Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A financial institution wants to deploy a gen AI model for fraud detection but must comply with strict regulations regarding explainability. What is the best strategy?
⚠ Common exam trap
Google Cloud often tests the misconception that post-hoc explainability tools (like Vertex AI Explainable AI) are equivalent to inherent model interpretability, leading candidates to choose complex models with added explanation layers instead of simpler, transparent models.
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 a smaller interpretable model with acceptable accuracy
Regulatory compliance for fraud detection demands explainability, which complex black-box models cannot provide. A smaller interpretable model (e.g., logistic regression or decision tree) offers transparency into decision factors, satisfying regulations like GDPR's right to explanation while maintaining acceptable accuracy for the use case.
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 Explainable AI with a complex model
Why it's wrong here
Explainable AI wraps a complex model with post-hoc attribution methods, but the underlying black-box architecture still cannot produce the deterministic, auditable reasoning regulators demand for fraud decisions. It is tempting because it adds transparency tooling to existing models, and would suit scenarios needing feature-attribution insights rather than strict regulatory explainability.
- ✗
Deploy multiple models and ensemble
Why it's wrong here
Ensembling combines predictions from several models, compounding opacity rather than resolving it; aggregating outputs from multiple black boxes yields no traceable decision path for auditors. It is tempting because ensembles boost accuracy and robustness, and would be the right choice when maximising predictive performance matters more than per-decision explainability.
- ✗
Use a large black-box model and rely on external auditing
Why it's wrong here
External auditing reviews a black-box model's behaviour after deployment but cannot expose the internal reasoning behind individual fraud decisions, so it fails the explainability requirement itself. It is tempting because independent audit provides governance assurance, and would suit environments where regulatory oversight accepts outcome monitoring rather than intrinsic model transparency.
- ✓
Implement a smaller interpretable model with acceptable accuracy
Why this is correct
Regulations demand explainability, which opaque deep models cannot provide. A smaller interpretable model such as logistic regression or a decision tree exposes its decision logic directly, satisfying the compliance constraint, provided its fraud-detection accuracy remains acceptable to the business.
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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.