term emphasis

Author: nibaccam

articles/machine-learning/how-to-connect-data-ui.md

@@ -81,6 +81,7 @@ Create a new datastore in a few steps with the Azure Machine Learning studio. Le
 The following example demonstrates what the form looks like when you create an **Azure blob datastore**:
 
 ![Form for a new datastore](media/how-to-connect-data-ui/new-id-based-datastore-form.png)
+
 ---
 
 ## Create datasets

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

@@ -18,11 +18,11 @@ ms.custom: contperf-fy21q1, devx-track-python, data4ml
 
 In this article, you learn how to connect to storage services on Azure by using identity-based data access and Azure Machine Learning datastores via the [Azure Machine Learning SDK for Python](/python/api/overview/azure/ml/intro).  
 
-Typically, datastores use credential-based data access to confirm you have permission to access the storage service. They keep connection information, like your subscription ID and token authorization, in the [key vault](https://azure.microsoft.com/services/key-vault/) that's associated with the workspace. When you create a datastore that uses **identity-based data access**, your Azure account ([Azure Active Directory token](../active-directory/fundamentals/active-directory-whatis.md)) is used to confirm you have permission to access the storage service. In the **identity-based data access** scenario, no authentication credentials are saved. Only the storage account information is stored in the datastore. 
+Typically, datastores use **credential-based authentication** to confirm you have permission to access the storage service. They keep connection information, like your subscription ID and token authorization, in the [key vault](https://azure.microsoft.com/services/key-vault/) that's associated with the workspace. When you create a datastore that uses **identity-based data access**, your Azure account ([Azure Active Directory token](../active-directory/fundamentals/active-directory-whatis.md)) is used to confirm you have permission to access the storage service. In the **identity-based data access** scenario, no authentication credentials are saved. Only the storage account information is stored in the datastore. 
 
-To create datastores with identity-based data access via the Azure Machine Learning studio UI, see [Connect to data with the Azure Machine Learning studio](how-to-connect-data-ui.md#create-datastores).
+To create datastores with **identity-based** data access via the Azure Machine Learning studio UI, see [Connect to data with the Azure Machine Learning studio](how-to-connect-data-ui.md#create-datastores).
 
-To create datastores that use credential-based authentication, like access keys or service principals, see [Connect to storage services on Azure](how-to-access-data.md).
+To create datastores that use **credential-based** authentication, like access keys or service principals, see [Connect to storage services on Azure](how-to-access-data.md).
 
 ## Identity-based data access in Azure Machine Learning