Freshness update for dsvm-tutorial-bicep.md . . .

Author: fbsolo-ms1

articles/machine-learning/data-science-virtual-machine/dsvm-tutorial-bicep.md

@@ -1,19 +1,20 @@
 ---
 title: 'Quickstart: Create an Azure Data Science VM - Bicep'
 titleSuffix: Azure Data Science Virtual Machine
-description: In this quickstart, you use Bicep to quickly deploy a Data Science Virtual Machine
+description: In this quickstart, you use Bicep to quickly deploy a Data Science Virtual Machine.
 services: machine-learning
 author: s-polly
-ms.author: scottpolly 
-ms.date: 05/02/2022
+ms.author: scottpolly
+ms.reviewer: franksolomon
+ms.date: 04/22/2024
 ms.topic: quickstart
 ms.service: data-science-vm
 ms.custom: subject-armqs, mode-arm, devx-track-bicep
 ---
 
 # Quickstart: Create an Ubuntu Data Science Virtual Machine using Bicep
 
-This quickstart will show you how to create an Ubuntu Data Science Virtual Machine using Bicep. Data Science Virtual Machines are cloud-based virtual machines preloaded with a suite of data science and machine learning frameworks and tools. When deployed on GPU-powered compute resources, all tools and libraries are configured to use the GPU.
+This quickstart shows how to create an Ubuntu Data Science Virtual Machine using Bicep. A Data Science Virtual Machine (DSVM) is a cloud-based virtual machine, preloaded with a suite of data science and machine learning frameworks and tools. When deployed on GPU-powered compute resources, all tools and libraries are configured to use the GPU.
 
 [!INCLUDE [About Bicep](../../../includes/resource-manager-quickstart-bicep-introduction.md)]
 
@@ -23,23 +24,23 @@ An Azure subscription. If you don't have an Azure subscription, create a [free a
 
 ## Review the Bicep file
 
-The Bicep file used in this quickstart is from [Azure Quickstart Templates](https://azure.microsoft.com/resources/templates/vm-ubuntu-DSVM-GPU-or-CPU/).
+This quickstart uses the Bicep file from the [Azure Quickstart Templates](https://azure.microsoft.com/resources/templates/vm-ubuntu-DSVM-GPU-or-CPU/).
 
 :::code language="bicep" source="~/quickstart-templates/application-workloads/datascience/vm-ubuntu-DSVM-GPU-or-CPU/main.bicep":::
 
-The following resources are defined in the Bicep file:
+The Bicep file defines these resources:
 
 * [Microsoft.Network/networkInterfaces](/azure/templates/microsoft.network/networkinterfaces)
 * [Microsoft.Network/networkSecurityGroups](/azure/templates/microsoft.network/networksecuritygroups)
 * [Microsoft.Network/virtualNetworks](/azure/templates/microsoft.network/virtualnetworks)
 * [Microsoft.Network/publicIPAddresses](/azure/templates/microsoft.network/publicipaddresses)
 * [Microsoft.Storage/storageAccounts](/azure/templates/microsoft.storage/storageaccounts)
-* [Microsoft.Compute/virtualMachines](/azure/templates/microsoft.compute/virtualmachines): Create a cloud-based virtual machine. In this template, the virtual machine is configured as a Data Science Virtual Machine running Ubuntu.
+* [Microsoft.Compute/virtualMachines](/azure/templates/microsoft.compute/virtualmachines): Create a cloud-based virtual machine. In this template, the virtual machine is configured as a Data Science Virtual Machine that runs Ubuntu.
 
 ## Deploy the Bicep file
 
-1. Save the Bicep file as **main.bicep** to your local computer.
-1. Deploy the Bicep file using either Azure CLI or Azure PowerShell.
+1. Save the Bicep file as **main.bicep** to your local computer
+1. Deploy the Bicep file with either Azure CLI or Azure PowerShell
 
     # [Azure CLI](#tab/CLI)
 
@@ -60,7 +61,7 @@ The following resources are defined in the Bicep file:
     > [!NOTE]
     > Replace **\<admin-user\>** with the username for the administrator account. Replace **\<vm-name\>** with the name of your virtual machine.
 
-    When the deployment finishes, you should see a message indicating the deployment succeeded.
+    When the deployment finishes, you should see a message indicating that the deployment succeeded.
 
 ## Review deployed resources
 
@@ -82,7 +83,7 @@ Get-AzResource -ResourceGroupName exampleRG
 
 ## Clean up resources
 
-When no longer needed, use the Azure portal, Azure CLI, or Azure PowerShell to delete the resource group and its resources.
+When you no longer need your resources, use the Azure portal, Azure CLI, or Azure PowerShell to delete both the resource group and its resources.
 
 # [Azure CLI](#tab/CLI)