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Duration 14 hours
Course Outline
Introduction
- The Generative AI Leader certification: its significance and intended audience.
- Exam structure, key domains with weightings, and preparation strategies.
Core Generative AI Concepts (~30%)
- Fundamental generative AI ideas and applications (AI, Machine Learning, LLMs, foundation models, multimodal and diffusion models, prompt engineering).
- Machine Learning methodologies (supervised, unsupervised, reinforcement learning) and the ML development lifecycle.
- Criteria for choosing foundation models (modality, context window length, cost, performance, and customization options).
- Data categories and quality considerations in generative AI (structured vs. unstructured, labeled vs. unlabeled data).
- Hierarchy of the generative AI landscape and Google’s foundation models (Gemini, Gemma, Imagen, Veo).
Google Cloud Generative AI Ecosystem (~35%)
- Google Cloud’s advantages in generative AI and its AI-specific infrastructure (TPUs, GPUs, hypercomputer).
- Ready-to-use solutions: Gemini app and Gemini Advanced, Gemini integration in Google Workspace, and Gemini Enterprise.
- Customer experience enhancements: Customer Engagement Suite (Conversational Agents, Agent Assist, Conversational Insights).
- Support for developers: Vertex AI / Agent Platform, Model Garden, and RAG services.
- Generative AI agent tools (extensions, functions, data stores) and associated Google Cloud services.
Methods for Enhancing Generative AI Model Performance (~20%)
- Mitigating foundation model constraints (knowledge cutoffs, bias, hallucinations, and edge cases).
- Prompt engineering strategies (zero-shot, one-shot, few-shot, role-based, prompt chaining, chain-of-thought, ReAct).
- Grounding techniques and Retrieval-Augmented Generation (RAG).
- Configuring sampling parameters to manage output (temperature, top-p, token limits, safety settings).
Strategic Business Approaches for Generative AI Success (~15%)
- Implementation roadmaps and methods for selecting the right solutions.
- Secure AI practices and Google’s Secure AI Framework (SAIF).
- Responsible AI principles: privacy, bias and fairness, accountability, and explainability.
Exam Readiness
- Practice questions and detailed review by exam domain.
- Comprehensive mock exam with detailed answer analysis.
- Structured study plan and tactics for exam day.
Wrap-up and Future Directions
Requirements
Entry Requirements
- No specific technical prerequisites are necessary.
- Basic awareness of business technologies is beneficial but not mandatory.
Target Audience
- Executives, managers, and strategic decision-makers.
- Business professionals in any capacity who are integrating generative AI into their workflows.
- Individuals studying for the Google Cloud Generative AI Leader certification exam.
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