Update how-to-access-data.md

wip

Author: MayMSFT

articles/machine-learning/how-to-access-data.md

@@ -67,7 +67,11 @@ We recommend creating a datastore for an [Azure Blob container](https://docs.mic
 
 [Azure Data Lake Storage Gen2](https://docs.microsoft.com/azure/storage/blobs/data-lake-storage-introduction?toc=/azure/storage/blobs/toc.json) is built on top of Azure Blob storage and designed for enterprise big data analytics. A fundamental part of Data Lake Storage Gen2 is the addition of a [hierarchical namespace](https://docs.microsoft.com/azure/storage/blobs/data-lake-storage-namespace) to Blob storage. The hierarchical namespace organizes objects/files into a hierarchy of directories for efficient data access.
 
-When you create a workspace, an Azure blob container and an Azure file share are automatically registered to the workspace. They're named `workspaceblobstore` and `workspacefilestore`, respectively. `workspaceblobstore` is used to store workspace artifacts and your machine learning experiment logs. `workspacefirestore` is used to store notebooks and R scripts authorized via [compute instance](https://docs.microsoft.com/azure/machine-learning/concept-compute-instance#accessing-files). The `workspaceblobstore` container is set as the default datastore.
+When you create a workspace, an Azure blob container and an Azure file share are automatically registered to the workspace. They're named `workspaceblobstore` and `workspacefilestore`, respectively. `workspaceblobstore` is used to store workspace artifacts and your machine learning experiment logs. `workspacefilestore` is used to store notebooks and R scripts authorized via [compute instance](https://docs.microsoft.com/azure/machine-learning/concept-compute-instance#accessing-files). The `workspaceblobstore` container is set as the default datastore.
+
+> [!IMPORTANT]
+> Azure Machine Learning designer (preview) will create a datastore named **azureml_globaldatasets** automatically when you open a sample in the designer homepage. This datastore only contains sample datasets. Please **do not** use this datastore for any confidential data access!
+> ![Auto-created datastore for designer sample datasets](media/how-to-access-data/datastore-designer-sample.png)
 
 <a name="access"></a>
 
@@ -136,7 +140,7 @@ file_datastore = Datastore.register_azure_file_share(workspace=ws,
 
 #### Azure Data Lake Storage Generation 2
 
-For an Azure Data Lake Storage Generation 2 (ADLS Gen 2) datastore, use [register_azure_data_lake_gen2()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.datastore.datastore?view=azure-ml-py#register-azure-data-lake-gen2-workspace--datastore-name--filesystem--account-name--tenant-id--client-id--client-secret--resource-url-none--authority-url-none--protocol-none--endpoint-none--overwrite-false-) to register a credential datastore connected to an Azure DataLake Gen 2 storage with [service principal permissions](https://docs.microsoft.com/azure/active-directory/develop/howto-create-service-principal-portal). In order to utilize your service principal you need to [register your application](https://docs.microsoft.com/azure/active-directory/develop/app-objects-and-service-principals) and set role assignments to Reader and Data access. Learn more about [access control set up for ADLS Gen 2](https://docs.microsoft.com/azure/storage/blobs/data-lake-storage-access-control). 
+For an Azure Data Lake Storage Generation 2 (ADLS Gen 2) datastore, use [register_azure_data_lake_gen2()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.datastore.datastore?view=azure-ml-py#register-azure-data-lake-gen2-workspace--datastore-name--filesystem--account-name--tenant-id--client-id--client-secret--resource-url-none--authority-url-none--protocol-none--endpoint-none--overwrite-false-) to register a credential datastore connected to an Azure DataLake Gen 2 storage with [service principal permissions](https://docs.microsoft.com/azure/active-directory/develop/howto-create-service-principal-portal). In order to utilize your service principal you need to [register your application](https://docs.microsoft.com/azure/active-directory/develop/app-objects-and-service-principals) and grant the service principal with the right data access. Learn more about [access control set up for ADLS Gen 2](https://docs.microsoft.com/azure/storage/blobs/data-lake-storage-access-control). 
 
 The following code creates and registers the `adlsgen2_datastore_name` datastore to the `ws` workspace. This datastore accesses the file system `test` on the `account_name` storage account, by using the provided service principal credentials.
 
@@ -187,10 +191,6 @@ The following example demonstrates what the form looks like when you create an A
 
 ## Get datastores from your workspace
 
-> [!IMPORTANT]
-> Azure Machine Learning designer (preview) will create a datastore named **azureml_globaldatasets** automatically when you open a sample in the designer homepage. This datastore only contains sample datasets. Please **do not** use this datastore for any confidential data access!
-> ![Auto-created datastore for designer sample datasets](media/how-to-access-data/datastore-designer-sample.png)
-
 To get a specific datastore registered in the current workspace, use the [`get()`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.datastore(class)?view=azure-ml-py#get-workspace--datastore-name-) static method on the `Datastore` class:
 
 ```Python