Google Generative Ai Leader Exam: Complete Preparation Guide

Google Generative Ai Leader Exam: Complete Preparation Guide
Published 9/2025
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz

Step-by-step guidance to help you grow as an AI leader and drive innovation

What you’ll learn

Understand the fundamentals of generative AI, machine learning, and deep learning.

Explore Google Cloud’s AI-first strategy, platform advantages, and AI-optimized infrastructure.
Work with tools like Vertex AI, Gemini, Workspace integrations, and RAG APIs.

Apply prompt engineering, fine-tuning, and monitoring techniques to improve model performance.

Build strategies for AI adoption, change management, and ethical implementation in your organization.

Test your knowledge with practice activities and quizzes that mirror the exam experience.

Requirements

No prior experience is needed – just basic tech familiarity and a desire to learn about generative AI and its business applications.

Description

Overview

Section 1: Fundamentals of Generative AI

Lecture 1 Introduction to AI

Lecture 2 How to study on Udemy?

Lecture 3 How to use an AI Assistant?

Lecture 4 What is Machine Learning?

Lecture 5 What is Deep Learning?

Lecture 6 Machine Learning Paradigms & Lifecycle

Lecture 7 Foundation Models and Business Use Cases

Lecture 8 Generative AI Applications in Business

Lecture 9 Data Quality and Types

Lecture 10 Generative AI Landscape Overview

Section 2: Google Cloud’s Generative AI Offerings

Lecture 11 Google’s AI-First Strategy & Platform Advantages
Lecture 12 AI-Optimized Infrastructure

Lecture 13 Prebuilt Generative AI Solutions (Gemini & Workspace)

Lecture 14 Prebuilt Generative AI Solutions (Agentspace)

Lecture 15 Vertex AI Search vs. Google Search Integration

Lecture 16 Customer Engagement Suite (CCAI)

Lecture 17 Developer Enablement – Vertex AI Platform

Lecture 18 Retrieval-Augmented Generation (RAG) APIs

Lecture 19 Building Custom Generative AI Agents

Lecture 20 Agent Tooling and Integrations

Lecture 21 Vertex AI Studio vs Google AI Studio

Section 3: Techniques to Improve Generative AI Model Output

Lecture 22 Limitations of Generative Models

Lecture 23 Grounding and Retrieval to Overcome Limits

Lecture 24 Fine-Tuning and Custom Models

Lecture 25 Human in the Loop (HITL)

Lecture 26 Continuous Model Monitoring

Lecture 27 Introduction to Prompt Engineering

Lecture 28 Advanced Prompting Techniques

Lecture 29 Safety and Parameter Tuning

Lecture 30 When to Use What – Scenario Discussion

Section 4: Business Strategies for Successful Generative AI Solutions

Lecture 31 Identifying High-Value Gen AI Use Cases

Lecture 32 Matching Solution Types to Needs

Lecture 33 Key Factors in Requirements

Lecture 34 Planning & Integration Steps

Lecture 35 Measuring Impact and ROI

Lecture 36 Security Considerations

Lecture 37 Responsible AI & Ethics

Lecture 38 AI Adoption and Change Management

Section 5: Conclusion

Lecture 39 Wrapping Up

Lecture 40 How to leave a review?

Lecture 41 How to get your certificate?

Aspiring Google AI Leaders preparing for the certification exam,Business managers looking to integrate AI solutions into their strategies,Technical professionals exploring Google Cloud’s AI tools,Data analysts and AI enthusiasts wanting to expand their knowledge of generative AI,Teams seeking practical insights into AI adoption and implementation