Google Cloud Generative AI Leader – Certification Preparation Training Course
The Google Cloud Generative AI Leader certification confirms the expertise required to spearhead generative AI initiatives and comprehend how Google Cloud's generative AI solutions generate business value.
This live, instructor-led training (available online or onsite) targets beginner-level business professionals and leaders aiming to prepare for and successfully pass the Generative AI Leader certification exam.
Upon completing this training, participants will be able to:
- Articulate the basics of generative AI, foundation models, and the broader gen AI ecosystem.
- Outline Google Cloud's gen AI products, ranging from Gemini applications to Vertex AI and agents.
- Utilize methods to enhance model outputs (prompt engineering, grounding, RAG).
- Grasp business strategies, secure AI practices, and responsible AI principles for effective adoption.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practice sessions.
- Hands-on implementation within a live-lab environment.
Customization Options
- To request tailored training for this course, please contact us to make arrangements.
Course Outline
Introduction
- The Generative AI Leader certification: value and audience.
- Exam format, domains and weightings, and preparation strategies.
Fundamentals of Gen AI (~30%)
- Core gen AI concepts and use cases (AI, ML, LLMs, foundation models, multimodal and diffusion models, prompt engineering).
- Machine learning approaches (supervised, unsupervised, reinforcement) and the ML lifecycle.
- Foundation model selection criteria (modality, context window, cost, performance, customization).
- Data types and data quality in gen AI (structured vs unstructured, labeled vs unlabeled).
- The gen AI landscape layers and Google's foundation models (Gemini, Gemma, Imagen, Veo).
Google Cloud's Gen AI Offerings (~35%)
- Google Cloud's gen AI strengths and AI-optimized infrastructure (TPUs, GPUs, hypercomputer).
- Prebuilt offerings: Gemini app and Gemini Advanced, Gemini for Google Workspace, Gemini Enterprise.
- Customer experience: Customer Engagement Suite (Conversational Agents, Agent Assist, Conversational Insights).
- Developer enablement: Vertex AI / Agent Platform, Model Garden, and RAG offerings.
- Gen AI agent tooling (extensions, functions, data stores) and relevant Google Cloud services.
Techniques to Improve Gen AI Model Output (~20%)
- Overcoming foundation model limitations (knowledge cutoff, bias, hallucinations, edge cases).
- Prompt engineering techniques (zero-shot, one-shot, few-shot, role, prompt chaining, chain-of-thought, ReAct).
- Grounding and Retrieval-Augmented Generation (RAG).
- Sampling parameters for controlling output (temperature, top-p, token count, safety settings).
Business Strategies for Successful Gen AI Solutions (~15%)
- Implementation steps and solution selection methodology.
- Secure AI and Google's Secure AI Framework (SAIF).
- Responsible AI: privacy, bias and fairness, accountability, and explainability.
Exam Preparation
- Sample questions and domain-by-domain review.
- Full mock exam and answer analysis.
- Study plan and exam-day strategy.
Summary and Next Steps
Requirements
Prerequisites
- No technical prerequisites are required.
- A general understanding of business technology is beneficial.
Target Audience
- Leaders, managers, and decision-makers.
- Business professionals in any role looking to adopt generative AI.
- Individuals preparing for the Google Cloud Generative AI Leader certification.
Open Training Courses require 5+ participants.
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