Merge branch 'master' of https://github.com/MicrosoftDocs/azure-docs-pr into three-event-hubs-articles

Author: itechedit

articles/data-explorer/ingest-json-formats.md

@@ -6,12 +6,12 @@ ms.author: orspodek
 ms.reviewer: kerend
 ms.service: data-explorer
 ms.topic: conceptual
-ms.date: 01/23/2020
+ms.date: 01/27/2020
 ---
 
 # Ingest JSON formatted sample data into Azure Data Explorer
 
-This article shows you how to ingest JSON formatted data into an Azure Data Explorer database. You'll start with simple examples of raw and mapped JSON, continue to multi-lined JSON, and then tackle more complex JSON schemas containing arrays and dictionaries. 
+This article shows you how to ingest JSON formatted data into an Azure Data Explorer database. You'll start with simple examples of raw and mapped JSON, continue to multi-lined JSON, and then tackle more complex JSON schemas containing arrays and dictionaries.  The examples detail the process of ingesting JSON formatted data using Kusto query language (KQL), C#, or Python. The Kusto query language `ingest` control commands are executed directly to the engine endpoint. In production scenarios, ingestion is executed to the Data Management service using client libraries or data connections. Read [Ingest data using the Azure Data Explorer Python library](/azure/data-explorer/python-ingest-data) and [Ingest data using the Azure Data Explorer .NET Standard SDK](/azure/data-explorer/net-standard-ingest-data) for a walk-through regarding ingesting data with these client libraries.
 
 ## Prerequisites
 
@@ -77,9 +77,6 @@ Use Kusto query language to ingest data in a raw JSON format.
     .ingest into table RawEvents h'https://kustosamplefiles.blob.core.windows.net/jsonsamplefiles/simple.json?st=2018-08-31T22%3A02%3A25Z&se=2020-09-01T22%3A02%3A00Z&sp=r&sv=2018-03-28&sr=b&sig=LQIbomcKI8Ooz425hWtjeq6d61uEaq21UVX7YrM61N4%3D' with (format=json, jsonMappingReference=RawEventMapping)
     ```
 
-    > [!NOTE]
-    > This shows the `ingest` control commands executed directly to the engine endpoint. In production scenarios, ingestion is executed to the Data Management service using client libraries or data connections. Read [Ingest data using the Azure Data Explorer Python library](/azure/data-explorer/python-ingest-data) and [Ingest data using the Azure Data Explorer .NET Standard SDK](/azure/data-explorer/net-standard-ingest-data) for a walk-through regarding ingesting data with these client libraries.
-
 # [C#](#tab/c-sharp)
 
 Use C# to ingest data in raw JSON format.

articles/digital-twins/how-to-configure-postman.md

@@ -82,9 +82,7 @@ Set up and configure Postman to obtain an Azure Active Directory token. Afterwar
 
     | Name  | Replace with | Example |
     |---------|---------|---------|
-    | YOUR_AZURE_TENANT | The name of your tenant or organization | `microsoft` |
-
-    The YOUR_AZURE_TENANT placeholder is for your **Tenant ID** in the Azure Active Directory. There is a numeric directory Id and a human-readable tenant Id. You must use the human-readable Id. The **Tenant ID** is located under the **Overview** tab of your Azure Active Directory.
+    | YOUR_AZURE_TENANT | The name of your tenant or organization. Use the human-friendly name instead of the alphanumeric **Tenant ID** of your Azure Active Directory app registration. | `microsoft` |
 
 1. Go to [www.getpostman.com](https://www.getpostman.com/) to download the app.
 

includes/machine-learning-service-inference-config.md

@@ -14,7 +14,7 @@ The entries in the `inferenceconfig.json` document map to the parameters for the
 | `runtime` | `runtime` | Optional. Which runtime to use for the image. Current supported runtimes are `spark-py` and `python`. If `environment` is set, this gets ignored. |
 | `condaFile` | `conda_file` | Optional. Path to a local file that contains a Conda environment definition to use for the image.  If `environment` is set, this gets ignored. |
 | `extraDockerFileSteps` | `extra_docker_file_steps` | Optional. Path to a local file that contains additional Docker steps to run when setting up the image.  If `environment` is set, this gets ignored.|
-| `sourceDirectory` | `source_directory` | Optional. Path to folders that contain all files to create the image. |
+| `sourceDirectory` | `source_directory` | Optional. Path to folders that contain all files to create the image which makes it easy to access any files within this folder or subfolder. You can upload an entire folder from your local machine as dependencies for the Webservice. Note: your entry_script, conda_file, and extra_docker_file_steps paths are relative paths to the source_directory path. |
 | `enableGpu` | `enable_gpu` | Optional. Whether to enable GPU support in the image. The GPU image must be used on an Azure service, like Azure Container Instances, Azure Machine Learning Compute, Azure Virtual Machines, and Azure Kubernetes Service. The default is False. If `environment` is set, this gets ignored.|
 | `baseImage` | `base_image` | Optional. Custom image to be used as a base image. If no base image is provided, the image will be based on the provided runtime parameter. If `environment` is set, this gets ignored. |
 | `baseImageRegistry` | `base_image_registry` | Optional. Image registry that contains the base image. If `environment` is set, this gets ignored.|