AI-102 Implement generative AI solutions Practice Question
You are designing a generative AI solution that uses Azure OpenAI GPT-4 to answer customer support questions. The solution must comply with Microsoft's Responsible AI principles, particularly transparency and accountability. Which implementation approach best meets these requirements?
⚠ Common exam trap
It's easy for candidates to assume human review (Option A) or model fine-tuning (Option B) alone satisfy Responsible AI principles, but Microsoft explicitly requires automated content filtering, logging, and transparency disclaimers as part of a comprehensive compliance strategy.
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
✓
Enable content filtering, log all interactions, and include a disclaimer that responses are AI-generated.
It directly addresses Microsoft's Responsible AI principles of transparency and accountability. Enabling content filtering (via Azure AI Content Safety) ensures harmful outputs are blocked, logging all interactions provides an audit trail for accountability, and including a disclaimer that responses are AI-generated satisfies transparency by clearly informing users they are interacting with an AI system.
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 the model without any modifications, and have a human review all responses.
Why it's wrong here
Human review of every response adds oversight but supplies neither transparency to the end user nor accountability through logging, ownership and documented limitations. Review is tempting because human-in-the-loop sounds like strong governance, and it suits high-risk decisions, yet transparency requires disclosure and accountability requires traceable responsibility.
- ✗
Fine-tune the model on a curated dataset of support tickets and disable content filtering.
Why it's wrong here
Disabling content filtering removes the safety layer that transparency and accountability depend on, and fine-tuning alone does not provide disclosure or human oversight. Fine-tuning is tempting for domain accuracy on support tickets, and would suit a scenario prioritising answer relevance, but it cannot satisfy Responsible AI transparency obligations.
- ✓
Enable content filtering, log all interactions, and include a disclaimer that responses are AI-generated.
Why this is correct
Logging every interaction creates the audit trail that accountability demands, while the AI-generated disclaimer delivers the transparency requirement by telling users they are not reading human output. Content filtering addresses harm prevention rather than the two named principles, but the logging and disclosure pairing directly satisfies the stem's stated constraints.
- ✗
Use the default model deployment and rely on the model's inherent safety.
Why it's wrong here
Relying on default deployment and inherent safety provides no disclosure that users are interacting with AI, no logging for accountability, and no documented risk mitigation. Defaults are tempting because they require no configuration effort and Azure OpenAI ships with baseline filters, but that baseline alone does not evidence transparency or accountability.
Go deeper
Related to this question
About these practice questions
This AI-102 question is part of Courseiva's 761-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam 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.