Microsoft Azure AI Fundamentals (AI-900) Practice Exam

Why is it important to randomly split data into separate subsets when training a model?

1. To increase the accuracy of the model

2. To ensure all data is used for training

3. To test the model with data not used for training

4. To reduce the amount of data needed for training

The correct answer is: To test the model with data not used for training

Randomly splitting data into separate subsets is crucial in the training and evaluation process of a model because it allows for the testing of the model with data that was not included during the training phase. This step is essential to accurately assess the model's performance and generalizability to unseen data, which is critical in real-world applications. When a model is trained, it learns patterns and relationships present in the training data. However, if the same data is used for both training and testing, it can lead to overfitting, where the model performs exceptionally well on the training data but fails to generalize to new, unseen data. By using a separate test set that was not seen during training, we can effectively evaluate how well the model will perform in practice. This separation helps ensure that the metrics used to gauge the model's performance—such as accuracy, precision, and recall—are truly reflective of its ability to deal with real-world scenarios rather than just memorizing the training data. The other options highlight various facets of data usage in model training, but they do not capture the primary purpose of data splitting effectively. Increasing accuracy might happen through good practices but is not guaranteed by the mere act of splitting. Ensuring all data is used for training could lead