In Azure Machine Learning designer, which two components can you drag onto a canvas?

In Azure Machine Learning Designer, the ability to drag components onto a canvas is essential for building and visualizing machine learning workflows. The correct answer highlights the two components that are integral to this process: datasets and modules.

Datasets serve as the foundation for any machine learning project, providing the input data that algorithms will use for training and validation. In Azure Machine Learning, datasets can be imported or created directly within the environment, and they help set the stage for data manipulation, transformation, and model training.

Modules, on the other hand, are the functional units or building blocks in the workflow. They represent various operations such as data preprocessing, model training, scoring, and evaluation. Each module has specific functionalities that contribute to the overall machine learning pipeline, allowing data scientists and AI developers to apply different algorithms and techniques easily.

Together, datasets and modules form a comprehensive toolkit that enables users to create effective machine learning models within the Azure ecosystem. The combination of these two components allows users to construct workflows that can systematically process data and train models, making this choice the correct one.