1Z0-1127-25 Prompt Engineering Practice Question
A data scientist wants to generate a response that lists top-3 product features in a bulleted format with no introductory text. Which prompt engineering technique should be used?
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
✓
Include a clear task instruction specifying the format
Specifying the output format (bulleted list, no intro text) directly in the instruction is the most straightforward way to control the structure. Few-shot examples could also work but are unnecessary when a clear instruction suffices.
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 to ensure deterministic output
Why it's wrong here
Lowering temperature reduces randomness but does not guarantee a specific format; instruction is needed.
- ✗
Provide few-shot examples of desired output
Why it's wrong here
Few-shot examples guide the model to replicate a specific output structure by conditioning on prior demonstrations, but the stem requires the model to suppress all introductory text and produce only a bulleted list. This constraint is a formatting directive, not a pattern learned from examples; the correct technique is a system-message instruction that explicitly bans preamble. Few-shot prompting is tempting because it excels at teaching stylistic or structural patterns when the desired format is complex or variable, making it the correct choice for tasks like generating product descriptions in a fixed template.
- ✗
Use chain-of-thought prompting
Why it's wrong here
Chain-of-thought is for reasoning steps, not output formatting.
- ✓
Include a clear task instruction specifying the format
Why this is correct
Directly instructing the model to output a bulleted list without intro text is the most straightforward technique.
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