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Generative AI Leader Practice Question: A financial institution is deploying a generative…
A financial institution is deploying a generative AI model to recommend investment strategies. They must ensure the model's outputs are explainable to clients and regulators. Which approach BEST meets this requirement?
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 chain-of-thought prompting and cite sources for each recommendation
Explainability in GenAI can be achieved by grounding outputs in verifiable sources and showing the reasoning process. Chain-of-thought reasoning provides step-by-step logic, making recommendations auditable.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply differential privacy to the training data
Why it's wrong here
Differential privacy protects individual training records from inference; it adds noise and explains nothing about why a specific investment recommendation was produced. It is tempting because privacy is a genuine regulatory concern, but it is the right control when the requirement is training-data confidentiality, not output explainability.
- ✗
Aggregate model outputs across multiple runs to reduce variance
Why it's wrong here
Aggregating outputs across runs reduces variance between generations but yields no account of which features drove a given recommendation. It is tempting because stability suggests reliability, yet the requirement is per-output justification for clients and regulators, which aggregation obscures rather than provides.
- ✗
Use a larger model with more parameters to improve accuracy
Why it's wrong here
Scaling parameters increases predictive accuracy but yields no attribution of which inputs drove a recommendation, so outputs remain opaque to clients and regulators. Larger models are genuinely useful where raw performance on benchmarks matters and explainability is not mandated. Here the requirement is traceable reasoning, which parameter count does not supply.
- ✓
Implement chain-of-thought prompting and cite sources for each recommendation
Why this is correct
Chain-of-thought prompting exposes the model's intermediate reasoning steps, while source citations ground each recommendation in verifiable evidence. Together these satisfy the explainability constraint by letting clients and regulators trace how a strategy was derived, rather than receiving an opaque conclusion. This directly addresses the financial institution's regulatory transparency obligation.
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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.