Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A global corporation with 50,000 employees has seen rapid adoption of GenAI across marketing, product, and engineering teams. Each team selected its own models and cloud accounts, resulting in fragmented governance, unexpected costs, and varying output quality. The CFO demands a unified strategy to control costs and ensure consistency. The Chief AI Officer proposes several solutions. Which course of action best balances control with innovation?
⚠ Common exam trap
The tension between centralization and flexibility is a frequent topic in Google Gen AI exams. Candidates often mistakenly choose Option C (single model mandate) because it appears to enforce strict control, but the trap is that it ignores the need for team-specific innovation and risks shadow AI adoption.
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
✓
Establish a GenAI Center of Excellence (CoE) that provides approved models, shared APIs, and best practices, while allowing team-specific customizations
A GenAI Center of Excellence (CoE) provides centralized governance through approved models and shared APIs, enabling cost control and quality consistency while preserving team-level flexibility for innovation. This balances the CFO's need for unified strategy with the CAIO's goal of avoiding rigid mandates that stifle experimentation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Migrate all GenAI workloads to a single on-premises server to reduce cloud costs
Why it's wrong here
Consolidating onto one on-premises server removes cloud elasticity, capacity for 50,000 employees, and access to current models, so it cannot balance control with innovation. It is tempting as a cost-capping measure, and on-premises hosting would be correct where data residency or air-gapped operation forbids public cloud.
- ✓
Establish a GenAI Center of Excellence (CoE) that provides approved models, shared APIs, and best practices, while allowing team-specific customizations
Why this is correct
A CoE supplies approved models, shared APIs and best practices, centralising cost governance and output consistency across all 50,000 employees. Allowing team-specific customisations preserves the innovation autonomy that fragmented adoption previously delivered, satisfying the CFO's control demand without stifling each team.
- ✗
Mandate all teams use a single model (e.g., Gemini) via a centralized Vertex AI endpoint with usage quotas
Why it's wrong here
A single mandated model on one Vertex AI endpoint with quotas centralises governance and cost, but it blocks teams from selecting task-appropriate models, which the stem's innovation requirement demands. It is tempting because standardisation genuinely solves fragmentation, and it would be correct where consistency outweighs model diversity.
- ✗
Allow teams to continue using their own models but require them to submit monthly cost reports
Why it's wrong here
Monthly cost reports give visibility after spend occurs but leave each team's models, cloud accounts, and output quality fragmented, so governance stays reactive. It is tempting because reporting is low-friction and preserves autonomy, and it would be correct where teams already share a governance framework and only chargeback is missing.
Go deeper
Related to this question
About these practice questions
One of 1,008 original Generative AI Leader practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
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.