1Z0-1127-25 Prompt Engineering Practice Question
A developer is using chain-of-thought prompting to solve a multi-step math problem. The model produces an incorrect final answer, but the intermediate reasoning steps appear logical. Which technique should be applied to improve accuracy?
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
✓
Use self-consistency by generating multiple reasoning chains and picking the majority answer
Self-consistency generates multiple reasoning paths (using a higher temperature) and then selects the most common final answer. This reduces the chance that a single flawed path leads to an incorrect result.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use self-consistency by generating multiple reasoning chains and picking the majority answer
Why this is correct
Self-consistency runs the chain-of-thought multiple times with a higher temperature and aggregates the answers to improve reliability.
- ✗
Reduce the max_tokens parameter so the model does not over-reason
Why it's wrong here
Reducing max_tokens would truncate reasoning, potentially removing necessary steps.
- ✗
Switch to zero-shot prompting to avoid reasoning errors
Why it's wrong here
Zero-shot prompting would remove reasoning steps, likely making the problem harder to solve correctly.
- ✗
Increase the temperature to 1.5 to encourage more diverse reasoning
Why it's wrong here
While self-consistency uses higher temperature, simply increasing temperature without aggregation would not help; also temperature is typically max 1.0.
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