The template should include realistic guidance on how to connect to data sources:

• For training, how to connect to data sources (whether of a type supported by Azure ML Datasource classes, or other ODBC data source type)
• Whether we should have a separate pipeline stage for pulling the data (separation of concerns + reuse of run steps) or just query the data in the training step
• How to manage credentials, one way is to use Key Vault in the run script itself (https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azureml.ipynb) but it's hard to find, should be part of the solution template (+any other method to manage credentials if appropriate)
• For scoring, similarly how to manage connectivity to data sources. Enterprise DS models often enrich incoming requests with data from a data mart (e.g. user profiles)