Learning Path
Generative AI Leader Path
Master generative AI concepts on Google Cloud — from foundation models and prompt engineering to responsible AI strategy. 4 deep-dive study guides mapped to all exam sections.
Fundamentals of Generative AI
Master the foundational concepts of artificial intelligence, machine learning, and generative AI. This section covers AI/ML core concepts, foundation models, LLMs, diffusion models, the ML lifecycle, choosing models, data types and quality, and Google's foundation model family including Gemi
Google Cloud's Gen AI Offerings
The largest exam section. Covers Google's AI-first approach, enterprise AI platform, AI infrastructure (TPUs, GPUs, Hypercomputer), prebuilt products (Gemini app, Workspace integrations), customer experience tools, and the full Vertex AI developer platform including Model Garden, RAG, and Ag
Techniques to Improve Gen AI Output
Learn the key techniques for getting better results from generative AI models: prompt engineering (zero-shot, few-shot, chain-of-thought, ReAct), grounding, RAG, fine-tuning, human-in-the-loop review, monitoring, and sampling parameters (temperature, top-p, tokens).
Business Strategies for Generative AI
Covers the strategic and governance aspects of deploying generative AI in enterprise settings: implementation steps, securing AI systems with Google's SAIF framework, IAM and Security Command Center, and responsible AI principles including transparency, privacy, bias, fairness, accountabilit