Azure Data Scientists Associate Practice Exam

The function of Model.register() in Azure Machine Learning is to register a machine learning model. This process involves saving the model in a centralized model registry within Azure Machine Learning Workspace. By registering a model, you enable version control, simplifying the management of different iterations of the model as it evolves over time. Additionally, registered models can be easily retrieved, deployed, and monitored across various environments, facilitating collaboration among team members and integration into production systems.

The other options relate to different aspects of the machine learning workflow. Training a model involves using algorithms on data to improve its predictive capabilities, while evaluating a model refers to assessing its performance using metrics on a validation dataset. Submitting a training script is about starting the actual training process but does not relate to the process of managing or tracking model versions, which is the primary purpose of Model.register().