Azure DP-100 Practice Exam

The goal of using automated machine learning (AutoML) in the context of Azure Machine Learning is to enable non-experts to build high-quality models quickly. AutoML simplifies the machine learning process by automating various tasks such as feature selection, model selection, and hyperparameter tuning. This makes it accessible for those who may not possess in-depth knowledge of machine learning techniques, allowing them to achieve good model performance without requiring extensive expertise.

By streamlining the workflow and removing complex, manual processes, AutoML empowers users to focus on solving business problems rather than becoming bogged down by the technical intricacies of model development. This capability is particularly beneficial in scenarios where quick insights or solutions are needed, democratizing access to machine learning across different levels of expertise in an organization.

The other options suggest objectives that are not the primary focus of AutoML. While data preprocessing is an important step in machine learning, AutoML does not provide complete automation of this step but rather assists in it as part of a larger process. Model selection is also streamlined rather than entirely eliminated, as there may still be cases where expert judgment is needed for selecting the most appropriate model based on specific requirements. Finally, data visualization continues to play a crucial role in understanding data and model performance and