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1Z0-1127-25 Fundamentals of Large Language Models Practice Question

A developer is building a code generation assistant. The model occasionally produces syntactically correct but semantically wrong code. Which technique directly addresses semantic correctness?

⚠ Common exam trap

Oracle often tests the misconception that adjusting decoding parameters (temperature, beam search) or tokenization can fix semantic errors, when in fact only training techniques like RLHF that incorporate human feedback can directly improve semantic correctness.

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

✓

Apply RLHF using human-validated code examples

Reinforcement Learning from Human Feedback (RLHF) directly addresses semantic correctness by fine-tuning the model using human-validated code examples. This process teaches the model to prefer outputs that are not only syntactically valid but also logically correct and aligned with developer intent, reducing semantically wrong code generation.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Expand the token vocabulary
Why it's wrong here
Vocabulary size doesn't address semantic errors.
✗
Lower the temperature to 0
Why it's wrong here
Lower temperature makes output deterministic but not semantically correct.
✓
Apply RLHF using human-validated code examples
Why this is correct
RLHF directly optimizes for desired outcomes like semantic correctness.
✗
Increase beam search width
Why it's wrong here
Beam search improves likelihood of fluent output but not semantic accuracy.

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1Z0-1127-25 Fundamentals of Large Language Models Practice Question

Why each option matters

Option-by-option breakdown

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