Google Cloud Generative AI Leader - Pearson Cert Prep (Video Course)

Description

Prepare for the Generative AI Leader certification with comprehensive training on how to lead successful generative AI initiatives by learning to match real business problems to the right Google Cloud gen AI tools, techniques, and governance

Description

• Understand Google Cloud Generative AI Leadership: Prepare for the Google Cloud Generative AI Leader Certification by mastering generative AI fundamentals, large language models (LLMs), foundation models, and enterprise AI strategy.
• Gain Hands-On Experience with Gemini and Vertex AI: Learn how to leverage Google Gemini, Vertex AI, Google AI Studio, NotebookLM, AI agents, and retrieval-augmented generation (RAG) to drive business innovation and AI transformation.
• Build High-Impact AI Solutions: Develop expertise in prompt engineering, model selection, grounding techniques, AI governance, responsible AI, and secure AI implementation to improve AI performance and business outcomes.

Concise Overview

Generative AI is transforming how organizations innovate, compete, and deliver value. In this comprehensive video course, youll gain the knowledge and strategic perspective needed to lead successful AI initiatives while preparing for the Google Cloud Generative AI Leader certification.

Designed for technology leaders, architects, consultants, product managers, and transformation professionals, this course from author Dan Sullivan explores the complete certification blueprint through a practical, business-focused lens. Youll learn the fundamentals of generative AI, large language models (LLMs), foundation models, and Googles AI ecosystem, including Gemini, Vertex AI, AI Studio, and NotebookLM. Along the way, youll discover how to identify high-value AI opportunities, select the right solutions for your organization, and drive responsible AI adoption at scale.

Through real-world examples, demonstrations, and expert guidance, youll learn how to improve AI outcomes with effective prompt engineering, evaluate enterprise use cases, establish AI governance frameworks, and measure the business impact of AI investments. Whether youre leading digital transformation efforts or helping shape your organizations AI strategy, this course provides the practical insights needed to make informed decisions and accelerate innovation. By the end of the course, youll build the confidence to take on the Google Cloud Generative AI Leader exam while developing the leadership skills required to turn AI potential into measurable business results.

Related Learning:

Watch: Google Cloud Essentials by Dan Sullivan: https://learning.oreilly.com/course/google-cloud-essentials/9780138174255/

Skill Level

• Beginner
• Intermediate

Course Requirements

Basic knowledge of AI, cloud computing, or business technology concepts is helpful but not required. No prior Google Cloud or Generative AI certification experience is needed.

About Pearson Video Training

Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, and Que. Topics include IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. Learn more about Pearson Video training at http://www.informit.com/video.

Sample Content

Table of Contents

Module 1: Fundamentals of Generative AI
Lesson 1: Core Gen AI Concepts and Terminology
1.1 Introduction to the AI landscape
1.2 AI approaches: From symbolic to generative
1.3 Understanding Large Language Models (LLMs)
1.4 Model customization techniques overview
Lesson 2: Machine Learning Approaches and Lifecycle
2.1 Machine Learning approaches explained
2.2 Google Cloud tools for ML lifecycle
2.3 Model training, deployment, and management
Lesson 3: Data Types and Quality
3.1 Understanding data types for AI
3.2 Data quality and business use cases
Lesson 4: Gen AI Landscape and Model Selection
4.1 The five core layers of gen AI
4.2 Model selection criteria
4.3 Googles foundation models
4.4 Demo: Exploring Gemini in Google AI Studio
4.5 Demo: Text prompting basics
4.6 Demo: Multimodal inputs
4.7 Module 1 quiz & review
Module 2: Google Clouds Gen AI Offerings
Lesson 5: Google Clouds Strategic Advantages
5.1 Googles AI-first philosophy
5.2 Ecosystem and infrastructure
5.3 Democratizing AI development
Lesson 6: Prebuilt Gen AI Solutions
6.1 Gemini app and enterprise solutions
6.2 Gemini for Google Workspace
6.3 Demo: Gemini in action (Workspace)
Lesson 7: Customer Experience Solutions
7.1 Agent platform search and discovery
7.2 Customer Engagement Suite
7.3 Contact Center intelligence
7.4 Demo: Building a Search App
Lesson 8: Developer Empowerment Tools
8.1 Gemini Enterprise Agent Platform overview
8.2 RAG and agent builder
8.3 Demo: Navigating Model Garden
8.4 Demo: Model deployment flow
8.5 Comparing Models
Lesson 9: Agent Tooling and Studio Environments
9.1 How agents use tools
9.2 Compute, AI APIs, and studio environments
9.3 Demo: AI Agent Builder
9.4 Module 2 quiz & review
Module 3: Techniques to Improve Gen AI Model Output
Lesson 10: Overcoming Foundation Model Limitations
10.1 Common foundation model limitations
10.2 Mitigation strategies and monitoring
10.3 Feature management and model updates
Lesson 11: Prompt Engineering Techniques
11.1 Basic prompt engineering techniques
11.2 Advanced prompt engineering and best practices
11.3 Demo: Prompt engineering experiments
Lesson 12: Grounding Techniques and Model Control
12.1 Types of grounding data
12.2 Google Cloud grounding options
12.3 Sampling parameters and safety settings
12.4 Module 3 quiz & review
Module 4: Business Strategies for Successful Gen AI Solutions
Lesson 13: Implementing Gen AI Solutions
13.1 Solution types and key factors
13.2 Technical constraints and decision framework
Lesson 14: Integration and Impact Measurement
14.1 Integration steps and readiness assessment
14.2 Measuring AI impact
Lesson 15: Secure AI
15.1 Security throughout the ML lifecycle
15.2 Googles Secure AI Framework
Lesson 16: Responsible AI
16.1 Transparency and privacy in AI
16.2 Bias, fairness, and accountability
16.3 Module 4 quiz & review

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