Microsoft Azure AI Fundamentals AI-900 Practice Question

To evaluate the model during training and adjust hyperparameters - This is the correct answer. The validation dataset is used to evaluate the model's performance during training and help adjust hyperparameters for example learning rate and regularization. The goal is to ensure the model generalizes well to unseen data and to fine-tune the model's configuration.

To train the model by fitting its parameters to this data - This is the purpose of the training dataset, not the validation dataset. The training data is used to fit the model's parameters and allow the model to learn.

To test the model's performance after training is complete - This describes the function of the test dataset, which is used to evaluate the model's final performance after training and validation. The test data is not used during the training or hyperparameter tuning process.

To provide additional features for the model - The validation dataset does not provide additional features. It is a subset of the data that helps evaluate the model during training, but it doesn't directly affect the feature selection or engineering.