Microsoft Azure AI Fundamentals AI-900 Practice Question

To evaluate the model's performance on unseen data, helping to prevent overfitting - This is the correct answer. Dividing a dataset into training and validation subsets allows the model to learn from the training data while being evaluated on a separate validation set. This helps assess the model's ability to generalize to new, unseen data, preventing overfitting where the model memorizes the training data but performs poorly on new data.

To increase the total amount of data available for training - This is incorrect because splitting the data reduces the amount of data available for training. The goal is to use a portion for training and another for validation, not to increase the total amount of data.

To reduce the training time by using smaller datasets - Splitting the data does not specifically aim to reduce training time. It’s more about evaluating model performance and preventing overfitting.

To ensure the model memorizes the training data perfectly - The goal is not for the model to memorize the training data. In fact, memorization (overfitting) is something practitioners want to avoid. The focus is on ensuring the model generalizes well to new, unseen data.