AI-102 Implement generative AI solutions Practice Question
Your team is developing an AI-powered document summarization solution using Azure OpenAI. You need to ensure that the solution complies with Microsoft's Responsible AI principles, specifically transparency. Which configuration should you implement?
⚠ Common exam trap
It's easy for candidates to confuse 'transparency' with 'accountability' or 'safety' and select diagnostic logging or content filtering, not realizing that transparency specifically requires user-facing disclosure of AI involvement.
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 system message that informs users the summary is generated by AI.
Transparency under Microsoft's Responsible AI principles requires that users are aware when they are interacting with an AI system. Adding a system message that explicitly states the summary is AI-generated fulfills this disclosure requirement. Diagnostic logging (A) aids in accountability and debugging but does not directly inform the user. Fine-tuning (B) improves accuracy but does not address transparency. Content filtering (C) mitigates harmful outputs but does not disclose AI involvement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure diagnostic logging to capture all model inputs and outputs.
Why it's wrong here
Diagnostic logging records inputs and outputs for operators and auditors, not for end users, so it does not deliver transparency to the people consuming summaries. It is tempting because logging supports accountability and auditing, but transparency specifically requires disclosing AI involvement and limitations to users.
- ✗
Fine-tune the model on a custom dataset to improve accuracy.
Why it's wrong here
Fine-tuning improves task accuracy and tone; it does not disclose to users that content is AI-generated or explain the model's limitations. It is tempting because custom training is a legitimate Azure OpenAI capability, but transparency obligations concern user-facing disclosure, not model quality.
- ✗
Enable content filtering with severity levels high and medium.
Why it's wrong here
Content filtering blocks harmful content, addressing harm prevention rather than transparency. It is tempting because filtering is a core Responsible AI control, but transparency requires disclosing AI involvement and limitations to users, which severity thresholds do not provide.
- ✓
Add a system message that informs users the summary is generated by AI.
Why this is correct
A system message disclosing AI generation directly satisfies transparency by informing users they are reading machine-generated output. Unlike content filters or metadata logging, which address harm prevention and auditability, this mechanism makes the AI's role visible at the point of interaction, meeting the stem's explicit transparency requirement.
Go deeper
Related to this question
About these practice questions
Courseiva writes every AI-102 question from scratch — 761 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-102 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-102 exam.