1Z0-1127-25 LLM Fundamentals Practice Question
Which TWO of the following sampling strategies introduce randomness into text generation?
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 sampling
Top-k sampling and top-p (nucleus) sampling introduce randomness by sampling from a restricted token set. Temperature modifies the probability distribution and controls randomness but is a generation parameter, not a standalone sampling strategy. Greedy decoding and beam search are deterministic or near-deterministic.
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 explores multiple high-probability paths deterministically; it does not introduce randomness.
- ✗
Greedy decoding
Why it's wrong here
Greedy decoding always picks the most likely next token, so it is deterministic.
- ✗
Temperature sampling
Why it's wrong here
Temperature adjusts logits to make the distribution flatter or sharper, but it is a parameter rather than a sampling strategy itself.
- ✓
Top-k sampling
Why this is correct
Correct — Top-k sampling randomly samples from the k most probable tokens.
- ✓
Top-p (nucleus) sampling
Why this is correct
Correct — Top-p/nucleus sampling randomly samples from the smallest token set whose cumulative probability meets p.
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