1Z0-1127-25 Prompt Engineering Practice Question
A prompt engineer is testing different versions of a prompt to improve accuracy on a classification task. Which practice is most appropriate for systematic refinement?
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
✓
Run A/B tests on a representative evaluation set and measure accuracy
A/B testing with a holdout evaluation set allows objective comparison of prompt variants and measures performance improvements reliably.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Always increase the number of few-shot examples
Why it's wrong here
More examples may help but can also introduce noise; systematic testing is needed.
- ✓
Run A/B tests on a representative evaluation set and measure accuracy
Why this is correct
A/B testing with a labeled dataset provides quantitative evidence to guide refinement.
- ✗
Manually review outputs of a few examples and adjust based on intuition
Why it's wrong here
Manual review is subjective and not systematic; may miss edge cases.
- ✗
Change the model to a larger one
Why it's wrong here
Model size is not a prompt refinement technique; it may not address prompt quality.
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