A data scientist trains a regression model on a dataset with 100 features and 10,000 samples. The model achieves a low training error but a much higher error on a held-out test set. Which approach is most likely to improve the model's generalization performance?

Increase the complexity of the model by adding more layers or parameters

Increasing model complexity would typically worsen overfitting, as the model would have more capacity to memorize the training data.

Add more training data

Adding more data can reduce overfitting, but it is not always feasible. Reducing features is often a more practical first step. However, this option is not the most likely among the given choices because reducing features directly addresses the high dimensionality.

Use a different train-test split ratio like 80-20 instead of 70-30

Changing the split ratio slightly may affect evaluation but does not address the underlying overfitting problem. The model architecture remains unchanged.

What does this AI-900 question test?

Read the scenario before looking for a memorised answer.

What is the correct answer to this question?

The correct answer is: Reduce the number of features or apply regularization — The model is overfitting, meaning it has learned noise in the training data and fails to generalize. Reducing the number of features (dimensionality reduction) or simplifying the model is a standard technique to combat overfitting. Adding more training data could also help, but reducing features is a more direct and often immediate countermeasure.

What should I do if I get this AI-900 question wrong?

Then try more questions from the same exam bank and focus on understanding why the wrong options are tempting.