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Scenario: Pennyworth's Haberdashery, a well-established clothier with multiple locations in Greater London, has acquired a Madrid-based fashion line and is integrating its systems with Microsoft Azure. To ensure a successful merger, Alfred Pennyworth has enlisted you as a Microsoft Azure Expert to guide the process. Currently, the team is focused on training a machine learning model using sales data. Dataset Structure: The sales data consists of CSV files, each containing monthly records with a consistent schema. The files are stored in Azure Blob Storage, organized under a parent folder named sales, with subfolders representing individual months: / sales / 01 - 2021 / sales . csv / 02 - 2021 / sales . csv / 03 - 2021 / sales . csv ... At the end of each month, a new folder is added following the same format. Project Requirements: To train the model effectively, the team must: Define a dataset that loads all existing sales data into a structure that can be easily converted to a DataFrame. Enable experiments to use data created before a specific past month, excluding any newer data. Minimize dataset registrations to keep the process efficient. Register the sales data as a dataset in the Azure Machine Learning workspace. Question: Which of the following actions should the IT team take to ensure the correct dataset definition, filtering, and registration within Azure Machine Learning?