Google Cloud Generative AI Leader Full Course 2026
Pass Google Cloud Generative AI Leader Certification Exam in First Attempt | Learn Vertex AI, Prompt Engineering Basics
Lo que serás capaz de hacer
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 Domains Each 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 Thinking The 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 objective Identify potential ethical risks in an AI implementation Recommend deployment strategies for cross-functional teams Optimize prompt inputs for better model outputs These 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 Insights While 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 support Finance – Generating reports, analyzing transactions, or summarizing market data Retail – Personalizing product recommendations, automating customer support, or generating marketing content We provide practical examples , templates, and frameworks to translate Gen AI capabilities into enterprise-grade business outcomes . Ethical Considerations and Responsible AI Understanding how to deploy Generative AI responsibly is a critical skill for any leader. This course covers: AI governance frameworks Bias detection and mitigation strategies Data privacy and intellectual property concerns Model monitoring and human oversight We equip you with the knowledge to lead responsible AI adoption , aligned with best practices from Google Cloud’s Responsible AI guidelines .
- 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)
Temario
UNIDAD 1 - Introduction to the Generative AI Leader Role and Google Cloud Certification · 8 leccionesUNIDAD 1 - Introduction to the Generative AI Leader Role and Google Cloud Certification
UNIDAD 1 - Introduction to the Generative AI Leader Role and Google Cloud Certification
- Welcome to the Google Cloud Generative AI Leader Certification Course
- Welcome to the Generative AI Leader Certification Course
- Course Introduction & Exam Overview
- What Is a Generative AI Leader?
- Setting Up Your Learning Environment
- Case Study: The Business Value of Gen AI
- Demonstrating the Strategic and Business Value of Generative AI Leadership
- Quiz: Foundations of the Generative AI Leader Journey
UNIDAD 2 - Domain 1 – Fundamentals of Generative AI · 15 leccionesUNIDAD 2 - Domain 1 – Fundamentals of Generative AI
UNIDAD 2 - Domain 1 – Fundamentals of Generative AI
- Introduction to Generative AI
- Business Clarity: Explaining Generative AI to Non-Technical Executives
- Core Concepts: Tokens, Embeddings, and Attention
- Foundation Models and Model Families
- Model Parameters and Hyperparameters Explained
- Prompt Engineering: The Essential Skill
- Generative AI Architectures Explained
- Generative AI for Text and Code
- Generative AI for Images and Video
- Generative AI for Structured Data
- The Generative AI Lifecycle
- The Role of Data in Generative AI
- Generative AI and the Cloud
- Quiz: Fundamentals of Generative AI
- Explaining Generative AI's Business Value to a Skeptical Executive
UNIDAD 3 - Section 2A: Foundational Reinforcement (Optional but Exam-Critical) · 4 leccionesUNIDAD 3 - Section 2A: Foundational Reinforcement (Optional but Exam-Critical)
UNIDAD 3 - Section 2A: Foundational Reinforcement (Optional but Exam-Critical)
- Prompt Engineering, Diffusion Models, and Multimodal AI
- Supervised, Unsupervised, and Reinforcement Learning
- Stages of the ML Lifecycle: From Ingestion to Management
- Labeled vs Unlabeled Data with Business Examples
UNIDAD 4 - Domain 2 – Google Cloud Generative AI Offerings · 28 leccionesUNIDAD 4 - Domain 2 – Google Cloud Generative AI Offerings
UNIDAD 4 - Domain 2 – Google Cloud Generative AI Offerings
- Overview of the Vertex AI Platform
- Exploring the Vertex AI Model Garden
- Using the Vertex AI Workbench
- Understanding Vertex AI Endpoints
- Introduction to Vertex AI Pipelines
- Responsible AI on Google Cloud
- Managing Data with BigQuery and Cloud Storage
- Understanding the Gemini API
- Customizing Models with Fine-Tuning
- Customizing Models with Prompt Tuning
- Customizing Models with LoRA
- Vertex AI Search and Conversation
- Using the Generative AI Studio
- Advanced Prompt Engineering on Vertex AI
- Deploying Models with Vertex AI Endpoints
- Managing the MLOps Lifecycle with Pipelines
- Managing Model Artifacts with Vertex AI Metadata
- Monitoring Model Performance with Vertex AI Monitoring
- Security and Access Control with IAM
- Cost Management and Optimization
- Integrating Generative AI with Google Workspace
- Integrating with Third-Party Tools
- Vector Search in Vertex AI
- Data Preparation for Fine-Tuning
- Evaluating Generative AI Models
- The Future of Vertex AI and Generative AI
- Domain 2 Quiz: Google Cloud Generative AI & Vertex AI Fundamentals
- Defending a Google Cloud Generative AI Architecture to a Concerned Executive
UNIDAD 5 - Section 4A: Platform & Architecture Deep Dives · 4 leccionesUNIDAD 5 - Section 4A: Platform & Architecture Deep Dives
UNIDAD 5 - Section 4A: Platform & Architecture Deep Dives
- Infrastructure, Models, Platforms, Agents, and Applications
- Gemini, Gemma, Imagen, and Veo – Strengths and Use Cases
- RAG APIs and Prebuilt Retrieval-Augmented Generation
- Agent Builder and Low-Code AI Workflows
UNIDAD 6 - Domain 3: Techniques to Improve Generative AI Model Output · 20 leccionesUNIDAD 6 - Domain 3: Techniques to Improve Generative AI Model Output
UNIDAD 6 - Domain 3: Techniques to Improve Generative AI Model Output
- The Prompt Engineering Hierarchy
- Advanced Prompting: Chain-of-Thought and RAG
- Prompting vs. Fine-Tuning: Key Trade-offs
- Data Preparation for Fine-Tuning
- Fine-Tuning Models on Vertex AI
- Understanding LoRA and Prompt Tuning
- Retrieval-Augmented Generation (RAG) Deep Dive
- Data Augmentation Techniques
- Advanced Evaluation Techniques
- Managing Safety and Security in Prompts
- Prompting for Code Generation and Review
- Prompting for Structured Data Output
- Managing Context and Conversation History
- Evaluating Model Performance and Drift
- Human-in-the-Loop (HITL) Strategies
- Adversarial Testing and Red Teaming
- Managing Bias and Fairness
- Conclusion of Domain 3
- Quiz: Techniques to Improve Generative AI Output
- Justifying Prompting-First Strategy to Fix Model Output Issues
UNIDAD 7 - Domain 4: Business Strategies for Generative AI Success · 13 leccionesUNIDAD 7 - Domain 4: Business Strategies for Generative AI Success
UNIDAD 7 - Domain 4: Business Strategies for Generative AI Success
- Identifying High-Value Generative AI Use Cases
- Calculating Return on Investment (ROI) for Gen AI
- Managing Organizational Change and Adoption
- Navigating the Ethical and Legal Landscape
- Establishing an AI Governance Framework
- Building a Generative AI Center of Excellence (CoE)
- Managing the AI Talent Pipeline
- Measuring and Communicating Business Impact
- The Role of the Generative AI Leader
- Future Trends and Staying Current in Gen AI
- Quiz - Strategic Deployment & Responsible AI in the Enterprise
- Presenting a GenAI Strategy to Address Executive Concerns
- Quiz: Business Strategies for Generative AI Success
UNIDAD 8 - Section 7A: Strategic Governance & Trust Essentials · 2 leccionesUNIDAD 8 - Section 7A: Strategic Governance & Trust Essentials
UNIDAD 8 - Section 7A: Strategic Governance & Trust Essentials
- Data Privacy, Governance, and Model Control Options
- Data Quality and Accessibility: Why It Matters
UNIDAD 9 - Final Exam Prep and Leadership Readiness · 6 leccionesUNIDAD 9 - Final Exam Prep and Leadership Readiness
UNIDAD 9 - Final Exam Prep and Leadership Readiness
- Final Words and Certification Strategy
- Preparing for the Generative AI Leader Certification Exam
- Common Mistakes and Time Management Tips
- Course Conclusion and Next Steps
- Google Cloud Generative AI Leader Practice Test 1
- Google Cloud Generative AI Leader Practice Test 2
Tu certificado lo emite la SEP, no solo una plataforma
Al demostrar la competencia recibes un certificado emitido por la Secretaría de Educación Pública a través del Gobierno del Estado de México: un documento oficial que puedes presentar con confianza ante cualquier empleador o institución.
Tu certificado con INEC
- Lo emite la Secretaría de Educación Pública (SEP), a través del Gobierno del Estado de México
- Documento oficial con folio verificable en línea
- Lo otorga la Universidad Tecnológica Fidel Velázquez
Otras plataformas en línea
- Constancia privada emitida por la misma plataforma
- Solo acredita horas cursadas o asistencia
- Sin respaldo de una autoridad educativa
Tu itinerario, de la inscripción al certificado
- 1
Inscripción con pago único
Pagas $2,800 MXN una sola vez y entras de inmediato al curso.
- 2
Estudias a tu ritmo
9 unidades con videos, lecturas y actividades, disponibles 24/7 durante 12 meses.
- 3
Cuestionarios por unidad
8 cuestionarios para comprobar lo aprendido.
- 4
Certificado
Emitido por la SEP a través del Gobierno del Estado de México, con folio verificable.
Un certificado oficial de la SEP
- Emitido por la Secretaría de Educación Pública (SEP), a través del Gobierno del Estado de México. No es una constancia privada: es un documento oficial, y ese respaldo marca la diferencia frente a otras plataformas en línea.
- Contenido impartido por Manish Patel.
- Folio y QR verificables para compartirlo con quien te contrate.
Empieza hoy, a tu ritmo
Un solo pago y acceso inmediato al curso completo durante 12 meses.
- 9 unidades · 100 lecciones · 45 h
- 12 meses de acceso
- 8 cuestionarios de evaluación
- Certificado emitido por la SEP (Gob. del Estado de México) con folio verificable
Sobre tu inscripción
¿Cómo es el pago?
Un solo pago de $2,800 MXN. No hay mensualidades ni cargos recurrentes.
¿Cuánto tiempo tengo acceso?
12 meses desde tu inscripción, 24/7, para avanzar a tu ritmo.
¿Quién imparte el contenido?
El curso es impartido por Manish Patel; INEC gestiona tu inscripción, evaluación y certificado.
¿Qué pasa cuando vence mi acceso?
Puedes renovar tu cuenta tú mismo desde la plataforma, sin trámites ni esperas.


