Merge branch 'master' of https://github.com/MicrosoftDocs/azure-docs-pr into asc-melvyn-test

Author: memildin

.openpublishing.redirection.json

@@ -26194,11 +26194,6 @@
       "redirect_url": "/azure/security-center/security-center-features-retirement-july2019#custom-alert-rules-preview",
       "redirect_document_id": false
     },  
-    {
-      "source_path": "articles/security-center/security-center-threat-intel.md",
-      "redirect_url": "/azure/security-center/security-center-features-retirement-july2019#menu_securityeventsmap",
-      "redirect_document_id": false
-    },  
     {
       "source_path": "articles/virtual-network/virtual-network-deploy-multinic-arm-cli.md",
       "redirect_url": "/azure/virtual-machines/linux/multiple-nics",

articles/active-directory/conditional-access/howto-conditional-access-policy-block-legacy.md

@@ -21,7 +21,7 @@ Due to the increased risk associated with legacy authentication protocols, Micro
 
 ## Create a Conditional Access policy
 
-The following steps will help create a Conditional Access policy to require those assigned administrative roles to perform multi-factor authentication.
+The following steps will help create a Conditional Access policy to block legacy authentication requests.
 
 1. Sign in to the **Azure portal** as a global administrator, security administrator, or Conditional Access administrator.
 1. Browse to **Azure Active Directory** > **Conditional Access**.

articles/aks/security-hardened-vm-host-image.md

@@ -73,8 +73,6 @@ AKS-Engine does not promote or adhere to any specific security standard at this
 |5.4.4 |Ensure default user umask is 027 or more restrictive |
 |5.6 |Ensure access to the su command is restricted|
 
-(*) Must be enabled. See the cluster definition [documentation](https://github.com/Azure/aks-engine/blob/master/docs/topics/clusterdefinitions.md).
-
 ## Additional notes
 
 * To further reduce the attack surface area, some unnecessary kernel module drivers have been disabled in the OS. 

articles/machine-learning/service/how-to-create-register-datasets.md

@@ -43,7 +43,7 @@ To create and work with datasets, you need:
 
 Datasets are categorized into two types based on how users consume them in training. 
 
-* [TabularDataset](https://docs.microsoft.com/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) represents data in a tabular format by parsing the provided file or list of files. This provides you with the ability to materialize the data into a pandas DataFrame. A `TabularDataset` object can be created from csv, tsv, parquet files, SQL query results etc. For a complete list, please visit our [documentation](https://aka.ms/tabulardataset-api-reference).
+* [TabularDataset](https://docs.microsoft.com/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) represents data in a tabular format by parsing the provided file or list of files. This provides you with the ability to materialize the data into a pandas or spark DataFrame. A `TabularDataset` object can be created from csv, tsv, parquet files, SQL query results etc. For a complete list, please visit our [documentation](https://aka.ms/tabulardataset-api-reference).
 
 * [FileDataset](https://docs.microsoft.com/python/api/azureml-core/azureml.data.file_dataset.filedataset?view=azure-ml-py) references single or multiple files in your datastores or public urls. This provides you with the ability to download or mount the files to your compute. The files can be of any format, which enables a wider range of machine learning scenarios including deep learning.
 
@@ -106,6 +106,16 @@ titanic_ds.take(3).to_pandas_dataframe()
 1|2|1|1|Cumings, Mrs. John Bradley (Florence Briggs Th...|female|38.0|1|0|PC 17599|71.2833|C85|C
 2|3|1|3|Heikkinen, Miss. Laina|female|26.0|0|0|STON/O2. 3101282|7.9250||S
 
+Use the [`from_sql_query()`](https://docs.microsoft.com/python/api/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory?view=azure-ml-py#from-sql-query-query--validate-true--set-column-types-none-) method on `TabularDatasetFactory` class to read from Azure SQL Database.
+
+```Python
+
+from azureml.core import Dataset, Datastore
+
+# create tabular dataset from a SQL database in datastore
+sql_datastore = Datastore.get(workspace, 'mssql')
+sql_ds = Dataset.Tabular.from_sql_query((sql_datastore, 'SELECT * FROM my_table'))
+```
 Use the [`with_timestamp_columns()`](https://docs.microsoft.com/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py#with-timestamp-columns-fine-grain-timestamp--coarse-grain-timestamp-none--validate-false-) method on `TabularDataset` class to enable easy and efficient filtering by time. More examples and details can be found [here](http://aka.ms/azureml-tsd-notebook). 
 
 ```Python
@@ -193,7 +203,7 @@ titanic_ds = titanic_ds.register(workspace = workspace,
 ```
 
 
-## Access your data during training
+## Access datasets in your script
 
 Registered datasets are accessible locally and remotely on compute clusters like the Azure Machine Learning compute. To access your registered Dataset across experiments, use the following code to get your workspace and registered dataset by name. The [`get_by_name()`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#get-by-name-workspace--name--version--latest--) method on the `Dataset` class by default returns the latest version of the dataset registered with the workspace.
 
@@ -216,5 +226,6 @@ df = titanic_ds.to_pandas_dataframe()
 
 ## Next steps
 
+* Learn [how to train with datasets](how-to-train-with-datasets.md)
 * Use automated machine learning to [train with TabularDatasets](https://aka.ms/automl-dataset).
 * For more examples of training with datasets, see the [sample notebooks](https://aka.ms/dataset-tutorial).

articles/virtual-machines/linux/openshift-prerequisites.md

@@ -113,7 +113,7 @@ Create service principal:
 scope=`az group show --name openshiftrg --query id`
 az ad sp create-for-rbac --name openshiftsp \
       --role Contributor --password {Strong Password} \
-      --scopes $scope
+      --scopes $scope \
 ```
 If you're using Windows, execute ```az group show --name openshiftrg --query id``` and use the output in place of $scope.
 
@@ -170,4 +170,4 @@ This article covered the following topics:
 Next, deploy an OpenShift cluster:
 
 - [Deploy OpenShift Container Platform](./openshift-container-platform.md)
-- [Deploy OpenShift Container Platform Self-Managed Marketplace Offer](./openshift-marketplace-self-managed.md)
+- [Deploy OpenShift Container Platform Self-Managed Marketplace Offer](./openshift-marketplace-self-managed.md)

includes/azure-monitor-limits-alerts.md

@@ -13,8 +13,8 @@ ms.custom: "include file"
 | Resource | Default limit | Maximum limit |
 | --- | --- | --- |
 | Metric alerts (classic) |100 active alert rules per subscription. | Call support. |
-| Metric alerts |1000 active alert rules per subscription (in public clouds) and 100 active alert rules per subscription in Azure China 21Vianet and Azure Government. | Call support. |
+| Metric alerts |1000 active alert rules per subscription in Azure public, Azure China 21Vianet and Azure Government clouds. | Call support. |
 | Activity log alerts | 100 active alert rules per subscription. | Same as default. |
 | Log alerts | 512 | Call support. |
 | Action groups |2,000 action groups per subscription. | Call support. |
-| Autoscale settings |100 per region per subscription. | Same as default. |
+| Autoscale settings |100 per region per subscription. | Same as default. |