1Z0-1127-25 LLM Fundamentals Practice Question
A developer is implementing a text generation pipeline using OCI Generative AI and needs to produce diverse, creative outputs for a marketing campaign. Which sampling strategy should they choose?
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-p sampling with p=0.9
Top-p (nucleus) sampling selects from a dynamically chosen set of tokens whose cumulative probability exceeds p, allowing diversity while avoiding the long tail of improbable tokens.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Beam search
Why it's wrong here
Beam search keeps multiple hypotheses but still lacks the randomness needed for creative diversity.
- ✗
Temperature sampling with temperature=0.0
Why it's wrong here
Temperature=0.0 is equivalent to greedy decoding, yielding deterministic and less creative results.
- ✓
Top-p sampling with p=0.9
Why this is correct
Top-p sampling introduces controlled randomness, making outputs more diverse and creative while maintaining coherence.
- ✗
Greedy decoding
Why it's wrong here
Greedy decoding always picks the most likely token, leading to repetitive and less creative outputs.
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