Google Cloud · 2026 Edition
A complete preparation guide written by Google Cloud-certified engineers. Covers the exam format,all 4 blueprint domains, a week-by-week study plan, and proven tips for passing first time.
3–6 weeks
Prep time
Beginner–Intermediate
Difficulty
50
Exam questions
700/1000
Pass mark
Exam code
Generative AI Leader
Full name
Google Cloud Generative AI Leader
Vendor
Google Cloud
Duration
90 minutes
Questions
50 items
Passing score
700/1000 (scaled)
Domains covered
4 blueprint domains
Recommended experience
Business or technology leadership background; no ML or coding experience required
Typical prep time
3–6 weeks
Google Cloud Generative AI Leader validates strategic and conceptual understanding of generative AI — how it works, where it creates business value, its risks, and how organisations should govern it. It is aimed at executives, product leaders, and senior professionals who need to lead AI initiatives without building models themselves.
Job roles this opens
Domain percentage weights are not currently available for this exam. The checklist below is still useful for planning your study.
Week 1
Generative AI Fundamentals: how LLMs work, foundation models, training vs fine-tuning, key capabilities and limitations
Tip: Focus on conceptual understanding. Know what a foundation model is (large pre-trained model adaptable to many tasks), why they are expensive to train but cheap to use, and what fine-tuning adds (domain-specific behaviour at lower cost than training from scratch).
Week 2–3
Google AI Ecosystem: Gemini models, Vertex AI, Google AI Studio, NotebookLM, Duet AI, Google Workspace AI features
Tip: Know the Google AI portfolio at a high level: Gemini (Google's foundation model family — Ultra, Pro, Flash, Nano), Vertex AI (enterprise ML platform for building and deploying models), Google AI Studio (free prototyping tool), and Gemini for Workspace (AI features in Docs, Sheets, Gmail, Meet).
Week 4–5
Business Value & Use Cases: productivity, code generation, customer service, content creation, data analysis, ROI measurement
Tip: The exam tests whether you can identify the right gen AI use case for a business scenario. High-value use cases: customer service chatbots (24/7 support, reduced ticket volume), code generation (developer productivity), document summarisation (knowledge worker efficiency), personalised marketing (engagement lift).
Week 6
Responsible AI & Governance: bias, hallucination, data privacy, copyright, AI regulations (EU AI Act), governance frameworks
Tip: Governance is a major exam theme. Know Google's AI Principles (be socially beneficial, avoid creating/reinforcing unfair bias, be safe, be accountable, etc.), the EU AI Act risk tiers (unacceptable, high, limited, minimal), and how to build an enterprise AI governance framework.
This exam is conceptual, not technical. Questions test whether you understand strategic implications, not implementation details. Focus on when and why to use gen AI, not how to build it.
Hallucination is a key risk to understand: LLMs can generate plausible-sounding but factually incorrect information. Mitigations include RAG (grounding in verified data), human review workflows, and domain-specific fine-tuning.
Prompt engineering matters even for leaders: know zero-shot, few-shot, and chain-of-thought at a conceptual level. Know that better prompts produce more reliable outputs and that prompt design is a skill organisations should invest in.
Data privacy risks in gen AI: know that enterprise deployments should use private model instances (not shared public models), that PII in prompts can be exposed in model training if using public APIs, and that contracts with AI providers should include data handling terms.
ROI measurement for gen AI: know common metrics — time saved per task, error rate reduction, customer satisfaction improvement, developer velocity increase. Know that gen AI ROI is often indirect (productivity) rather than direct (revenue), making business cases harder to build.
Apply everything in this guide with adaptive practice questions, detailed answer explanations, and domain analytics.