1Z0-1127-25 Fundamentals of Large Language Models Practice Question
A developer is using a large language model to generate code snippets. The model often produces code that is syntactically correct but functionally incorrect. What is the most effective way to improve the functional correctness of the generated code?
⚠ Common exam trap
Oracle often tests the misconception that increasing model parameters like temperature or max_tokens can improve output quality, when in fact these parameters control randomness and length, not functional correctness, which is best addressed through prompt engineering techniques like few-shot learning.
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
✓
Provide few-shot examples of correct code in the prompt.
Providing few-shot examples of correct code in the prompt directly demonstrates the desired functional behavior to the model. This technique, known as few-shot prompting, grounds the model's output in concrete examples, significantly improving the likelihood that the generated code will be functionally correct by aligning the model's pattern completion with the intended logic, not just syntax.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Provide few-shot examples of correct code in the prompt.
Why this is correct
Few-shot examples help the model understand the expected output.
- ✗
Increase the temperature parameter to generate more creative solutions.
Why it's wrong here
Higher temperature increases randomness, potentially worsening functional correctness.
- ✗
Ask the model to only output syntactically valid code.
Why it's wrong here
Syntax is not the issue; the model already produces correct syntax.
- ✗
Set max_tokens to a very high value to allow the model more room to think.
Why it's wrong here
More tokens do not improve logical correctness.
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