Merge branch 'main' of https://github.com/MicrosoftDocs/azure-docs-pr into premV2

Author: roygara

articles/api-management/api-management-api-import-restrictions.md

@@ -226,6 +226,10 @@ You can create [SOAP pass-through](import-soap-api.md) and [SOAP-to-REST](restif
 
 * For an open-source tool to resolve and merge `wsdl:import`, `xsd:import`, and `xsd:include` dependencies in a WSDL file, see this [GitHub repo](https://github.com/Azure-Samples/api-management-schema-import).
 
+### WS-* specifications
+
+WSDL files incorporating WS-* specifications are not supported.
+
 ### Messages with multiple parts 
 This message type is not supported.
 

articles/azure-vmware/deploy-disaster-recovery-using-jetstream.md

@@ -3,7 +3,7 @@ title: Deploy disaster recovery using JetStream DR
 description: Learn how to implement JetStream DR for your Azure VMware Solution private cloud and on-premises VMware workloads. 
 ms.topic: how-to
 ms.service: azure-vmware
-ms.date: 04/11/2022
+ms.date: 07/15/2022
 ms.custom: references_regions
 ---
 
@@ -147,8 +147,6 @@ For full details, refer to the article: [Disaster Recovery with Azure NetApp Fil
 
 For more on-premises JetStream DR prerequisites, see the [JetStream Pre-Installation Guide](https://www.jetstreamsoft.com/portal/jetstream-knowledge-base/pre-installation-guidelines/).
 
-
-
 ## Install JetStream DR on Azure VMware Solution  
 
 You can follow these steps for both supported scenarios. 

articles/azure-vmware/media/run-command/run-command-overview-jetstream.png

@@ -9,6 +9,8 @@
   items:
   - name: Deploy Bastion with default settings
     href: quickstart-host-portal.md
+  - name: Deploy Bastion - ARM template
+    href: quickstart-host-arm-template.md
 - name: Tutorials
   items:
   - name: Deploy Bastion with manual settings

articles/bastion/TOC.yml

@@ -3,13 +3,11 @@ title: 'Tutorial: Link a VNet to an ExpressRoute circuit - Azure portal'
 description: This tutorial shows you how to create a connection to link a virtual network to an Azure ExpressRoute circuit using the Azure portal. 
 services: expressroute
 author: duongau
-
 ms.service: expressroute
 ms.topic: tutorial
-ms.date: 08/10/2021
+ms.date: 07/15/2022
 ms.author: duau
-ms.custom: seodec18
-
+ms.custom: seodec18, template-tutorial
 ---
 # Tutorial: Connect a virtual network to an ExpressRoute circuit using the portal
 
@@ -49,8 +47,6 @@ In this tutorial, you learn how to:
 
 * Review guidance for [connectivity between virtual networks over ExpressRoute](virtual-network-connectivity-guidance.md).
 
-* You can [view a video](https://azure.microsoft.com/documentation/videos/azure-expressroute-how-to-create-a-connection-between-your-vpn-gateway-and-expressroute-circuit) before beginning to better understand the steps.
-
 ## Connect a VNet to a circuit - same subscription
 
 > [!NOTE]
@@ -202,7 +198,9 @@ You can delete a connection and unlink your VNet to an ExpressRoute circuit by s
 
 ## Next steps
 
-In this tutorial, you learned how to connect a virtual network to a circuit in the same subscription and a different subscription. For more information about the ExpressRoute gateway, see: 
+In this tutorial, you learned how to connect a virtual network to a circuit in the same subscription and a different subscription. For more information about ExpressRoute gateways, see: [ExpressRoute virtual network gateways](expressroute-about-virtual-network-gateways.md).
+
+To learn how to configure route filters for Microsoft peering using the Azure portal, advance to the next tutorial.
 
 > [!div class="nextstepaction"]
-> [About ExpressRoute virtual network gateways](expressroute-about-virtual-network-gateways.md)
+> [Configure route filters for Microsoft peering](how-to-routefilter-portal.md)

articles/expressroute/expressroute-howto-linkvnet-portal-resource-manager.md

@@ -12,9 +12,9 @@ ms.custom: mode-api
 
 # Get started with MedTech service in Azure Health Data Services
 
-This article outlines the basic steps to get started with MedTech service in [Azure Health Data Services](../healthcare-apis-overview.md). MedTech service first processes data that has been sent to an event hub from a medical device, and then saves the data to the Fast Healthcare Interoperability Resources (FHIR®) service as Observation resources. This procedure makes it possible to link the FHIR service Observation to patient and device resources.
+This article outlines the basic steps to get started with Azure MedTech service in [Azure Health Data Services](../healthcare-apis-overview.md). MedTech service ingests health data from a medical device using Azure Event Hubs service. It then persists the data to the Azure Fast Healthcare Interoperability Resources (FHIR®) service as Observation resources. This data processing procedure makes it possible to link FHIR service Observations to patient and device resources.
 
-The following diagram shows the four development steps of the data flow needed to get MedTech service to receive data from a device and send it to FHIR service.
+The following diagram shows the four-step data flow that enables MedTech service to receive data from a device and send it to FHIR service.
 
 - Step 1 introduces the subscription and permissions prerequisites needed.
 

articles/healthcare-apis/iot/get-started-with-iot.md

@@ -5,7 +5,7 @@ author: kgremban
 ms.service: iot-hub
 services: iot-hub
 ms.topic: conceptual
-ms.date: 07/17/2018
+ms.date: 07/15/2022
 ms.author: kgremban
 ms.custom: [amqp, mqtt,'Role: Cloud Development', 'Role: IoT Device']
 ---
@@ -40,26 +40,27 @@ The payload for method requests and responses is a JSON document up to 128 KB.
 
 ## Invoke a direct method from a back-end app
 
-Now, invoke a direct method from a back-end app.
+To invoke a direct method from a back-end app use the [Invoke device method](/rest/api/iothub/service/devices/invoke-method) REST API or its equivalent in one of the [IoT Hub service SDKs](iot-hub-devguide-sdks.md#azure-iot-hub-service-sdks).
 
 ### Method invocation
 
 Direct method invocations on a device are HTTPS calls that are made up of the following items:
 
-* The *request URI* specific to the device along with the [API version](/rest/api/iothub/service/devices/invokemethod):
+* The *request URI* specific to the device along with the API version:
 
     ```http
     https://fully-qualified-iothubname.azure-devices.net/twins/{deviceId}/methods?api-version=2021-04-12
     ```
 
 * The POST *method*
 
-* *Headers* that contain the authorization, request ID, content type, and content encoding.
+* *Headers* that contain the authorization, content type, and content encoding.
 
 * A transparent JSON *body* in the following format:
 
     ```json
     {
+        "connectTimeoutInSeconds": 200,
         "methodName": "reboot",
         "responseTimeoutInSeconds": 200,
         "payload": {
@@ -75,7 +76,7 @@ The value provided as `connectTimeoutInSeconds` in the request is the amount of
 
 #### Example
 
-This example will allow you to securely initiate a request to invoke a Direct Method on an IoT device registered to an Azure IoT Hub.
+This example will allow you to securely initiate a request to invoke a direct method on an IoT device registered to an Azure IoT hub.
 
 To begin, use the [Microsoft Azure IoT extension for Azure CLI](https://github.com/Azure/azure-iot-cli-extension) to create a SharedAccessSignature.
 
@@ -100,14 +101,15 @@ curl -X POST \
 }'
 ```
 
-Execute the modified command to invoke the specified Direct Method. Successful requests will return an HTTP 200 status code.
+Execute the modified command to invoke the specified direct method. Successful requests will return an HTTP 200 status code.
 
 > [!NOTE]
-> The above example demonstrates invoking a Direct Method on a device.  If you wish to invoke a Direct Method in an IoT Edge Module, you would need to modify the url request as shown below:
+> The example above demonstrates invoking a direct method on a device.  If you want to invoke a direct method in an IoT Edge Module, you would need to modify the url request as shown below:
+>
+> ```bash
+> https://<iothubName>.azure-devices.net/twins/<deviceId>/modules/<moduleName>/methods?api-version=2021-04-12
+> ```
 
-```bash
-https://<iothubName>.azure-devices.net/twins/<deviceId>/modules/<moduleName>/methods?api-version=2021-04-12
-```
 ### Response
 
 The back-end app receives a response that is made up of the following items:
@@ -117,7 +119,7 @@ The back-end app receives a response that is made up of the following items:
   * 404 indicates that either device ID is invalid, or that the device was not online upon invocation of a direct method and for `connectTimeoutInSeconds` thereafter (use accompanied error message to understand the root cause);
   * 504 indicates gateway timeout caused by device not responding to a direct method call within `responseTimeoutInSeconds`.
 
-* *Headers* that contain the ETag, request ID, content type, and content encoding.
+* *Headers* that contain the request ID, content type, and content encoding.
 
 * A JSON *body* in the following format:
 
@@ -128,25 +130,25 @@ The back-end app receives a response that is made up of the following items:
     }
     ```
 
-    Both `status` and `body` are provided by the device and used to respond with the device's own status code and/or description.
+    Both `status` and `payload` are provided by the device and used to respond with the device's own status code and the method response.
 
 ### Method invocation for IoT Edge modules
 
-Invoking direct methods using a module ID is supported in the [IoT Service Client C# SDK](https://www.nuget.org/packages/Microsoft.Azure.Devices/).
+Invoking direct methods on a module is supported by the [Invoke module method](/rest/api/iothub/service/modules/invoke-method) REST API or its equivalent in one of the IoT Hub service SDKs.
 
-For this purpose, use the `ServiceClient.InvokeDeviceMethodAsync()` method and pass in the `deviceId` and `moduleId` as parameters.
+The `moduleId` is passed along with the `deviceId` in the request URI when using the REST API or as a parameter when using a service SDK. For example, `https://<iothubName>.azure-devices.net/twins/<deviceId>/modules/<moduleName>/methods?api-version=2021-04-12`. The request body and response is similar to that of direct methods invoked on the device.
 
 ## Handle a direct method on a device
 
-Let's look at how to handle a direct method on an IoT device.
+On an IoT device, direct methods can be received over MQTT, AMQP, or either of these protocols over WebSockets. The [IoT Hub device SDKs](iot-hub-devguide-sdks.md#azure-iot-hub-device-sdks) help you receive and respond to direct methods on devices without having to worry about the underlying protocol details.
 
 ### MQTT
 
-The following section is for the MQTT protocol.
+The following section is for the MQTT protocol. To learn more about using the MQTT protocol directly with IoT Hub, see [MQTT protocol support](iot-hub-mqtt-support.md).
 
 #### Method invocation
 
-Devices receive direct method requests on the MQTT topic: `$iothub/methods/POST/{method name}/?$rid={request id}`. The number of subscriptions per device is limited to 5. It is therefore recommended not to subscribe to each direct method individually. Instead consider subscribing to `$iothub/methods/POST/#` and then filter the delivered messages based on your desired method names.
+Devices receive direct method requests on the MQTT topic: `$iothub/methods/POST/{method name}/?$rid={request id}`. However, the `request id` is generated by IoT Hub and cannot be known ahead of time, so subscribe to `$iothub/methods/POST/#` and then filter the delivered messages based on method names supported by your device. (You'll use the `request id` to respond.)
 
 The body that the device receives is in the following format:
 
@@ -171,7 +173,7 @@ The body is set by the device and can be any status.
 
 ### AMQP
 
-The following section is for the AMQP protocol.
+The following section is for the AMQP protocol. To learn more about using the AMQP protocol directly with IoT Hub, see [AMQP protocol support](iot-hub-amqp-support.md).
 
 #### Method invocation
 

articles/iot-hub/iot-hub-devguide-direct-methods.md

@@ -50,7 +50,7 @@ Currently, **Train PyTorch Model** component supports both single node and distr
     > In distributed training, to keep gradient descent stable, the actual learning rate is calculated by `lr * torch.distributed.get_world_size()` because batch size of the process group is world size times that of single process.
     > Polynomial learning rate decay is applied and can help result in a better performing model.
 
-8.  For **Random seed**, optionally type an integer value to use as the seed. Using a seed is recommended if you want to ensure reproducibility of the experiment across runs.
+8.  For **Random seed**, optionally type an integer value to use as the seed. Using a seed is recommended if you want to ensure reproducibility of the experiment across jobs.
 
 9.  For **Patience**, specify how many epochs to early stop training if validation loss does not decrease consecutively. by default 3.
 
@@ -77,12 +77,12 @@ Click on this component 'Metrics' tab and see training metric graphs, such as 'T
 
 ### How to enable distributed training
 
-To enable distributed training for **Train PyTorch Model** component, you can set in **Run settings** in the right pane of the component. Only **[AML Compute cluster](../how-to-create-attach-compute-cluster.md?tabs=python)** is supported for distributed training.
+To enable distributed training for **Train PyTorch Model** component, you can set in **Job settings** in the right pane of the component. Only **[AML Compute cluster](../how-to-create-attach-compute-cluster.md?tabs=python)** is supported for distributed training.
 
 > [!NOTE]
 > **Multiple GPUs** are required to activate distributed training because NCCL backend Train PyTorch Model component uses needs cuda.
 
-1. Select the component and open the right panel. Expand the **Run settings** section.
+1. Select the component and open the right panel. Expand the **Job settings** section.
 
     [![Screenshot showing how to set distributed training in runsetting](./media/module/distributed-training-run-setting.png)](./media/module/distributed-training-run-setting.png#lightbox)
 
@@ -116,7 +116,7 @@ You can refer to [this article](designer-error-codes.md) for more details about
 
 ## Results
 
-After pipeline run is completed, to use the model for scoring, connect the [Train PyTorch Model](train-PyTorch-model.md) to [Score Image Model](score-image-model.md), to predict values for new input examples.
+After pipeline job is completed, to use the model for scoring, connect the [Train PyTorch Model](train-PyTorch-model.md) to [Score Image Model](score-image-model.md), to predict values for new input examples.
 
 ## Technical notes
 ###  Expected inputs  

articles/machine-learning/component-reference/train-pytorch-model.md

@@ -68,7 +68,7 @@ From this sample, you can see that a single user has rated several movies.
 
 ## Results
 
-After pipeline run is completed, to use the model for scoring, connect the [Train SVD Recommender](train-svd-recommender.md) to [Score SVD Recommender](score-svd-recommender.md), to predict values for new input examples.
+After pipeline job is completed, to use the model for scoring, connect the [Train SVD Recommender](train-svd-recommender.md) to [Score SVD Recommender](score-svd-recommender.md), to predict values for new input examples.
 
 ## Next steps
 

articles/machine-learning/component-reference/train-svd-recommender.md

@@ -65,9 +65,9 @@ The data can be read from two kinds of datasets, file dataset or tabular dataset
     - **VW** represents the internal format used by  Vowpal Wabbit . See the [Vowpal Wabbit wiki page](https://github.com/JohnLangford/vowpal_wabbit/wiki/Input-format) for details. 
     - **SVMLight** is a format used by some other machine learning tools. 
 
-6. **Output readable model file**: select the option if you want the component to save the readable model to the run records. This argument corresponds to the `--readable_model` parameter in the VW command line.  
+6. **Output readable model file**: select the option if you want the component to save the readable model to the job records. This argument corresponds to the `--readable_model` parameter in the VW command line.  
 
-7. **Output inverted hash file**: select the option if you want the component to save the inverted hashing function to one file in the run records. This argument corresponds to the `--invert_hash` parameter in the VW command line.  
+7. **Output inverted hash file**: select the option if you want the component to save the inverted hashing function to one file in the job records. This argument corresponds to the `--invert_hash` parameter in the VW command line.  
 
 8. Submit the pipeline.
 
@@ -83,7 +83,7 @@ Vowpal Wabbit supports incremental training by adding new data to an existing mo
 2. Connect the previously trained model to the **Pre-trained Vowpal Wabbit Model** input port of the component.
 3. Connect the new training data to the **Training data** input port of the component.
 4. In the parameters pane of **Train Vowpal Wabbit Model**, specify the format of the new training data, and also the training data file name if the input dataset is a directory.
-5. Select the **Output readable model file** and **Output inverted hash file** options if the corresponding files need to be saved in the run records.
+5. Select the **Output readable model file** and **Output inverted hash file** options if the corresponding files need to be saved in the job records.
 
 6. Submit the pipeline.  
 7. Select the component and select **Register dataset** under **Outputs+logs** tab in the right pane, to preserve the updated model in your Azure Machine Learning workspace.  If you don't specify a new name, the updated model overwrites the existing saved model.