Merge pull request #207341 from SturgeonMi/patch-9

Update notes for datasets generated from SQL

Author: PRMerger16

articles/machine-learning/v1/how-to-create-register-datasets.md

@@ -88,7 +88,10 @@ With TabularDatasets, you can specify a time stamp from a column in the data or
 Create a TabularDataset with [the Python SDK](#create-a-tabulardataset) or [Azure Machine Learning studio](../how-to-connect-data-ui.md#create-datasets).
 
 >[!NOTE]
-> [Automated ML](../concept-automated-ml.md) workflows generated via the Azure Machine Learning studio currently only support TabularDatasets. 
+> [Automated ML](../concept-automated-ml.md) workflows generated via the Azure Machine Learning studio currently only support TabularDatasets.
+> 
+>[!NOTE]
+> For TabularDatasets generating from [SQL query results](/python/api/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory#from-sql-query-query--validate-true--set-column-types-none--query-timeout-30-), T-SQL (e.g. 'WITH' sub query) or duplicate column name is not supported. Complex queries like T-SQL can cause performance issues. Duplicate column names in a dataset can cause ambiguity issues.
 
 ## Access datasets in a virtual network
 
@@ -388,4 +391,4 @@ titanic_ds = titanic_ds.register(workspace = workspace,
 
 * Learn [how to train with datasets](../how-to-train-with-datasets.md).
 * Use automated machine learning to [train with TabularDatasets](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb).
-* For more dataset training examples, see the [sample notebooks](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/work-with-data/).
+* For more dataset training examples, see the [sample notebooks](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/work-with-data/).