1Z0-1127-25 LLM Fundamentals Practice Question
When using an LLM for code generation, a developer notices the model occasionally produces syntactically incorrect code. Which approach is most likely to reduce syntax errors while still allowing diverse output?
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
✓
Set temperature to 0 and use greedy decoding
Lowering temperature reduces randomness, making outputs more deterministic and less prone to errors, while still allowing some variation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use top-k sampling with k=100
Why it's wrong here
Large k still allows many options, potentially causing errors.
- ✗
Increase the context window size
Why it's wrong here
Context window length does not directly affect output determinism.
- ✓
Set temperature to 0 and use greedy decoding
Why this is correct
Greedy decoding (temperature=0) is deterministic and lowers syntax errors.
- ✗
Increase the temperature to 1.5
Why it's wrong here
Higher temperature increases randomness and error likelihood.
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