Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A financial institution is deploying a generative AI solution that generates investment advice. They must ensure fairness, avoid toxic outputs, and comply with regulations like GDPR. Which TWO strategies should they implement? (Choose two.)
⚠ Common exam trap
A common misconception is that reducing model temperature or fine-tuning on compliant data alone can ensure safety and regulatory compliance, when in fact these measures do not address dynamic, context-dependent toxic outputs or logging requirements.
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 Vertex AI Safety Attributes to filter harmful content in both input and output.
Vertex AI Safety Attributes provides built-in safety filters that can detect and block harmful content (e.g., hate speech, toxicity, financial misinformation) in both user prompts and model outputs. This directly addresses the need to avoid toxic outputs and comply with regulations like GDPR, which require protecting users from harmful or biased advice.
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 Vertex AI Safety Attributes to filter harmful content in both input and output.
Why this is correct
Vertex AI Safety Attributes provides built-in safety filters that detect and block harmful content (e.g., hate speech, toxicity, financial misinformation) in both user prompts and model outputs, directly addressing the need to avoid toxic outputs and comply with regulations like GDPR.
- ✗
Set the model temperature to 0 to eliminate creativity and reduce bias.
Why it's wrong here
Setting temperature to 0 reduces randomness but does not eliminate bias or ensure fairness. This strategy does not address dynamic, context-dependent toxic outputs or regulatory compliance.
- ✓
Implement a human review process for any advice above a certain risk threshold.
Why this is correct
Implementing a human review process for high-risk advice adds a layer of oversight beyond automated filters, helping to ensure fairness and compliance with regulations like GDPR.
- ✗
Fine-tune the model exclusively on compliant financial documents.
Why it's wrong here
Fine-tuning on compliant financial documents only limits the training data but does not guarantee avoidance of toxic outputs or compliance with regulations in all contexts. It does not address input or output filtering.
- ✗
Disable request logging to avoid storing sensitive data.
Why it's wrong here
Disabling request logging would violate GDPR and other auditing requirements, as logging is necessary for monitoring, compliance, and incident response. It does not enhance safety or fairness.
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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.