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Generative AI Leader Practice Question: A fintech company is deploying a generative AI…
A fintech company is deploying a generative AI system that offers investment advice. To comply with regulations and Google's AI Principles, they need to ensure appropriate human oversight and transparency. Which two actions should they take? (Choose two.)
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
✓
Require a human advisor to review and approve all AI-generated recommendations
Option C is correct because requiring a human advisor to review and approve all AI-generated recommendations directly implements the human oversight (human-in-the-loop) that Google's AI Principles and financial regulations demand for high-stakes advice, ensuring accountability before any recommendation reaches a client. Option D is correct because a Model Card is the standard transparency artifact that documents a model's intended use, limitations, performance, and ethical considerations, giving regulators and users the visibility needed to understand and trust the system. Option A is not required here: synthetic data can help with privacy but does not by itself provide human oversight or transparency, and it may even reduce real-world representativeness. Option B is wrong because fully automated trade execution removes the human oversight the scenario explicitly requires and could violate fiduciary and regulatory obligations. Option E is wrong because disabling logging destroys the audit trail needed for transparency, accountability, and regulatory review, and it does not enhance meaningful privacy in a compliant manner.
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 only synthetic data for training to avoid privacy issues
Why it's wrong here
Synthetic data addresses privacy and training-data scarcity, not human oversight or transparency in advice delivery. It is tempting because synthetic data genuinely supports privacy compliance, and would be correct if the requirement were avoiding personal data exposure rather than ensuring oversight.
- ✗
Allow the AI to execute trades automatically without human intervention
Why it's wrong here
Automatic trade execution removes the human oversight the scenario requires, and opaque automated decisions breach transparency obligations. It is tempting because straight-through processing suits low-risk, high-volume execution, but would only be acceptable where humans retain review authority over advice and trades.
- ✓
Require a human advisor to review and approve all AI-generated recommendations
Why this is correct
Human-in-the-loop review places a qualified adviser between the model's output and the client, catching erroneous or unsuitable recommendations before they take effect. This delivers the human oversight the stem demands for regulated investment advice, aligning with Google's AI Principles on accountability and responsible deployment.
- ✓
Develop and publish a Model Card that describes the model's limitations and intended use
Why this is correct
A Model Card documents intended use, limitations, performance characteristics and ethical considerations, giving regulators, advisers and clients the transparency the stem requires. Publishing it satisfies Google's AI Principles on being accountable and understandable, complementing human review of AI-generated investment recommendations.
- ✗
Disable logging of all interactions to maximize user privacy
Why it's wrong here
Disabling logging removes the audit trail that regulators and Google's AI Principles require for tracing advisory outputs back to their inputs, defeating transparency and oversight. Logging is tempting when privacy is the goal, and it would suit a system handling no regulated decisions or accountability requirements.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This Generative AI Leader practice question is part of Courseiva's free Google Cloud 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 Generative AI Leader exam.