This guide provides a structured learning curriculum for the Google Cloud Generative AI Leader certification exam, covering generative AI fundamentals, output improvement, business strategy, and Google Cloud offerings.
This guide works best as a loop: read a chapter, test yourself with practice questions, look up unfamiliar terms in the glossary, then move to the next chapter.
14 chapters covering every exam objective. Each chapter includes key concepts, exam tips, common traps, comparison tables, and a 5-question quiz at the end.
Start Chapter 1Free timed and untimed practice with instant feedback and full explanations. Pick 10–120 questions per session. Filter by domain to drill your weak areas.
Go to practice testEvery Generative AI Leaderterm defined and searchable. Use it when a chapter mentions a concept you haven't seen before or want a quick refresher on.
Browse glossaryExam blueprint, domain weights, passing score, duration, cost, and registration links. Start here if you're new to this certification.
View exam guideFoundational Models and Large Language Models (LLMs)
Objective 1.2 · Describe foundational models and LLMs
Generative AI Capabilities and Use Cases
Objective 1.3 · Identify common use cases for generative AI
Prompt Engineering Fundamentals
Objective 2.1 · Explain prompt engineering principles
Advanced Prompt Techniques and Tuning
Objective 2.2 · Describe techniques to improve generative AI output
Managing Model Output Quality and Safety
Objective 2.3 · Identify methods to control output content and reduce hallucinations
Business Value and ROI of Generative AI
Objective 3.1 · Explain how generative AI creates business value
Responsible AI and Governance Strategy
Objective 3.2 · Describe responsible AI principles and governance considerations
Adoption Strategy and Risk Management
Objective 3.3 · Identify strategies for adopting generative AI and managing associated risks
Google Cloud Generative AI Stack Overview
Objective 4.1 · Describe the Google Cloud generative AI stack and key components
Vertex AI Platform and Model Garden
Objective 4.2 · Explain Vertex AI and Model Garden capabilities
Generative AI Studio and Prompt Design Tools
Objective 4.3 · Describe Generative AI Studio and its prompt design features
Foundation Models on Google Cloud (PaLM, Gemini, Imagen)
Objective 4.4 · Identify foundation models available on Google Cloud and their use cases
Model Tuning and Deployment on Vertex AI
Objective 4.5 · Explain model tuning (fine-tuning, adapter tuning) and deployment options
Google Cloud AI Solutions and Integrations
Objective 4.6 · Describe Google Cloud AI solutions and integrations with other services
Free Generative AI Leader practice questions with full explanations. Test what you learn chapter by chapter.
Generative AI Leader Practice Questions