1Z0-1127-25 Prompt Engineering Practice Question
An organization wants to ensure that prompts submitted to an LLM do not contain sensitive customer data. Which practice is most effective?
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
✓
Sanitize user inputs by removing sensitive information before including them in the prompt
Sanitizing prompts before submission (e.g., removing PII, using placeholders) prevents sensitive data from being sent to the model. Other options either do not prevent data leakage or are less direct.
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 a low temperature to avoid generating sensitive data
Why it's wrong here
Temperature does not prevent sensitive data in prompts.
- ✗
Increase the max tokens to allow the model to ignore sensitive data
Why it's wrong here
Max tokens does not filter input data.
- ✗
Implement a prompt injection detection system that blocks malicious prompts
Why it's wrong here
Prompt injection is about adversarial attacks, not accidental inclusion of sensitive data.
- ✓
Sanitize user inputs by removing sensitive information before including them in the prompt
Why this is correct
Correct: input sanitization is a direct mitigation.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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