AWS Certified Cloud Practitioner
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A product manager wants consistent, reproducible outputs across multiple calls to the same model using the same input. Which configuration should they use?
Real Exam
Community
RRitesh
0
A
High temperature
B
Randomized seed
C
Low temperature and fixed random seed
D
High top-p value
Explanation:
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.
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