1Z0-1127-25 LLM Fundamentals Practice Question
A developer is building a text generation application using OCI Generative AI and wants to control the creativity of the output. Which THREE sampling parameters can they adjust? (Choose three.)
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Top-k
Temperature scales the logits before softmax. Top-k limits the sampling pool to the k most likely tokens. Top-p (nucleus) sampling selects from tokens whose cumulative probability exceeds p. Beam search is a decoding strategy (not a sampling parameter), max tokens controls output length, and frequency penalty reduces repetition.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Top-k
Why this is correct
Top-k limits the sampling pool to the k most likely tokens, controlling diversity.
- ✓
Top-p
Why this is correct
Top-p (nucleus) sampling selects from a dynamically sized set of tokens, balancing creativity and relevance.
- ✗
Max tokens
Why it's wrong here
Max tokens controls the maximum length of the output, not the creativity or diversity.
- ✓
Temperature
Why this is correct
Temperature controls the randomness of token selection; higher values increase creativity.
- ✗
Beam search width
Why it's wrong here
Beam search is a deterministic decoding strategy that explores multiple sequences; it is not a sampling parameter for controlling randomness.
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