AWS Certified AI Practitioner Practice Exam

What solution should a company use to increase the accuracy of a foundation model?

1. Decrease the batch size

2. Increase the epochs

3. Decrease the epochs

4. Increase the temperature parameter

The correct answer is: Increase the epochs

Increasing the epochs in the training of a foundation model is a valid approach to enhance its accuracy. Each epoch represents one complete pass through the entire training dataset. When the model trains over more epochs, it has more opportunities to learn patterns and relationships within the data, allowing it to refine its weights and biases based on the input it receives. This iterative process can lead to improved accuracy as the model becomes better at minimizing error on the training set. However, there is a balance to be struck, as increasing epochs too much can lead to overfitting, where the model learns the training data too well, including the noise and outliers, thereby performing poorly on unseen data. It is crucial to monitor validation performance to ensure that increased epochs contribute positively to model accuracy without crossing into overfitting. The other options—decreasing the batch size, decreasing the epochs, and increasing the temperature parameter—don't effectively contribute to improved model accuracy in the same way. Decreasing the batch size can lead to more unstable gradients, while decreasing the epochs may reduce the model's training time without allowing it enough exposure to the data. Increasing the temperature parameter is relevant to certain model behaviors, such as influencing randomness in predictions, but it does not directly correlate with improving the foundational