1Z0-1127-25 LLM Fundamentals Practice Question
A developer is implementing a text generation pipeline and wants to produce diverse, creative outputs. They set temperature=1.2, top_k=50, and top_p=1.0. What is the MOST likely effect of this combination?
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
✓
The output will be diverse and creative, but may occasionally be incoherent or off-topic
Temperature >1 flattens the probability distribution, making low-probability tokens more likely. top_k=50 restricts to top 50 tokens, but top_p=1.0 imposes no cumulative probability cutoff. The combination yields diverse but potentially incoherent outputs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The output will be identical to greedy decoding because top_p=1.0 disables sampling
Why it's wrong here
top_p=1.0 means no nucleus truncation, but temperature and top_k still apply sampling.
- ✗
The output will be mostly factual because top_k filters out unlikely tokens
Why it's wrong here
Factuality is not guaranteed by these parameters; high temperature can introduce errors.
- ✓
The output will be diverse and creative, but may occasionally be incoherent or off-topic
Why this is correct
High temperature increases randomness, and the relaxed cutoffs allow less likely tokens, yielding creative but sometimes nonsensical outputs.
- ✗
The output will be highly deterministic and repetitive
Why it's wrong here
High temperature and permissive sampling promote diversity, not determinism.
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