1Z0-1127-25 Prompt Engineering Practice Question
A developer notices that an LLM occasionally generates harmful or biased responses despite a system prompt instructing it to be safe. Which technique can help mitigate this at inference time without retraining?
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 a detailed system prompt with explicit safety constraints and use content filtering if available
Using a strong system prompt with explicit constraints is the first line of defense; also, setting low temperature can reduce unpredictable outputs. But among the options, updating the system prompt with more specific guidelines is the most direct approach.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the top-p value to 0.95
Why it's wrong here
Higher top-p increases diversity, which may worsen harmful outputs.
- ✓
Add a detailed system prompt with explicit safety constraints and use content filtering if available
Why this is correct
A well-crafted system prompt can reduce harmful responses; content filtering adds another layer.
- ✗
Use a higher temperature to encourage safer outputs
Why it's wrong here
High temperature makes outputs more random and less safe.
- ✗
Fine-tune the model on a curated safe dataset
Why it's wrong here
Fine-tuning is a separate process, not an inference-time technique.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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