Google Cloud Generative Ai Leader Full Course 2025

Pass Google Cloud Generative AI Leader Certification Exam in First Attempt | Learn Vertex AI, Prompt Engineering Basics

What you'll learn

Understand the core concepts of Generative AI, large language models (LLMs), and their real-world applications

Learn how to use Vertex AI, including Vertex AI Studio, Agent Builder, Search, and Model Garden

Explore Google’s foundation models such as PaLM and Gemini, and how to choose the right model for your use case

Master prompt engineering techniques: zero-shot, few-shot, chain-of-thought, and grounding strategies

Identify and evaluate GenAI use cases for business, such as content generation, summarization, classification, and personalization

Apply ethical and responsible AI practices based on Google’s Secure AI Framework (SAIF)

Design and plan GenAI-powered solutions using Google Cloud tools

Get familiar with Google Cloud’s GenAI Leader certification exam format, question styles, and key focus areas

Practice with realistic sample questions and quizzes aligned with the official exam guide

Gain confidence to pass the Generative AI Leader certification exam on your first attempt

Requirements

None – beginner friendly: No prior Google Cloud or AI certification is required. We start with fundamentals, so you can join with little or no background in AI.

Technical background (optional): Basic familiarity with cloud computing or software development will help you follow along, but it’s not mandatory.

Tools: A (free) Google Cloud Platform account is recommended for optional hands-on practice with Vertex AI.

Description

This course is meticulously designed to provide complete alignment with the official Google Cloud Generative AI Leader Exam Guide, ensuring that learners are equipped with both the knowledge and confidence to succeed on exam day. The certification exam consists of 50 to 60 multiple-choice questions to be completed within 90 minutes, testing both conceptual understanding and strategic application of Generative AI principles within the Google Cloud ecosystem.Whether you're a business leader, product manager, strategist, or technical advisor, this course provides clear, actionable, and exam-relevant training for you to thrive as a certified Generative AI Leader.Comprehensive Coverage of All Exam DomainsEach module in this course is designed to mirror the structure and domain weightage of the official exam. This ensures your study time is spent wisely, focusing on the areas that matter most.Google Cloud Generative AI Leader Exam Domains & Weightage:Fundamentals of Generative AI – 30%Understand the core principles of Generative AI, including foundation models, training data, inference, prompt engineering, and responsible AI practices.Google Cloud’s Generative AI Offerings – 35%Explore Google Cloud’s Gen AI tools and platforms such as Vertex AI, Gemini models, and Agent Builder. Learn how to choose the right tools for various business and technical needs.Techniques to Improve Gen AI Model Output – 20%Learn best practices for prompt design, fine-tuning, and evaluation of model performance. Understand how to optimize accuracy, reduce bias, and avoid hallucinations.Business Strategies for a Successful Gen AI Solution – 15%Apply your knowledge to real-world enterprise scenarios, including AI strategy, governance, ethical deployment, cross-functional collaboration, and value realization.Each of these sections is richly detailed, explained in simple language, and reinforced through quizzes, case studies, and interactive exercises.Scenario-Based Learning & Strategic ThinkingThe Google Cloud Generative AI Leader exam is not a technical coding exam. It is geared towards leaders and decision-makers who must evaluate AI solutions, guide responsible deployment, and drive value at scale.This course includes numerous scenario-based questions, reflecting the real exam structure. You’ll practice answering questions that ask you to:Choose the best AI tool for a business objectiveIdentify potential ethical risks in an AI implementationRecommend deployment strategies for cross-functional teamsOptimize prompt inputs for better model outputsThese realistic business cases help you develop the critical thinking needed to pass the exam and apply your skills in real-world settings.Each question is followed by a thorough explanation that helps you understand the reasoning behind each correct and incorrect option. These tests are structured to closely replicate the actual exam’s difficulty and tone.Feedback from successful candidates highlights that many Udemy practice questions closely resemble the actual exam, making this resource invaluable for real-world preparation.Real-World Use Cases and Practical InsightsWhile theory is important, real-world application is essential. This course goes beyond definitions and academic concepts to show you how Gen AI is being used in sectors such as:Healthcare – Diagnosing conditions using medical imaging and Gen AI chat agents for patient supportFinance – Generating reports, analyzing transactions, or summarizing market dataRetail – Personalizing product recommendations, automating customer support, or generating marketing contentWe provide practical examples, templates, and frameworks to translate Gen AI capabilities into enterprise-grade business outcomes.Ethical Considerations and Responsible AIUnderstanding how to deploy Generative AI responsibly is a critical skill for any leader. This course covers:AI governance frameworksBias detection and mitigation strategiesData privacy and intellectual property concernsModel monitoring and human oversightWe equip you with the knowledge to lead responsible AI adoption, aligned with best practices from Google Cloud’s Responsible AI guidelines.

Overview

Section 1: Introduction

Lecture 1 Welcome to the Google Cloud Generative AI Leader Certification Course

Lecture 2 Welcome to the Generative AI Leader Certification Course

Lecture 3 Google Cloud Generative AI Leader - Exam Overview and Preparation Strategy

Lecture 4 Who Should Take This Course – Is This Course Right for You?

Lecture 5 How to Navigate and Maximize This Course

Section 2: Fundamentals of Generative AI: Concepts, Models, and Business Relevance

Lecture 6 What is Generative AI (Generative AI Explained)? Definitions and Differentiators

Lecture 7 Core Concepts of Generative AI: AI, ML, NLP, LLMs, and Foundation Models

Lecture 8 Mastering Prompt Engineering, Diffusion Models, and Multimodal AI

Lecture 9 Real-World Business Applications of Generative AI

Lecture 10 Supervised, Unsupervised, and Reinforcement Learning in Generative AI

Lecture 11 The Machine Learning Lifecycle: From Data Ingestion to Responsible Deployment

Lecture 12 Google Cloud AI Tools Mapped to the ML Lifecycle

Lecture 13 Choosing the Right Foundation Model: Modality, Context, and Cost

Lecture 14 Model Performance, Fine-Tuning, and Security in Generative AI

Lecture 15 Data Quality and Accessibility: Foundations of Responsible AI

Lecture 16 Structured vs. Unstructured Data in Generative AI Workflows

Lecture 17 Labeled vs. Unlabeled Data: Choosing the Right Training Strategy

Lecture 18 The Gen AI Technology Stack: From Infrastructure to Applications

Lecture 19 Gemini, Gemma, Imagen, and Veo: Google’s Foundation Models Explained

Section 3: Google Cloud Gen AI: Platform, Tools, and Enterprise Capabilities

Lecture 20 What Sets Google Apart in Generative AI

Lecture 21 Enterprise-Ready AI: Privacy, Scale, and Reliability on Google Cloud

Lecture 22 Open, Governed, and Accountable: Google’s AI Strategy for Enterprises

Lecture 23 TPUs, GPUs, and the AI Hypercomputer: Scaling Performance with Google

Lecture 24 Data Privacy, Model Governance, and Control with Google Cloud AI

Lecture 25 Gemini App vs. Gemini Advanced: Choosing the Right Enterprise Tool

Lecture 26 Google Agentspace: Custom Agents, NotebookLM, and Search Integration

Lecture 27 Gemini for Google Workspace: AI Inside Gmail, Docs, Sheets, and More

Lecture 28 Vertex AI Search vs. Google Search: Enterprise Knowledge Retrieval

Lecture 29 Customer Engagement AI: Contact Center, Agent Assist, and Insights

Lecture 30 Vertex AI, Model Garden, and AutoML: Tools for Every Developer Level

Lecture 31 Retrieval-Augmented Generation (RAG): APIs and Enterprise Workflows

Lecture 32 Vertex AI Agent Builder: Low-Code Tools for Custom AI Workflows

Lecture 33 Extensions, Plugins, and Data Access: Making Agents Actionable

Lecture 34 Speech, Vision, Translation, and Document AI: Google Cloud APIs

Lecture 35 Google AI Studio vs. Vertex AI Studio: Prototyping vs. Production

Section 4: Responsible Generative AI: Risks, Grounding, and Output Control

Lecture 36 Common Gen AI Risks: Bias, Hallucination & Knowledge Gaps

Lecture 37 Mitigation Strategies: Grounding, RAG, HITL & Fine-Tuning

Lecture 38 Monitoring Gen AI: KPIs, Observability & Feature Store

Lecture 39 Prompt Engineering: Zero-Shot, One-Shot, and Few-Shot Techniques

Lecture 40 Role Prompting and Prompt Chaining for Structured AI Behavior

Lecture 41 Chain-of-Thought and ReAct Prompting: Reasoning and Action

Lecture 42 Grounding in Gen AI: Enterprise, Third-Party, and Public Data

Lecture 43 How RAG Improves Output Accuracy, Relevance, and Trust

Lecture 44 Tuning Output with Sampling Parameters: Tokens, Temperature, Top-p

Section 5: Scaling and Governing Generative AI in the Enterprise

Lecture 45 Mapping Solutions – Text, Image, Code, Personalization

Lecture 46 Aligning Solutions with Business Needs

Lecture 47 Steps to Integrate Gen AI into the Enterprise

Lecture 48 Impact Measurement Techniques

Lecture 49 Google’s Secure AI Framework (SAIF)

Lecture 50 IAM, Secure-by-Design Infrastructure, Monitoring Tools

Lecture 51 Transparency, Explainability, and Accountability

Lecture 52 Privacy – Anonymization, Pseudonymization

Lecture 53 Bias, Fairness, and Ethical Business Use

Section 6: Final Exam Prep and Leadership Readiness

Lecture 54 Recap by Domain – What to Memorize and Master

Lecture 55 Sample Questions and Practice Walkthrough

Lecture 56 Common Mistakes and Time Management Tips

Lecture 57 Final Words and Certification Strategy

AI Enthusiasts: Individuals passionate about artificial intelligence, machine learning, and cloud computing.,Aspiring AI Leaders: Professionals who want to lead AI-driven initiatives and transform organizations using Google Cloud.,Business Leaders and Managers: Executives looking to leverage AI for business growth, operational efficiency, and innovation.,Technology Professionals: Cloud architects, data engineers, and AI engineers who want to build expertise in generative AI.,Anyone looking to enhance their career with cutting-edge AI skills and a Google Cloud certification.