AWS Certified Cloud Practitioner

Explanation

To achieve consistent, reproducible outputs across multiple calls to the same model with the same input, you need:

1. Low temperature: Temperature controls the randomness of the model's outputs. A low temperature (closer to 0) makes the model more deterministic and focused on the highest probability tokens.

2. Fixed random seed: The random seed ensures that any random processes in the model are initialized the same way each time, providing reproducibility.

Why other options are incorrect:

• A) High temperature: High temperature increases randomness and diversity in outputs, making them less consistent.

• B) Randomized seed: Using different random seeds each time would produce different outputs even with the same input.

• D) High top-p value: Top-p (nucleus sampling) controls the cumulative probability threshold for token selection. A high top-p value allows more diverse token selection, reducing consistency.

Key takeaway: For reproducible results in generative AI models, use low temperature combined with a fixed random seed.