
 > [!NOTE]
 > AKS reconciles the default storage classes and will overwrite any changes you make to those storage classes.
 
+For more information about storage classes, see [StorageClass in Kubernetes](https://kubernetes.io/docs/concepts/storage/storage-classes/).
+
 ## Persistent volume claims
 
 A PersistentVolumeClaim requests storage of a particular StorageClass, access mode, and size. The Kubernetes API server can dynamically provision the underlying Azure storage resource if no existing resource can fulfill the claim based on the defined StorageClass.

articles/azure-monitor/app/status-monitor-v2-overview.md

@@ -52,7 +52,7 @@ Each of these options is described in the [detailed instructions](status-monitor
 
 ### Does Application Insights Agent support ASP.NET Core applications?
 
-  Yes. Starting from [Application Insights Agent 2.0.0-beta1](https://www.powershellgallery.com/packages/Az.ApplicationMonitor/2.0.0-beta1), ASP.NET Core applications hosted in IIS are supported.
+  Yes. Starting from [Application Insights Agent 2.0.0](https://www.powershellgallery.com/packages/Az.ApplicationMonitor/2.0.0), ASP.NET Core applications hosted in IIS are supported.
 
 ### How do I verify that the enablement succeeded?
 
@@ -74,6 +74,10 @@ See the dedicated [troubleshooting article](/troubleshoot/azure/azure-monitor/ap
 
 The release note updates are listed here.
 
+### 2.0.0
+
+- Updated the Application Insights .NET/.NET Core SDK to 2.21.0-redfield
+
 ### 2.0.0-beta3
 
 - Updated the Application Insights .NET/.NET Core SDK to 2.20.1-redfield

articles/azure-monitor/containers/container-insights-enable-arc-enabled-clusters.md

@@ -28,7 +28,7 @@ ms.reviewer: aul
 
 - Pre-requisites listed under the [generic cluster extensions documentation](../../azure-arc/kubernetes/extensions.md#prerequisites).
 - Log Analytics workspace. Azure Monitor Container Insights supports a Log Analytics workspace in the regions listed under Azure [products by region page](https://azure.microsoft.com/global-infrastructure/services/?regions=all&products=monitor). You can create your own workspace using [Azure Resource Manager](../logs/resource-manager-workspace.md), [PowerShell](../logs/powershell-workspace-configuration.md), or [Azure portal](../logs/quick-create-workspace.md).
-- [Contributor](../../role-based-access-control/built-in-roles.md#contributor) role assignment on the Azure subscription containing the Azure Arc-enabled Kubernetes resource. If the Log Analytics workspace is in a different subscription, then [Log Analytics Contributor](../logs/manage-access.md#azure-rbac) role assignment is needed on the Log Analytics workspace.
+- [Contributor](../../role-based-access-control/built-in-roles.md#contributor) role assignment on the Azure subscription containing the Azure Arc-enabled Kubernetes resource. If the Log Analytics workspace is in a different subscription, then [Log Analytics Contributor](../logs/manage-access.md#azure-rbac) role assignment is needed on the resource group containing the Log Analytics Workspace
 - To view the monitoring data, you need to have [Log Analytics Reader](../logs/manage-access.md#azure-rbac) role assignment on the Log Analytics workspace.
 - The following endpoints need to be enabled for outbound access in addition to the ones mentioned under [connecting a Kubernetes cluster to Azure Arc](../../azure-arc/kubernetes/quickstart-connect-cluster.md#meet-network-requirements).
 

articles/azure-netapp-files/cross-zone-replication-introduction.md

@@ -45,6 +45,7 @@ The preview of cross-zone replication is available in the following regions:
 * South Central US 
 * Sweden Central
 * Switzerland North
+* UAE North
 * UK South
 * US Gov Virginia
 * West Europe

articles/azure-vmware/attach-azure-netapp-files-to-azure-vmware-solution-hosts.md

@@ -3,7 +3,7 @@ title: Attach Azure NetApp Files datastores to Azure VMware Solution hosts
 description: Learn how to create Azure NetApp Files-based NFS datastores for Azure VMware Solution hosts.
 ms.topic: how-to
 ms.service: azure-vmware
-ms.date: 02/17/2023
+ms.date: 02/21/2023
 ms.custom: "references_regions, engagement-fy23"
 ---
 
@@ -13,7 +13,7 @@ ms.custom: "references_regions, engagement-fy23"
 
 [Azure VMware Solution](./introduction.md) supports attaching Network File System (NFS) datastores as a persistent storage option. You can create NFS datastores with Azure NetApp Files volumes and attach them to clusters of your choice. You can also create virtual machines (VMs) for optimal cost and performance.  
 
-By using NFS datastores backed by Azure NetApp Files, you can expand your storage instead of scaling the clusters. You can also use Azure NetApp Files volumes to replicate data from on-premises or primary VMware environments for the secondary site. 
+By using NFS datastores backed by Azure NetApp Files, you can expand your storage instead of scaling the clusters. You can also use Azure NetApp Files volumes to replicate data from on-premises or primary VMware vSphere environments for the secondary site. 
 
 Create your Azure VMware Solution and create Azure NetApp Files NFS volumes in the virtual network connected to it using an ExpressRoute. Ensure there's connectivity from the private cloud to the NFS volumes created. Use those volumes to create NFS datastores and attach the datastores to clusters of your choice in a private cloud. As a native integration, you need no other permissions configured via vSphere.
 
@@ -41,19 +41,7 @@ Before you begin the prerequisites, review the [Performance best practices](#per
 >[!NOTE]
 >Azure NetApp Files datastores for Azure VMware Solution are generally available. To use it, you must register Azure NetApp Files datastores for Azure VMware Solution.
 
-## Supported regions
-
-Azure VMware Solution currently supports the following regions:
-
-**Asia**      : East Asia, Japan East, Japan West, Southeast Asia.
-
-**Australia** : Australia East, Australia Southeast.
-
-**Brazil**    : Brazil South.
-
-**Europe**    : France Central, Germany West Central, North Europe, Sweden Central, Sweden North, Switzerland West, UK South, UK West, West Europe
-
-**North America**   : Canada Central, Canada East, Central US, East US, East US 2, North Central US, South Central US, West US, West US 2.
+Azure VMware Solution is currently supported in these [regions](https://azure.microsoft.com/global-infrastructure/services/?products=azure-vmware).
 
 ## Performance best practices
 
@@ -144,12 +132,14 @@ To attach an Azure NetApp Files volume to your private cloud using Azure CLI, fo
 
     `az vmware datastore list --resource-group MyResourceGroup --cluster Cluster-1 --private-cloud MyPrivateCloud`
 
+---
+
 ## Service level change for Azure NetApp Files datastore
 
 Based on the performance requirements of the datastore, you can change the service level of the Azure NetApp Files volume used for the datastore by following the instructions to [dynamically change the service level of a volume for Azure NetApp Files](../azure-netapp-files/dynamic-change-volume-service-level.md)
-This has no impact to the Datastore or private cloud as there is no downtime involved and the IP address/mount path remain unchanged. However, the volume Resource Id will be changed due to the capacity pool change. Therefore to avoid any metadata mismatch re-issue the datastore create command via Azure CLI as follows: `az vmware datastore netapp-volume create`.
+This has no impact to the Datastore or private cloud as there is no downtime involved and the IP address/mount path remain unchanged. However, the volume Resource ID will be changed due to the capacity pool change. Therefore to avoid any metadata mismatch re-issue the datastore create command via Azure CLI as follows: `az vmware datastore netapp-volume create`.
 >[!IMPORTANT]  
-> The input values for **cluster** name, datastore **name**, **private-cloud** (SDDC) name, and **resource-group** must be **exactly the same as the current one**, and the **volume-id** is the new Resource Id of the volume.
+> The input values for **cluster** name, datastore **name**, **private-cloud** (SDDC) name, and **resource-group** must be **exactly the same as the current one**, and the **volume-id** is the new Resource ID of the volume.
 
    -**cluster**  
    -**name**  

articles/azure-vmware/media/attach-netapp-files-to-cloud/architecture-netapp-files-nfs-datastores.png

@@ -34,35 +34,35 @@ AutoML creates a number of pipelines in parallel that try different algorithms a
 
 1. For **classification**, you can also enable deep learning.
 
-If deep learning is enabled, validation is limited to _train_validation split_. [Learn more about validation options](/how-to-configure-cross-validation-data-splits.md).
+If deep learning is enabled, validation is limited to _train_validation split_. [Learn more about validation options](../how-to-configure-cross-validation-data-splits.md).
 
 
 1. (Optional) View addition configuration settings: additional settings you can use to better control the training job. Otherwise, defaults are applied based on experiment selection and data. 
 
     Additional configurations|Description
     ------|------
-    Primary metric| Main metric used for scoring your model. [Learn more about model metrics](/how-to-configure-auto-train.md#primary-metric).
-    Explain best model | Select to enable or disable, in order to show explanations for the recommended best model. <br> This functionality is not currently available for [certain forecasting algorithms](/how-to-machine-learning-interpretability-automl.md#interpretability-during-training-for-the-best-model). 
-    Blocked algorithm| Select algorithms you want to exclude from the training job. <br><br> Allowing algorithms is only available for [SDK experiments](/how-to-configure-auto-train.md#supported-algorithms). <br> See the [supported algorithms for each task type](/python/api/azureml-automl-core/azureml.automl.core.shared.constants.supportedmodels).
+    Primary metric| Main metric used for scoring your model. [Learn more about model metrics](../how-to-configure-auto-train.md#primary-metric).
+    Explain best model | Select to enable or disable, in order to show explanations for the recommended best model. <br> This functionality is not currently available for [certain forecasting algorithms](../v1/how-to-machine-learning-interpretability-automl.md#interpretability-during-training-for-the-best-model). 
+    Blocked algorithm| Select algorithms you want to exclude from the training job. <br><br> Allowing algorithms is only available for [SDK experiments](../how-to-configure-auto-train.md#supported-algorithms). <br> See the [supported algorithms for each task type](/python/api/azureml-automl-core/azureml.automl.core.shared.constants.supportedmodels).
     Exit criterion| When any of these criteria are met, the training job is stopped. <br> *Training job time (hours)*: How long to allow the training job to run. <br> *Metric score threshold*:  Minimum metric score for all pipelines. This ensures that if you have a defined target metric you want to reach, you do not spend more time on the training job than necessary.
     Concurrency| *Max concurrent iterations*: Maximum number of pipelines (iterations) to test in the training job. The job will not run more than the specified number of iterations. Learn more about how automated ML performs [multiple child jobs on clusters](/how-to-configure-auto-train.md#multiple-child-runs-on-clusters).
 
 
 1. The **[Optional] Validate and test** form allows you to do the following. 
 
-    1. Specify the type of validation to be used for your training job. [Learn more about cross validation](/how-to-configure-cross-validation-data-splits.md#prerequisites). 
+    1. Specify the type of validation to be used for your training job. [Learn more about cross validation](../how-to-configure-cross-validation-data-splits.md#prerequisites). 
 
     1. Provide a test dataset (preview) to evaluate the recommended model that automated ML generates for you at the end of your experiment. When you provide test data, a test job is automatically triggered at the end of your experiment. This test job is only job on the best model that was recommended by automated ML. 
 
         >[!IMPORTANT]
         > Providing a test dataset to evaluate generated models is a preview feature. This capability is an [experimental](/python/api/overview/azure/ml/#stable-vs-experimental) preview feature, and may change at any time.
         
-        * Test data is considered a separate from training and validation, so as to not bias the results of the test job of the recommended model. [Learn more about bias during model validation](/concept-automated-ml.md#training-validation-and-test-data).
+        * Test data is considered a separate from training and validation, so as to not bias the results of the test job of the recommended model. [Learn more about bias during model validation](../concept-automated-ml.md#training-validation-and-test-data).
         * You can either provide your own test dataset or opt to use a percentage of your training dataset. Test data must be in the form of an [Azure Machine Learning TabularDataset](../v1/how-to-create-register-datasets.md#tabulardataset).         
         * The schema of the test dataset should match the training dataset. The target column is optional, but if no target column is indicated no test metrics are calculated.
         * The test dataset should not be the same as the training dataset or the validation dataset.
 
 
 ## Next steps
 
-See the [set of components available](/component-reference.md) to Azure Machine Learning. 
+See the [set of components available](../component-reference/component-reference.md) to Azure Machine Learning.