AWS Certified Cloud Practitioner

Explanation

To achieve consistent, reproducible outputs across multiple calls to the same model with the same input, the correct configuration is:

• Low temperature: Temperature controls the randomness of the output. Lower temperature values (closer to 0) make the model more deterministic and focused on the most likely tokens, reducing randomness.

• Fixed random seed: Setting a fixed seed ensures that the random number generator produces the same sequence of values each time, making the output reproducible.

Why other options are incorrect:

• A) High temperature: Increases randomness and variability in outputs, making them less consistent.
• B) Randomized seed: Using different seeds would produce different outputs even with the same input.
• D) High top-p value: Top-p (nucleus sampling) filters the probability distribution, but doesn't guarantee reproducibility like a fixed seed does.

This combination ensures deterministic behavior while maintaining model quality for consistent results across multiple inference calls.