Question 7 of 683
Governance Practices for Responsible Generative AI
A company is establishing governance practices for generative AI models. Which three actions are essential for responsible AI deployment?
Quick Answer
The answer is that implementing a human review process for critical decisions, conducting regular bias audits, and maintaining model versioning for traceability are the three essential actions for responsible AI deployment. These three pillars form the foundation of governance practices for generative AI because they directly address accountability, fairness, and reproducibility—core tenets of responsible AI. Bias audits catch systematic skew in model outputs, versioning ensures you can trace which model produced a given result, and human review acts as a safety net for high-stakes outputs that automated checks might miss. On the Google Cloud Generative AI Leader exam, this question tests your ability to distinguish mandatory governance controls from optional or secondary measures; a common trap is mistaking data leakage monitoring for a core governance pillar when it is actually a security concern, or assuming open-sourcing models is essential when it is a voluntary transparency choice. To remember the three essentials, think of the acronym BHR: Bias audits, Human review, and versioning for Reproducibility.
⚠ Common exam trap
Candidates often confuse operational security practices (like data leakage monitoring) with core governance actions (like versioning, auditing, and human review), leading them to select Option C as essential when it is actually a secondary security measure.
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 model versioning to track changes.
Model versioning (Option A) is essential because it enables tracking of changes to generative AI models over time, ensuring reproducibility, rollback capability, and compliance with governance policies. Without versioning, it becomes impossible to audit which model produced a specific output, undermining accountability and regulatory adherence.
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 model versioning to track changes.
Why this is correct
Versioning ensures reproducibility and accountability for model updates.
- ✓
Regularly audit model outputs for bias.
Why this is correct
Bias auditing is a fundamental responsible AI practice to detect and mitigate unfairness.
- ✗
Monitor for data leakage from training data.
Why it's wrong here
Important for privacy but often part of security, not always considered core governance for responsible AI.
- ✓
Implement a human review process for critical decisions.
Why this is correct
Human oversight is crucial for high-stakes applications to prevent harm.
- ✗
Open-source the model to ensure transparency.
Why it's wrong here
Open-sourcing is not required and may not be appropriate for proprietary or sensitive use cases.
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Same concept, more angles
2 more ways this is tested on Generative AI Leader
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which THREE are essential components of a responsible AI strategy for GenAI? (Select three.)
easy- A.Use of only open-source models
- B.Maximum model size
- ✓ C.Human oversight for critical decisions
- ✓ D.Model transparency and explainability
- ✓ E.Bias detection and mitigation
Why C: Human oversight for critical decisions (C) is essential because GenAI models can produce plausible but incorrect or harmful outputs. A responsible AI strategy mandates that a human-in-the-loop reviews high-stakes outputs, such as medical diagnoses or financial approvals, to prevent automated errors from causing real-world harm. This aligns with the principle of human accountability in AI governance frameworks like the NIST AI Risk Management Framework.
Variation 2. A financial services firm must comply with regulations when using gen AI. Which two measures are critical?
hard- ✓ A.Implement audit trails
- B.Deploy without risk assessment
- C.Use a closed-source model
- ✓ D.Use explainable AI
- E.Use only synthetic data
Why A: Audit trails are critical for compliance because they provide a tamper-evident, chronological record of all AI model inputs, outputs, and decisions. This enables firms to demonstrate regulatory adherence (e.g., under GDPR or SOX) by reconstructing the exact sequence of events that led to a specific AI-generated output, which is essential for accountability and forensic review.
Last reviewed: Jul 4, 2026
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