AI-102 Implement generative AI solutions Practice Question
A company is using Azure OpenAI to generate customer support responses. They want to ensure the model does not use any personally identifiable information (PII) in its outputs. What should they implement?
⚠ Common exam trap
Candidates often confuse 'instruction-based approaches' (prompts, system messages) with 'enforcement-based approaches' (content safety filters), assuming that telling the model not to do something is as effective as actively filtering the output.
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 Azure AI Content Safety to filter PII from the output.
Azure AI Content Safety provides built-in PII detection and redaction capabilities that can automatically scan and filter sensitive information from model outputs. This is the most reliable approach because it operates as a post-processing filter, catching PII that the model might generate despite instructions. Fine-tuning, prompt engineering, and system messages are all fallible because they rely on the model's compliance rather than enforced filtering.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Fine-tune the model on anonymized data.
Why it's wrong here
Fine-tuning may not prevent the model from generating PII in new contexts.
- ✗
Use prompt engineering to instruct the model to redact PII.
Why it's wrong here
Prompt engineering is not reliable for safety.
- ✓
Use Azure AI Content Safety to filter PII from the output.
Why this is correct
Azure AI Content Safety can detect and block PII.
- ✗
Use a system message instructing the model to avoid PII.
Why it's wrong here
System messages are not always followed.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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