Exam AWS Certified AI Practitioner AIF-C01 topic 1 question 44 discussion

How can you prevent overfitting? • Increase the training data size • Early stopping the training of the model • Data augmentation (to increase diversity in the dataset) • Adjust hyperparameters (but you can’t “add” them)

To prevent overfitting, increase training data, use early stopping, apply data augmentation, and fine-tune hyperparameters without adding new ones.

Reducing the training data make the model prone to overfitting, and will likely further degrade the model's performance.

More diverse training data helps the model learn broader patterns and generalize better to unseen data in production. This reduces the risk of overfitting to the training set. Reduced Overfitting: The significant performance drop in production suggests overfitting to the training data. Increasing the data volume can help the model learn more robust features that are truly predictive rather than memorizing specifics of a limited dataset.. For A - Reducing the training data volume would likely exacerbate the problem rather than solve it. The model's poor performance in production suggests it's not generalizing well, which is often a result of insufficient or non-representative training data.

yes Overfitting.. but if the "Volume Data" is FIXED, meaning if they are going to reuse the same data.. this time the need to REDUCE it.. so "A" if they have MORE/EXTRA data to augment the one already available.. than C

yes Overfitting.. but if the "Volume Data" is FIXED, meaning if they are going to reuse the same data.. this time the need to REDUCE it.. so "A" if they have MORE/EXTRA data to augment the one already available.. than C

Model is overfitting. Needs more training data