Google Cloud Generative AI Leader Cert Prep

Course details

Prepare to take the Google Cloud Generative AI Leader Certification exam with this comprehensive course. Learn about ML life cycles and methodologies, like supervised, unsupervised, and reinforcement learning, and transform theories into practical insights. Explore the Google suite of AI tools, including Vertex AI and Gemini, and discern how they seamlessly integrate into and transform enterprise applications. Discover mitigation strategies to address common generative AI risks, including bias and hallucinations, to ensure your AI deployments are ethical and reliable. Build your understanding of model selection, grounding techniques, and prompt engineering. Ideal for innovation leaders, product managers, and solution architects, this course offers you the frameworks to scale AI confidently, fostering enterprise-wide transformation and ensuring compliance.

Concepts

Introduction

Welcome to the Generative AI Leader Certification course
Google Cloud Generative AI Leader - Exam overview and preparation strategy
Who should take this course - Is this course right for you
How to navigate and maximize this course

Fundamentals of Generative AI - Concepts, Models, and Business Relevance

What is generative AI (generative AI explained) Definitions and differentiators
Core concepts of generative AI - AI, ML, NLP, LLMs, and foundation models
Mastering prompt engineering, diffusion models, and multimodal AI
Real-world business applications of generative AI
Supervised, unsupervised, and reinforcement learning in generative AI
The machine learning lifecycle - From data ingestion to responsible deployment
Google Cloud AI tools mapped to the ML lifecycle
Choosing the right foundation model - Modality, context, and cost
Model performance, fine-tuning, and security in generative AI
Data quality and accessibility - Foundations of responsible AI
Structured vs. unstructured data in generative AI workflows
Labeled vs. unlabeled data - Choosing the right training strategy
The gen AI technology stack - From infrastructure to applications
Gemini, Gemma, Imagen, and Veo - Google's foundation models explained

Google Cloud Gen AI - Platform, Tools, and Enterprise Capabilities

What sets Google apart in generative AI
Enterprise-ready AI - Privacy, scale, and reliability on Google Cloud
Open, governed, and accountable - Google's AI strategy for enterprises
TPUs, GPUs, and the AI hypercomputer - Scaling performance with Google
Data privacy, model governance, and control with Google Cloud AI
Gemini app vs. Gemini Advanced - Choosing the right enterprise tool
Google Agentspace - Custom Agents, NotebookLM, and search integration
Gemini for Google Workspace - AI inside Gmail, Docs, Sheets, and more
Vertex AI search vs. Google search - Enterprise knowledge retrieval
Customer engagement AI - Contact center, agent assist, and insights
Vertex AI, model garden, and AutoML - Tools for every developer level
Retrieval-augmented generation (RAG) - APIs and enterprise workflows
Vertex AI agent builder - Low-code tools for custom AI workflows
Extensions, plugins, and data access - making agents actionable
Speech, vision, translation, and document AI - Google Cloud APIs
Google AI Studio vs. Vertex AI Studio - Prototyping vs. production

Responsible Generative AI - Risks, Grounding, and Output Control

Common gen AI risks - Bias, hallucination & knowledge gaps
Mitigation strategies - Grounding, RAG, HITL & fine-tuning
Monitoring gen AI - KPIs, observability & feature store
Prompt engineering - Zero-shot, one-shot, and few-shot techniques
Role prompting and prompt chaining for structured AI behavior
Chain-of-thought and ReAct prompting - Reasoning and action
Grounding in gen AI - Enterprise, third-party, and public data
How RAG improves output accuracy, relevance, and trust
Tuning output with sampling parameters - Tokens, temperature, top-p

Scaling and Governing Generative AI in the Enterprise

Mapping solutions - Text, image, code, personalization
Privacy - Anonymization and pseudonymization
Bias, fairness, and ethical business use
Aligning solutions with business needs
Steps to integrate gen AI into the enterprise
Impact measurement techniques
The Google Secure AI Framework (SAIF)
IAM, secure-by-design infrastructure, monitoring tools
Transparency, explainability, and accountability

Final Exam Preparation and Leadership Readiness

Key domains to focus on for the exam
Sample questions and practice walkthrough
Common mistakes and time management tips
Final exam strategies and certification success