1Z0-1127-25 Fundamentals of Large Language Models Practice Question
A company uses an LLM to generate product descriptions. The outputs are consistently too verbose and include irrelevant details. The prompt includes a simple instruction: 'Describe the product.' Which adjustment to the prompt is most likely to yield concise, relevant descriptions?
⚠ Common exam trap
Oracle often tests the misconception that adjusting model parameters (temperature or max_tokens) is the primary way to control output quality, when in fact prompt engineering with explicit constraints is a more direct and reliable method for achieving specific formatting or length requirements.
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
✓
Add constraints like 'Max 30 words. Focus on key features.'
Adding explicit constraints like 'Max 30 words. Focus on key features.' directly instructs the LLM to limit verbosity and prioritize relevant details. This technique, known as prompt engineering with constraints, is the most effective way to control output length and content without altering model parameters or relying on examples that may not generalize.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set temperature to 0.
Why it's wrong here
Low temperature reduces randomness but does not enforce brevity.
- ✗
Increase max_tokens to 500.
Why it's wrong here
More tokens encourage longer outputs, not shorter.
- ✓
Add constraints like 'Max 30 words. Focus on key features.'
Why this is correct
Explicit constraints directly limit length and scope.
- ✗
Include a few examples of desired short descriptions.
Why it's wrong here
Examples help but without explicit constraints, model may still be verbose.
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