Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A logistics company plans to use generative AI to draft responses to customer shipment inquiries. Legal requires a documented process for reviewing model outputs and handling harmful or inaccurate content before responses reach customers. Which practice should be built into the solution?
⚠ Common exam trap
The trap here is assuming fine-tuning or post-hoc reporting substitutes for an explicit review and audit process, when governance requires proactive controls.
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
✓
Implement human-in-the-loop review with logging and content safety filters before responses are sent.
A documented review process for generative AI outputs typically combines automated safety filters, human review before customer delivery, and logging that preserves an audit trail. Reactive reporting, fine-tuning for tone, or avoiding review altogether do not provide the proactive controls and documentation legal demands. The layered human-in-the-loop design satisfies both safety and accountability requirements.
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 past customer emails so it learns the company's tone and never produces harmful content.
Why it's wrong here
Fine-tuning can align tone and style but does not guarantee safety or factual accuracy, and it provides no review or audit trail. Harmful or fabricated content can still appear, especially for edge cases absent from training data. This option addresses style rather than the governance controls legal requires.
- ✗
Restrict the assistant to internal use only and skip any output review since customers never see it.
Why it's wrong here
The scenario describes drafting responses to customer shipment inquiries, so the outputs are intended for customers. Even for internal use, unreviewed outputs can propagate errors into downstream communications. Skipping review contradicts the legal requirement for a documented review process and does not match the stated use case.
- ✗
Deploy the model directly to customers and rely on users to report bad responses afterward.
Why it's wrong here
Post-hoc reporting shifts the burden of catching harmful or inaccurate content onto customers and exposes the company to reputational and legal risk. It provides no proactive review or documentation, which the legal team explicitly requires. This reactive posture fails the governance expectation of a controlled, documented review process.
- ✓
Implement human-in-the-loop review with logging and content safety filters before responses are sent.
Why this is correct
Human-in-the-loop review ensures a person validates outputs before they reach customers, while logging creates an auditable record and safety filters catch harmful content automatically. Together these satisfy the legal requirement for a documented review process. This layered approach also produces data for improving prompts and evaluating model behavior over time.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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