Merge branch 'master' of https://github.com/MicrosoftDocs/azure-docs-pr into update-perms-howto-guides

Author: daveba

.openpublishing.publish.config.json

@@ -260,6 +260,12 @@
       "branch": "master",
       "branch_mapping": {}
     },
+    {
+      "path_to_root": "aml-sdk-samples",
+      "url": "https://github.com/Azure/MachineLearningNotebooks",
+      "branch": "sdk-codetest",
+      "branch_mapping": {}
+    },
     {
       "path_to_root": "cognitive-services-vision-csharp-sdk-quickstarts",
       "url": "https://github.com/Azure-Samples/cognitive-services-vision-csharp-sdk-quickstarts",

articles/avere-vfxt/avere-vfxt-deploy.md

@@ -86,6 +86,7 @@ Under **TERMS AND CONDITIONS**:
   > [!NOTE] 
   > If you are not a subscription owner, have an owner accept the terms for you by following the prerequisite steps in [Accept software terms in advance](avere-vfxt-prereqs.md#accept-software-terms-in-advance). 
 
+
 Click **Purchase** when finished. After five or six minutes, your controller node will be up and running.
 
 Visit the outputs page to gather the controller information that you need to create the cluster. Read [Information needed to create the cluster](#information-needed-to-create-the-cluster) to learn more.
@@ -220,7 +221,9 @@ To do the rest of the deployment steps, you need to connect to the cluster contr
 ## Create the cluster node access role
 
 > [!NOTE] 
-> If you are not a subscription owner, and the role has not already been created, have a subscription owner follow these steps or use the procedure in [Create the Avere vFXT cluster runtime access role without a controller](avere-vfxt-pre-role.md).
+> * If you are not a subscription owner, and the role has not already been created, have a subscription owner follow these steps or use the procedure in [Create the Avere vFXT cluster runtime access role without a controller](avere-vfxt-pre-role.md).
+> 
+> * Microsoft internal users should use the existing role named "Avere Cluster Runtime Operator" instead of attempting to create one. 
 
 [Role-based access control](https://docs.microsoft.com/azure/role-based-access-control/) (RBAC)  gives the vFXT cluster nodes authorization to perform necessary tasks.  
 

articles/cognitive-services/QnAMaker/Concepts/confidence-score.md

@@ -47,6 +47,8 @@ When choosing your threshold, keep in mind the balance between Accuracy and Cove
 
 - If **Coverage** (or recall) is more important- and you want to answer as many questions as possible, even if there is only a partial relation to the user's question- then LOWER the threshold. This means there could be more cases where the answer does not answer the user's actual query, but gives some other somewhat related answer. *For example:* if you make the threshold **30**, you might give not very related answers like, answering with the above example, for queries like "Where can I edit my KB?"
 
+> [!NOTE]
+> Newer versions of QnA Maker include improvements to scoring logic, and could affect your threshold. Any time you update the service, make sure to test and tweak the threshold if necessary. You can check your QnA Service version [here](https://www.qnamaker.ai/UserSettings), and see how to get the latest updates [here](../How-To/troubleshooting-runtime.md).
 
 ## Improve confidence scores
 To improve the confidence score of a particular response to a user query, you can add the user query to the knowledge base as an alternate question on that response.

articles/load-balancer/quickstart-create-basic-load-balancer-portal.md

@@ -4,11 +4,14 @@ description: This quickstart shows how to create a public Basic load balancer by
 services: load-balancer
 documentationcenter: na
 author: KumudD 
+manager: jeconnoc
+editor: ''
+tags: azure-resource-manager
 Customer intent: I want to create a Basic Load balancer so that I can load balance internet traffic to VMs.
 ms.assetid: aa9d26ca-3d8a-4a99-83b7-c410dd20b9d0
 ms.service: load-balancer
 ms.devlang: na
-ms.topic: quickstart
+ms.topic: hero-article
 ms.tgt_pltfrm: na
 ms.workload: infrastructure-services
 ms.date: 03/22/2018
@@ -151,7 +154,6 @@ To allow the Basic load balancer to monitor the status of your app, you use a he
    - **myHealthProbe** for the name of the health probe
    - **HTTP** for the protocol type
    - **80** for the port number
-   - **Healthprobe.aspx** for the URI path. You can either replace this value with any other URI or keep the default path value of **"\\"** to get the default URI.
    - **15** for **Interval**, the number of seconds between probe attempts
    - **2** for **Unhealthy threshold**, the number of consecutive probe failures that must occur before a VM is considered unhealthy
 

articles/load-balancer/quickstart-create-basic-load-balancer-powershell.md

@@ -4,13 +4,11 @@ description: This quickstart shows how to create a Basic Load Balancer using Pow
 services: load-balancer
 documentationcenter: na
 author: KumudD
-manager: jeconnoc
-tags: azure-resource-manager
 Customer intent: I want to create a Basic Load balancer so that I can load balance internet traffic to VMs.
 ms.assetid: 
 ms.service: load-balancer
 ms.devlang: na
-ms.topic: get-started-article
+ms.topic: quickstart
 ms.tgt_pltfrm: na
 ms.workload: infrastructure-services
 ms.date: 04/20/2018
@@ -194,10 +192,10 @@ $rule2 = New-AzureRmNetworkSecurityRuleConfig `
 Create a network security group with [New-AzureRmNetworkSecurityGroup](/powershell/module/azurerm.network/new-azurermnetworksecuritygroup).
 
 ```azurepowershell-interactive
-$nsg = New-AzureRmNetworkSecurityGroup`
+$nsg = New-AzureRmNetworkSecurityGroup `
 -ResourceGroupName 'myResourceGroupLB' `
 -Location 'EastUS' `
--Name 'myNetworkSecurityGroup'`
+-Name 'myNetworkSecurityGroup' `
 -SecurityRules $rule1,$rule2
 ```
 

articles/load-balancer/quickstart-create-standard-load-balancer-powershell.md

@@ -4,13 +4,11 @@ description: This quickstart shows how to create a Standard Load Balancer using
 services: load-balancer
 documentationcenter: na
 author: KumudD
-manager: jeconnoc
-tags: azure-resource-manager
 Customer intent: I want to create a Standard Load balancer so that I can load balance internet traffic to VMs.
 ms.assetid: 
 ms.service: load-balancer
 ms.devlang: na
-ms.topic: get-started-article
+ms.topic: qucikstart
 ms.tgt_pltfrm: na
 ms.workload: infrastructure-services
 ms.date: 08/22/2018
@@ -197,10 +195,10 @@ $rule2 = New-AzureRmNetworkSecurityRuleConfig `
 Create a network security group with [New-AzureRmNetworkSecurityGroup](/powershell/module/azurerm.network/new-azurermnetworksecuritygroup).
 
 ```azurepowershell-interactive
-$nsg = New-AzureRmNetworkSecurityGroup`
+$nsg = New-AzureRmNetworkSecurityGroup `
 -ResourceGroupName 'myResourceGroupLB' `
 -Location 'EastUS' `
--Name 'myNetworkSecurityGroup'`
+-Name 'myNetworkSecurityGroup' `
 -SecurityRules $rule1,$rule2
 ```
 

articles/load-balancer/quickstart-load-balancer-standard-public-portal.md

@@ -4,6 +4,8 @@ description: This quickstart shows how to create a Standard load balancer by usi
 services: load-balancer
 documentationcenter: na
 author: KumudD 
+manager: jeconnoc
+editor: ''
 tags: azure-resource-manager
 Customer intent: I want to create a Standard Load balancer so that I can load balance internet traffic to VMs.
 
@@ -139,7 +141,6 @@ To allow the load balancer to monitor the status of your app, you use a health p
     - *myHealthProbe* - for the name of the health probe.
     - **HTTP** - for the protocol type.
     - *80* - for the port number.
-    - **Healthprobe.aspx** for the URI path. You can either replace this value with any other URI or keep the default path value of **"\\"** to get the default URI.
     - *15* - for number of **Interval** in seconds between probe attempts.
     - *2* - for number of **Unhealthy threshold** or consecutive probe failures that must occur before a VM is considered unhealthy.
 4. Click **OK**.

articles/machine-learning/service/quickstart-create-workspace-with-python.md

@@ -34,6 +34,9 @@ The following Azure resources are added automatically to your workspace when the
 - [Azure Application Insights](https://azure.microsoft.com/services/application-insights/) 
 - [Azure Key Vault](https://azure.microsoft.com/services/key-vault/)
 
+>[!NOTE]
+> Code in this article was tested with Azure Machine Learning SDK version 0.1.74 
+
 If you don’t have an Azure subscription, create a [free account](https://azure.microsoft.com/free/?WT.mc_id=A261C142F) before you begin.
 
 
@@ -52,64 +55,39 @@ Before you install the SDK, we recommend that you create an isolated Python envi
 
 Open a command-line window. Then create a new conda environment named `myenv` with Python 3.6.
 
-```sh
+```shell
 conda create -n myenv -y Python=3.6
 ```
 
 Activate the environment.
 
-  ```sh
+  ```shell
   conda activate myenv
   ```
 
 ### Install the SDK
 
 In the activated conda environment, install the SDK. The below command installs the core components of the Machine Learning SDK. It also installs a Jupyter Notebook server in the `myenv` conda environment. The installation takes a few minutes to finish, depending on the configuration of your machine.
 
-```sh
+```shell
 # install the base SDK and Jupyter Notebook
 pip install azureml-sdk[notebooks]
-
-# install run histry widget
-jupyter nbextension install --py --user azureml.train.widgets
-
-# enable run histry widget
-jupyter nbextension enable --py --user azureml.train.widgets
 ```
 
-You can also use different "extra" keywords to install additional components of the SDK.
-
-```sh
-# install the base SDK and auto ml components
-pip install azureml-sdk[automl]
-
-# install the base SDK and model explainability component
-pip install azureml-sdk[explain]
-
-# install the base SDK and experimental components
-pip install azureml-sdk[contrib]
-
-# install the base SDK and automl components in Azure Databricks environment
-# read more at: https://github.com/Azure/MachineLearningNotebooks/tree/master/databricks
-pip install azureml-sdk[databricks]
-```
 
 
 ## Create a workspace
 
 To launch the Jupyter Notebook, enter this command.
-```sh
+```shell
 jupyter notebook
 ```
 
 In the browser window, create a new notebook by using the default `Python 3` kernel. 
 
 To display the SDK version, enter the following Python code in a notebook cell and execute it.
 
-```python
-import azureml.core
-print(azureml.core.VERSION)
-```
+[!code-python[](~/aml-sdk-samples/ignore/doc-qa/quickstart-create-workspace-with-python/quickstart.py?name=import)]
 
 Create a new Azure resource group and a new workspace.
 
@@ -118,35 +96,28 @@ Find a value for `<azure-subscription-id>` in the [subscriptions list in the Azu
 ```python
 from azureml.core import Workspace
 ws = Workspace.create(name='myworkspace',
-                      subscription_id='<azure-subscription-id>',
+                      subscription_id='<azure-subscription-id>',	
                       resource_group='myresourcegroup',
                       create_resource_group=True,
-                      location='eastus2' # or other supported Azure region
+                      location='eastus2' # or other supported Azure region	
                      )
 ```
 
 Executing the preceding code might trigger a new browser window for you to sign into your Azure account. After you sign in, the authentication token is cached locally.
 
 To see the workspace details, such as associated storage, container registry, and key vault, enter the following code.
 
-```python
-ws.get_details()
-```
+[!code-python[](~/aml-sdk-samples/ignore/doc-qa/quickstart-create-workspace-with-python/quickstart.py?name=getDetails)]
+
 
 ## Write a configuration file
 
 Save the details of your workspace in a configuration file into the current directory. This file is called 'aml_config\config.json'.  
 
 This workspace configuration file makes it easy to load this same workspace later. You can load it with other notebooks and scripts in the same directory or a subdirectory. 
 
-```python
-# Create the configuration file.
-ws.write_config()
+[!code-python[](~/aml-sdk-samples/ignore/doc-qa/quickstart-create-workspace-with-python/quickstart.py?name=writeConfig)]
 
-# Use this code to load the workspace from 
-# other scripts and notebooks in this directory.
-# ws = Workspace.from_config()
-```
 
 The `write_config()` API call creates the configuration file in the current directory. The `config.json` file contains the following script.
 
@@ -162,24 +133,8 @@ The `write_config()` API call creates the configuration file in the current dire
 
 Write some code that uses the basic APIs of the SDK to track experiment runs.
 
-```python
-from azureml.core import Experiment
-
-# create a new experiment
-exp = Experiment(workspace=ws, name='myexp')
+[!code-python[](~/aml-sdk-samples/ignore/doc-qa/quickstart-create-workspace-with-python/quickstart.py?name=useWs)]
 
-# start a run
-run = exp.start_logging()
-
-# log a number
-run.log('my magic number', 42)
-
-# log a list (Fibonacci numbers)
-run.log_list('my list', [1, 1, 2, 3, 5, 8, 13, 21, 34, 55]) 
-
-# finish the run
-run.complete()
-```
 
 ## View logged results
 When the run finishes, you can view the experiment run in the Azure portal. Use the following code to print a URL to the results for the last run.
@@ -198,9 +153,8 @@ Use the link to view the logged values in the Azure portal in your browser.
 
 If you don't plan to use the resources you created here, delete them so you don't incur any charges.
 
-```python
-ws.delete(delete_dependent_resources=True)
-```
+[!code-python[](~/aml-sdk-samples/ignore/doc-qa/quickstart-create-workspace-with-python/quickstart.py?name=delete)]
+
 
 ## Next steps
 
@@ -212,11 +166,35 @@ You need a few more packages in your environment to use it with Machine Learning
 1. In the command-line window, use `Ctrl`+`C` to stop the notebook server.
 1. Install additional packages.
 
-    ```sh
+    ```shell
     conda install -y cython matplotlib scikit-learn pandas numpy
     pip install azureml-sdk[automl]
+
+    # install run history widget
+    jupyter nbextension install --py --user azureml.train.widgets
+
+    # enable run history widget
+    jupyter nbextension enable --py --user azureml.train.widgets
     ```
 
+    You can also use different "extra" keywords to install additional components of the SDK.
+
+    ```shell
+    # install the base SDK and auto ml components
+    pip install azureml-sdk[automl]
+
+    # install the base SDK and model explainability component
+    pip install azureml-sdk[explain]
+
+    # install the base SDK and experimental components
+    pip install azureml-sdk[contrib]
+
+    # install the base SDK and automl components in Azure Databricks environment
+    # read more at: https://github.com/Azure/MachineLearningNotebooks/tree/master/databricks
+    pip install azureml-sdk[databricks]
+    ```
+
+
 After you install these packages, follow the tutorials to train and deploy a model. 
 
 > [!div class="nextstepaction"]

articles/service-bus-messaging/TOC.yml

@@ -188,6 +188,8 @@
       href: /dotnet/api/microsoft.azure.servicebus
   - name: Java
     href: /java/api/overview/azure/servicebus
+  - name: Node.js
+    href: /javascript/api/overview/azure/service-bus
   - name: Azure PowerShell
     href: /powershell/module/azurerm.servicebus
   - name: REST

articles/service-bus-messaging/index.yml

@@ -158,6 +158,6 @@ sections:
     - title: Command-Line
       html: <p><a href="/powershell/module/azurerm.servicebus/">Azure PowerShell</a></p><p><a href="/cli/azure/servicebus">Azure CLI</a></p>
     - title: Languages
-      html: <p><a href="/dotnet/api/microsoft.servicebus.messaging">.NET Framework</a></p><p><a href="/dotnet/api/microsoft.azure.servicebus">.NET Standard</a></p><p><a href="/java/api/overview/azure/servicebus">Java</a></p>
+      html: <p><a href="/dotnet/api/microsoft.servicebus.messaging">.NET Framework</a></p><p><a href="/dotnet/api/microsoft.azure.servicebus">.NET Standard</a></p><p><a href="/java/api/overview/azure/servicebus">Java</a></p><p><a href="/javascript/api/overview/azure/service-bus">Node.js</a></p>
     - title: REST
       html: <p><a href="/rest/api/servicebus/">REST API Reference</a></p>