Microsoft Azure AI Fundamentals (AI-900) Practice Exam

What does a validation set typically include?

1. Data to train the model

2. Data used to test the model's predictions

3. A random subset of the entire dataset

4. A set of hyperparameter configurations

The correct answer is: Data used to test the model's predictions

A validation set is an essential component in the process of building and evaluating a machine learning model. Specifically, it is used to assess how well the trained model performs on unseen data during the training process. The validation set typically includes a distinct portion of the dataset that is not used during the training phase but is instead set aside to evaluate the model's performance after it has been trained. This assessment helps in tuning the model’s parameters and prevents overfitting to the training data. By examining how well the model predicts outcomes on the validation set, practitioners can gain insights into its generalization capabilities and make necessary adjustments to improve performance before final testing. In contrast, the other choices do not accurately describe the primary purpose of a validation set. For instance, while the training set is specifically used to train the model, and a random subset refers to partitioning methods rather than a defined role in model evaluation, hyperparameter configurations are more related to model tuning rather than comprising the validation set itself. Thus, the role of the validation set as a tool for testing model predictions is pivotal in the model development lifecycle.