AWS Certified Cloud Practitioner

Explanation

Increasing the number of epochs during model training helps improve accuracy because:

1. More training iterations: Each epoch represents one complete pass through the entire training dataset. More epochs allow the model to see the training data multiple times and learn more complex patterns.

2. Better convergence: With more epochs, the model has more opportunities to adjust its weights and biases to minimize the loss function, leading to better convergence on the optimal solution.

3. Foundation models require extensive training: Foundation models (FMs) are large-scale models that typically require extensive training over many epochs to achieve high accuracy levels.

Why other options are incorrect:

• A. Decrease the batch size: While smaller batch sizes can sometimes help with generalization, they don't directly increase accuracy to a specific acceptance level and may increase training time.
• C. Decrease the epochs: Reducing epochs would decrease training time but would likely reduce accuracy as the model would have less opportunity to learn from the data.
• D. Increase the temperature parameter: Temperature is typically used in sampling/generation tasks (like in language models) to control randomness, not for increasing accuracy during training.

Key takeaway: For foundation models, increasing the number of training epochs is a standard approach to improve model accuracy until it reaches a desired acceptance level.