Ultimate access to all questions.
You have multiple CSV files with identical schemas stored in an Azure blob datastore. Each file is named sales.csv and is located in a month-year named folder under a parent sales directory. A new folder is added each month. You need to register a dataset for training a model with these requirements:
What should you do to register this sales data as a dataset in your Azure Machine Learning workspace?*
A
Create a tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/sales.csv' file every month. Register the dataset with the name sales_dataset each month, replacing the existing dataset and specifying a tag named month indicating the month and year it was registered. Use this dataset for all experiments._
B
Create a tabular dataset that references the datastore and specifies the path 'sales//sales.csv', register the dataset with the name sales_dataset and a tag named month indicating the month and year it was registered, and use this dataset for all experiments._
C
Create a new tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/sales.csv' file every month. Register the dataset with the name sales_dataset_MM-YYYY each month with appropriate MM and YYYY values for the month and year. Use the appropriate month-specific dataset for experiments.
D
Create a tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/sales.csv' file. Register the dataset with the name sales_dataset each month as a new version and with a tag named month indicating the month and year it was registered. Use this dataset for all experiments, identifying the version to be used based on the month tag as necessary._