Merge pull request #89625 from MicrosoftDocs/repo_sync_working_branch

huypub · web-flow · commit a1abdcce0086 · 2019-09-25T09:35:04.000-07:00
Confirm merge from repo_sync_working_branch to master to sync with https://github.com/Microsoft/azure-docs (branch master)
diff --git a/articles/active-directory/saas-apps/idc-tutorial.md b/articles/active-directory/saas-apps/idc-tutorial.md
@@ -91,7 +91,7 @@ Follow these steps to enable Azure AD SSO in the Azure portal.
 1. Click **Set additional URLs** and perform the following steps if you wish to configure the application in **SP** initiated mode:
 
     In the **Sign-on URL** text box, type a URL:
-    `https://cas.idc.com/saml-welcome/AzureAppDirectory`
+    `https://www.idc.com/saml-welcome/<SamlWelcomeCode>`
 
     > [!NOTE]
     > These values are not real. Update these values with the actual Identifier and Reply URL. Contact [IDC Client support team](mailto:webchanneldev@idc.com) to get these values. You can also refer to the patterns shown in the **Basic SAML Configuration** section in the Azure portal.
@@ -140,7 +140,7 @@ To configure single sign-on on **IDC** side, you need to send the downloaded **F
 
 ### Create IDC test user
 
-In this section, you create a user called Britta Simon in IDC. Work with [IDC support team](mailto:webchanneldev@idc.com) to add the users in the IDC platform. Users must be created and activated before you use single sign-on.
+A user does not have to be created in IDC in advance. The user will created automatically once he uses single sign-on for the first time.
 
 ## Test SSO 
 
diff --git a/articles/api-management/api-management-access-restriction-policies.md b/articles/api-management/api-management-access-restriction-policies.md
@@ -118,7 +118,7 @@ The `rate-limit` policy prevents API usage spikes on a per subscription basis by
 
 | Name      | Description                                                                                                                                                                                                                                                                                              | Required |
 | --------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------- |
-| set-limit | Root element.                                                                                                                                                                                                                                                                                            | Yes      |
+| rate-limit | Root element.                                                                                                                                                                                                                                                                                            | Yes      |
 | api       | Add one or more of these elements to impose a call rate limit on APIs within the product. Product and API call rate limits are applied independently. API can be referenced either via `name` or `id`. If both attributes are provided, `id` will be used and `name` will be ignored.                    | No       |
 | operation | Add one or more of these elements to impose a call rate limit on operations within an API. Product, API, and operation call rate limits are applied independently. Operation can be referenced either via `name` or `id`. If both attributes are provided, `id` will be used and `name` will be ignored. | No       |
 
@@ -181,9 +181,9 @@ In the following example, the rate limit is keyed by the caller IP address.
 
 ### Elements
 
-| Name      | Description   | Required |
-| --------- | ------------- | -------- |
-| set-limit | Root element. | Yes      |
+| Name              | Description   | Required |
+| ----------------- | ------------- | -------- |
+| rate-limit-by-key | Root element. | Yes      |
 
 ### Attributes
 
diff --git a/articles/batch/tutorial-rendering-cli.md b/articles/batch/tutorial-rendering-cli.md
@@ -164,20 +164,20 @@ az storage container create \
     --name job-myrenderjob
 ```
 
-To write output files to the container, Batch needs to use a Shared Access Signature (SAS) token. Create the token with the [az storage account generate-sas](/cli/azure/storage/account#az-storage-account-generate-sas) command. This example creates a token to write to any blob container in the account, and the token expires on November 15, 2018:
+To write output files to the container, Batch needs to use a Shared Access Signature (SAS) token. Create the token with the [az storage account generate-sas](/cli/azure/storage/account#az-storage-account-generate-sas) command. This example creates a token to write to any blob container in the account, and the token expires on November 15, 2020:
 
 ```azurecli-interactive
 az storage account generate-sas \
     --permissions w \
     --resource-types co \
     --services b \
-    --expiry 2019-11-15
+    --expiry 2020-11-15
 ```
 
 Take note of the token returned by the command, which looks similar to the following. You use this token in a later step.
 
 ```
-se=2018-11-15&sp=rw&sv=2017-04-17&ss=b&srt=co&sig=xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
+se=2020-11-15&sp=rw&sv=2019-09-24&ss=b&srt=co&sig=xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
 ```
 
 ## Render a single-frame scene
@@ -213,7 +213,7 @@ Modify the `blobSource` and `containerURL` elements in the JSON file so that the
   "commandLine": "cmd /c \"%3DSMAX_2018%3dsmaxcmdio.exe -secure off -v:5 -rfw:0 -start:1 -end:1 -outputName:\"dragon.jpg\" -w 400 -h 300 MotionBlur-DragonFlying.max\"",
   "resourceFiles": [
     {
-        "blobSource": "https://mystorageaccount.blob.core.windows.net/scenefiles/MotionBlur-DragonFlying.max",
+        "httpUrl": "https://mystorageaccount.blob.core.windows.net/scenefiles/MotionBlur-DragonFlying.max",
         "filePath": "MotionBlur-DragonFlying.max"
     }
   ],
diff --git a/articles/cognitive-services/QnAMaker/Tutorials/integrate-qnamaker-luis.md b/articles/cognitive-services/QnAMaker/Tutorials/integrate-qnamaker-luis.md
@@ -46,7 +46,7 @@ In the above scenario, QnA Maker first gets the intent of the incoming question
 
 ## Web app Bot
 
-1. [Create a "Basic" Web App bot](https://docs.microsoft.com/azure/bot-service/bot-service-quickstart?view=azure-bot-service-4.0) which automatically includes a LUIS app. Select the 4.x SDK and the C# programming language.
+1. [Create a "Basic" Web App bot](https://docs.microsoft.com/azure/bot-service/bot-service-quickstart?view=azure-bot-service-4.0) which automatically includes a LUIS app. Select C# programming language.
 
 1. Once the web app bot is created, in the Azure portal, select the web app bot.
 1. Select **Application Settings** in the Web app bot service navigation, then scroll down to **Application settings** section of available settings.
diff --git a/articles/data-factory/copy-activity-schema-and-type-mapping.md b/articles/data-factory/copy-activity-schema-and-type-mapping.md
@@ -18,7 +18,7 @@ ms.author: jingwang
 ---
 # Schema mapping in copy activity
 
-This article describes how Azure Data Factory copy activity does schema mapping and data type mapping from source data to sink data when execute the data copy.
+This article describes how the Azure Data Factory copy activity does schema mapping and data type mapping from source data to sink data when executing the data copy.
 
 ## Schema mapping
 
@@ -89,7 +89,7 @@ The following properties are supported under `translator` -> `mappings` -> objec
 | -------- | ------------------------------------------------------------ | -------- |
 | name     | Name of the source or sink column.                           | Yes      |
 | ordinal  | Column index. Start with 1. <br>Apply and required when using delimited text without header line. | No       |
-| path     | JSON path expression for each field to extract or map. Apply for hierarchical data e.g. MongoDB/REST.<br>For fields under root object, JSON path starts with root $; for fields inside the array chosen by `collectionReference` property, JSON path starts from the array element. | No       |
+| path     | JSON path expression for each field to extract or map. Apply for hierarchical data e.g. MongoDB/REST.<br>For fields under the root object, the JSON path starts with root $; for fields inside the array chosen by `collectionReference` property, JSON path starts from the array element. | No       |
 | type     | Data Factory interim data type of the source or sink column. | No       |
 | culture  | Culture of the source or sink column. <br>Apply when type is `Datetime` or `Datetimeoffset`. The default is `en-us`. | No       |
 | format   | Format string to be used when type is `Datetime` or `Datetimeoffset`. Refer to [Custom Date and Time Format Strings](https://docs.microsoft.com/dotnet/standard/base-types/custom-date-and-time-format-strings) on how to format datetime. | No       |
@@ -102,14 +102,14 @@ The following properties are supported under `translator` -> `mappings` in addit
 
 ### Alternative column mapping
 
-You can specify copy activity -> `translator` -> `columnMappings` to map between tabular-shaped data . In this case, "structure" section is required for both input and output datasets. Column mapping supports **mapping all or subset of columns in the source dataset "structure" to all columns in the sink dataset "structure"**. The following are error conditions that result in an exception:
+You can specify copy activity -> `translator` -> `columnMappings` to map between tabular-shaped data . In this case, the "structure" section is required for both input and output datasets. Column mapping supports **mapping all or subset of columns in the source dataset "structure" to all columns in the sink dataset "structure"**. The following are error conditions that result in an exception:
 
 * Source data store query result does not have a column name that is specified in the input dataset "structure" section.
 * Sink data store (if with pre-defined schema) does not have a column name that is specified in the output dataset "structure" section.
 * Either fewer columns or more columns in the "structure" of sink dataset than specified in the mapping.
 * Duplicate mapping.
 
-In the following example, the input dataset has a structure and it points to a table in an on-premises Oracle database.
+In the following example, the input dataset has a structure, and it points to a table in an on-premises Oracle database.
 
 ```json
 {
diff --git a/articles/data-factory/quickstart-create-data-factory-python.md b/articles/data-factory/quickstart-create-data-factory-python.md
@@ -153,7 +153,7 @@ You create linked services in a data factory to link your data stores and comput
     ls_name = 'storageLinkedService'
 
     # IMPORTANT: specify the name and key of your Azure Storage account.
-    storage_string = SecureString('DefaultEndpointsProtocol=https;AccountName=<storageaccountname>;AccountKey=<storageaccountkey>')
+    storage_string = SecureString(value='DefaultEndpointsProtocol=https;AccountName=<storageaccountname>;AccountKey=<storageaccountkey>')
 
     ls_azure_storage = AzureStorageLinkedService(connection_string=storage_string)
     ls = adf_client.linked_services.create_or_update(rg_name, df_name, ls_name, ls_azure_storage)
@@ -222,10 +222,7 @@ Add the following code to the **Main** method that **triggers a pipeline run**.
 
 ```python
     #Create a pipeline run.
-    run_response = adf_client.pipelines.create_run(rg_name, df_name, p_name,
-        {
-        }
-    )
+    run_response = adf_client.pipelines.create_run(rg_name, df_name, p_name, parameters={})
 ```
 
 ## Monitor a pipeline run
@@ -237,8 +234,12 @@ To monitor the pipeline run, add the following code the **Main** method:
     time.sleep(30)
     pipeline_run = adf_client.pipeline_runs.get(rg_name, df_name, run_response.run_id)
     print("\n\tPipeline run status: {}".format(pipeline_run.status))
-    activity_runs_paged = list(adf_client.activity_runs.list_by_pipeline_run(rg_name, df_name, pipeline_run.run_id, datetime.now() - timedelta(1),  datetime.now() + timedelta(1)))
-    print_activity_run_details(activity_runs_paged[0])
+    filter_params = RunFilterParameters(
+        last_updated_after=datetime.now() - timedelta(1), last_updated_before=datetime.now() + timedelta(1))
+    query_response = adf_client.activity_runs.query_by_pipeline_run(
+        rg_name, df_name, pipeline_run.run_id, filter_params)
+    print_activity_run_details(query_response.value[0])
+
 ```
 
 Now, add the following statement to invoke the **main** method when the program is run:
@@ -334,7 +335,7 @@ def main():
 
     # Specify the name and key of your Azure Storage account
     storage_string = SecureString(
-        'DefaultEndpointsProtocol=https;AccountName=<storage account name>;AccountKey=<storage account key>')
+        value='DefaultEndpointsProtocol=https;AccountName=<storage account name>;AccountKey=<storage account key>')
 
     ls_azure_storage = AzureStorageLinkedService(
         connection_string=storage_string)
@@ -348,15 +349,15 @@ def main():
     blob_path = 'adfv2tutorial/input'
     blob_filename = 'input.txt'
     ds_azure_blob = AzureBlobDataset(
-        ds_ls, folder_path=blob_path, file_name=blob_filename)
+        linked_service_name=ds_ls, folder_path=blob_path, file_name=blob_filename)
     ds = adf_client.datasets.create_or_update(
         rg_name, df_name, ds_name, ds_azure_blob)
     print_item(ds)
 
     # Create an Azure blob dataset (output)
     dsOut_name = 'ds_out'
     output_blobpath = 'adfv2tutorial/output'
-    dsOut_azure_blob = AzureBlobDataset(ds_ls, folder_path=output_blobpath)
+    dsOut_azure_blob = AzureBlobDataset(linked_service_name=ds_ls, folder_path=output_blobpath)
     dsOut = adf_client.datasets.create_or_update(
         rg_name, df_name, dsOut_name, dsOut_azure_blob)
     print_item(dsOut)
@@ -379,19 +380,18 @@ def main():
     print_item(p)
 
     # Create a pipeline run
-    run_response = adf_client.pipelines.create_run(rg_name, df_name, p_name,
-                                                   {
-                                                   }
-                                                   )
+    run_response = adf_client.pipelines.create_run(rg_name, df_name, p_name, parameters={})
 
     # Monitor the pipeline run
     time.sleep(30)
     pipeline_run = adf_client.pipeline_runs.get(
         rg_name, df_name, run_response.run_id)
     print("\n\tPipeline run status: {}".format(pipeline_run.status))
-    activity_runs_paged = list(adf_client.activity_runs.list_by_pipeline_run(
-        rg_name, df_name, pipeline_run.run_id, datetime.now() - timedelta(1), datetime.now() + timedelta(1)))
-    print_activity_run_details(activity_runs_paged[0])
+    filter_params = RunFilterParameters(
+        last_updated_after=datetime.now() - timedelta(1), last_updated_before=datetime.now() + timedelta(1))
+    query_response = adf_client.activity_runs.query_by_pipeline_run(
+        rg_name, df_name, pipeline_run.run_id, filter_params)
+    print_activity_run_details(query_response.value[0])
 
 
 # Start the main method
diff --git a/articles/sql-database/sql-database-managed-instance-determine-size-vnet-subnet.md b/articles/sql-database/sql-database-managed-instance-determine-size-vnet-subnet.md
@@ -23,7 +23,7 @@ When you create a Managed Instance, Azure allocates a number of virtual machines
 By design, a Managed Instance needs a minimum of 16 IP addresses in a subnet and may use up to 256 IP addresses. As a result, you can use a subnet masks between /28 and /24 when defining your subnet IP ranges. A network mask bit of /28 (14 hosts per network) is a good size for a single general purpose or business-critical deployment. A mask bit of /27 (30 hosts per network) is ideal for a multiple Managed Instance deployments within the same VNet. Mask bit settings of /26 (62 hosts) and /24 (254 hosts) allows further scaling out of the VNet to support additional Managed Instances.
 
 > [!IMPORTANT]
-> A subnet size with 16 IP addresses is the bare minimum with limited potential for the further Managed Instance scale out. Choosing subnet with the prefix /27 or below is highly recommended.
+> A subnet size with 16 IP addresses is the bare minimum with limited potential where a scaling operation like vCore size change is not supported. Choosing subnet with the prefix /27 or longest prefix is highly recommended.
 
 ## Determine subnet size
 
