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You are a business professional, manager, or non-technical stakeholder with little prior AI or Google Cloud knowledge. You need to build conceptual fluency across all four domains.
Week 1Learn Domain 1 fundamentals: the difference between AI, machine learning, and generative AI; foundation models and LLMs; multimodal and diffusion models; supervised vs unsupervised vs reinforcement learning; and the ML lifecycle (data ingestion, preparation, training, deployment, management). Read the official study guide sections 1.1-1.2
Week 2Finish Domain 1: structured vs unstructured data, labeled vs unlabeled data, data quality dimensions (completeness, consistency, relevance, availability, cost, format), the five gen AI landscape layers (infrastructure, models, platforms, agents, applications), and Google's foundation models (Gemini, Gemma, Imagen, Veo). Learn the create/summarize/discover/automate use case framework
Week 3Study Domain 2 (largest at 35%): Google's AI-first approach and enterprise-ready platform, AI Hypercomputer with TPUs and GPUs, the Gemini app / Gemini Advanced / Gems, Gemini Enterprise, Gemini for Google Workspace, the Customer Engagement Suite (Conversational Agents, Agent Assist, CCaaS), and the Agent Platform (Model Garden, Agent Search, AutoML)
Week 4Cover Domain 3: foundation model limitations (bias, hallucinations, knowledge cutoff, edge cases) and mitigations (grounding, RAG, prompt engineering, fine-tuning, HITL); prompt engineering techniques (zero/one/few-shot, role, chain-of-thought, ReAct); grounding types; and sampling parameters (temperature, top-p, token count, safety settings). Take your first full practice exam
Week 5Study Domain 4: steps to implement a transformational gen AI solution, measuring impact/ROI, the Secure AI Framework (SAIF), IAM and Security Command Center, and Responsible AI principles (transparency, privacy, fairness, accountability, explainability). Review all missed questions, then take a second practice exam aiming for 85%+ before scheduling
Exam Overview
Format
50-60 multiple choice and multiple select questions, 90 minutes. Non-technical, business-focused. Available in English, Japanese, Spanish, and Portuguese.
Scoring
Pass/fail only — Google does not disclose a numeric score. The 70% threshold shown is a widely cited community estimate. There is no penalty for wrong answers, so answer every question.
Domains & Weights
- Fundamentals of Generative AI30%
- Google Cloud's Generative AI Offerings35%
- Techniques to Improve Generative AI Model Output20%
- Business Strategies for a Successful Generative AI Solution15%
Registration
$99 USD. Delivered online-proctored (remote) or at onsite testing centers via Pearson VUE. Exam fee is $99 USD plus applicable taxes. No prerequisites. Certification is valid for 3 years.
Topic Priority Table
Not all topics are tested equally. Focus your study time on Tier 1 first, then Tier 2. Tier 3 topics rarely appear — just recognize what they do.
Domain 130% of exam
Fundamentals of Generative AI
The conceptual foundation of the exam. Covers core gen AI terminology, machine learning approaches, the ML lifecycle, how to choose a foundation model, business use cases (create, summarize, discover, automate), data types and quality, the gen AI landscape layers, and Google's foundation model families (Gemini, Gemma, Imagen, Veo). Master this vocabulary — every later domain builds on it.
Key Topics
GeminiGemmaImagenVeoFoundation modelsLarge language modelsModel Garden
Must-Know Concepts
- Definitions and relationships: artificial intelligence (broadest), machine learning (learns from data), generative AI (creates new content), foundation models (large pre-trained models adaptable to many tasks), LLMs (text foundation models), multimodal models (handle text/image/audio/video), and diffusion models (generate images by denoising)
- Prompt engineering vs prompt tuning: prompt engineering crafts the input text; prompt tuning is a lightweight training technique that learns soft prompts to adapt a model
- The three ML approaches: supervised (labeled data), unsupervised (unlabeled data, finds patterns/clusters), and reinforcement learning (learns from reward feedback)
- The ML lifecycle stages and Google Cloud tools for each: data ingestion, data preparation, model training, model deployment, and model management
- How to choose a foundation model for a use case: modality, context window, security, availability/reliability, cost, performance, and fine-tuning/customization needs
- The create/summarize/discover/automate framework of gen AI business use cases: text/image/code/video generation, data analysis, and personalized user experiences
- Data concepts: structured vs unstructured data (with real-world examples), labeled vs unlabeled data, and data quality dimensions (completeness, consistency, relevance, availability, cost, format)
- The five layers of the gen AI landscape: infrastructure, models, platforms, agents, and applications — and the business implications of each
- Google's foundation models and their strengths: Gemini (multimodal flagship), Gemma (open/lightweight), Imagen (image generation), Veo (video generation)
Common Exam Traps
Generative AI is a subset of ML, which is a subset of AI. Do not treat them as interchangeable — the exam tests the hierarchy
Labeled vs unlabeled and structured vs unstructured are two independent axes. Unstructured data (like images) can still be labeled; structured data can be unlabeled
Prompt tuning is a training technique that adapts the model; prompt engineering only changes the input text. The similar names are a deliberate trap
Diffusion models generate images (and video), not text. LLMs generate text. Match the model type to the modality in the question
Choosing a foundation model is about business factors (cost, latency, context window, security, modality), not just accuracy — the exam frames it as a leadership trade-off
Domain 235% of exam
Google Cloud's Generative AI Offerings
The largest domain. Covers why Google Cloud is a leader in gen AI (AI-first approach, enterprise-ready platform, open ecosystem, AI-optimized infrastructure), the prebuilt offerings that empower work (Gemini app, Gemini Advanced/Gems, Gemini Enterprise, Gemini for Workspace), customer-experience offerings (Customer Engagement Suite, Agent Search), developer offerings (Agent Platform, Model Garden, RAG), and agent tooling plus the pre-built AI APIs. This is a product-recognition domain.
Key Topics
Vertex AI / Agent PlatformModel GardenGemini EnterpriseGemini for WorkspaceCustomer Engagement SuiteAI HypercomputerPre-built AI APIsAgent Studio
Must-Know Concepts
- Google's differentiators: an AI-first approach, an enterprise-ready platform (responsible, secure, private, reliable, scalable), a comprehensive AI ecosystem integrated across Google products, and an open approach offering first-party, open, and third-party models
- AI-optimized infrastructure: the AI Hypercomputer combining Google's custom-designed TPUs, GPUs, data centers, and cloud computing — the foundation of Google's scale and efficiency
- Data control and democratization: Google Cloud gives customers control over their data (security, privacy, governance) and democratizes AI with low-code/no-code tools, pre-trained models, and APIs
- Gemini app and Gemini Advanced: the consumer assistant, with Gems as customizable reusable assistants for recurring tasks
- Gemini Enterprise: enterprise multimodal search, Cloud NotebookLM API capabilities, and custom agent building grounded in first-party data
- Gemini for Google Workspace: gen AI embedded in Gmail, Docs, Sheets, Slides, and Meet for workforce productivity
- Customer Engagement Suite: Conversational Agents (customer-facing virtual agents), Agent Assist (real-time help for human agents), Conversational Insights, and Contact Center as a Service (CCaaS)
- Agent Platform for developers: Model Garden (model catalog), Agent Search (enterprise/external search), and AutoML; plus Google Cloud's RAG offerings (prebuilt RAG with Agent Search, RAG APIs)
- Agent tooling: extensions and functions (call external APIs/services), data stores (grounding data), and plugins — how agents interact with the external environment
- Pre-built AI APIs for agent tooling: Speech-to-Text, Text-to-Speech, Translation, Document Translation, Document AI, Cloud Vision, Video Intelligence, and Natural Language
- When to use Agent Studio (enterprise/production agent building) vs Google AI Studio (fast prototyping with Gemini)
Common Exam Traps
Gemini for Workspace (productivity inside Google's office apps) is not the same as Gemini Enterprise (enterprise search and custom agents over company data). The exam tests this distinction directly
Conversational Agents serve customers directly; Agent Assist helps human agents. Do not swap them
Google AI Studio is for quick prototyping; Agent Studio is for building/deploying enterprise agents. Match the environment to the maturity of the project
Model Garden is a catalog of models (first-party, open, third-party), not a single model. It embodies Google's open approach
Match the pre-built API to the task: Document AI parses documents, Vision analyzes images, Video Intelligence analyzes video, Natural Language analyzes text sentiment/entities, Translation handles languages
Domain 320% of exam
Techniques to Improve Generative AI Model Output
Covers how to overcome foundation model limitations and get better, more trustworthy output. Topics include the limitations themselves (data dependency, knowledge cutoff, bias, hallucinations, edge cases), Google's recommended mitigations (grounding, RAG, prompt engineering, fine-tuning, human-in-the-loop), continuous monitoring and evaluation, prompt engineering techniques, grounding types and Google Cloud grounding offerings, and sampling parameters. Grounding and RAG are the most heavily tested topics here.
Key Topics
GroundingRAG (Agent Search, RAG APIs)Grounding with Google SearchPrompt engineeringFine-tuningSampling parametersHuman-in-the-loop
Must-Know Concepts
- Common foundation model limitations: data dependency (only as good as training data), knowledge cutoff (unaware of recent events), bias and fairness issues, hallucinations (confident but false output), and edge cases
- Google-recommended mitigations: grounding, retrieval-augmented generation (RAG), prompt engineering, fine-tuning, and human-in-the-loop (HITL) for high-stakes decisions
- Continuous monitoring and evaluation practices: automatic model upgrades, KPIs, security patches/updates, versioning, performance tracking, drift monitoring, and Feature Store
- Prompt engineering techniques and use cases: zero-shot (no examples), one-shot (one example), few-shot (several examples), role prompting (assign a persona), and prompt chaining (break a task into linked prompts)
- Advanced prompting: chain-of-thought (ask the model to reason step by step) and ReAct (reasoning plus acting with tools) — and when to use each
- Grounding: connecting model output to trusted data. Differentiate grounding with first-party enterprise data, third-party data, and world data
- How RAG affects output: retrieving relevant context at query time makes answers more factual and current, reducing hallucinations and overcoming the knowledge cutoff
- Google Cloud grounding offerings: prebuilt RAG with Agent Search, RAG APIs, and Grounding with Google Search
- Sampling parameters: token count / output length, temperature (randomness), top-p (nucleus sampling), and safety settings — and how each shapes model behavior
Common Exam Traps
Grounding/RAG addresses hallucinations, knowledge cutoff, and factual accuracy. Prompt engineering improves phrasing and structure. Fine-tuning changes behavior/style. Match the mitigation to the specific limitation
Zero-shot means NO examples in the prompt; few-shot means SEVERAL examples. One-shot is exactly one. Count the examples in the scenario
Chain-of-thought is about making the model reason step by step; ReAct combines reasoning with taking actions/using tools. Do not confuse them
Lower temperature = more deterministic and factual output; higher temperature = more creative and varied. If the scenario needs consistency and accuracy, lower the temperature
Human-in-the-loop is the right mitigation for high-risk or high-stakes decisions where errors are costly, even after grounding and prompt engineering
Domain 415% of exam
Business Strategies for a Successful Generative AI Solution
The leadership and governance domain. Covers Google Cloud's recommended steps to implement a transformational gen AI solution (choosing the right solution, aligning to business requirements and technical constraints, integrating into the organization, and measuring impact), securing AI systems with the Secure AI Framework (SAIF) and Google Cloud security tools, and the importance of Responsible AI (transparency, privacy, fairness, accountability, explainability).
Key Topics
Secure AI Framework (SAIF)IAMSecurity Command CenterResponsible AI principlesSecure-by-design infrastructure
Must-Know Concepts
- Types of gen AI solutions to match to needs: text generation, image generation, code generation, and personalized user experiences
- Key factors that influence gen AI needs: business requirements and technical constraints — choose the solution that fits both
- Google Cloud's recommended steps to implement a transformational solution: identify the need, choose the right solution, integrate it into the organization, and measure impact
- Techniques to measure the impact of gen AI initiatives: define KPIs and ROI, track adoption, and tie outcomes to business objectives
- Secure AI across the ML lifecycle: security must be applied to data, models, and infrastructure from ingestion through deployment and management
- The Secure AI Framework (SAIF): Google's framework to secure AI systems against attacks and misuse, extending security best practices across the AI lifecycle
- Google Cloud security tools and their purpose: secure-by-design infrastructure, Identity and Access Management (IAM), Security Command Center, and workload monitoring tools
- Responsible AI principles: transparency, privacy, fairness, accountability, and explainability — and why each matters in business
- Privacy practices: understanding privacy risks and using data anonymization and pseudonymization to protect sensitive data
- The implications of data quality, bias, and fairness on AI outcomes, and the importance of accountability and explainability in AI systems
Common Exam Traps
Responsible AI is broader than bias — it spans transparency, privacy, fairness, accountability, and explainability. Scenario questions ask which principle applies
SAIF is a security framework (protecting AI from attacks/misuse), while Responsible AI is about ethical/fair use. Do not use them interchangeably
Anonymization removes identifying data irreversibly; pseudonymization replaces identifiers with reversible tokens. Both are privacy techniques but they are not identical
Explainability (understanding why a model produced an output) is distinct from transparency (being open about how and when AI is used). The exam separates these principles
Measuring gen AI impact means tying to business KPIs and ROI, not just technical metrics like accuracy — this is a leadership exam
Products and Concepts You Must Not Confuse
These pairs appear on nearly every exam. Learn the difference and you'll avoid the most common traps.
Top Mistakes to Avoid
Studying this like a technical Vertex AI exam — it is a business/leadership exam that tests conceptual understanding and product recognition, not implementation or coding
Confusing the four Gemini surfaces: the consumer Gemini app, Gemini Advanced (with Gems), Gemini for Workspace (productivity apps), and Gemini Enterprise (search + custom agents)
Mixing up prompt engineering (change the input), fine-tuning (change the model's behavior/style), and grounding/RAG (give the model access to trusted facts) — each solves a different problem
Reaching for fine-tuning when the real problem is hallucinations or outdated answers — those call for grounding/RAG, not retraining
Confusing Conversational Agents (customer-facing) with Agent Assist (helps human agents) within the Customer Engagement Suite
Miscounting prompt examples: zero-shot has none, one-shot has exactly one, few-shot has several — the exam gives scenarios and expects you to name the technique
Treating SAIF (a security framework) and Responsible AI (ethics/fairness principles) as the same thing — they are complementary but distinct
Forgetting that Responsible AI spans five principles (transparency, privacy, fairness, accountability, explainability), not just bias mitigation
Confusing Agent Studio (build/deploy enterprise agents) with Google AI Studio (quick prototyping with Gemini)
Assuming higher temperature is always better — for factual, consistent output you want low temperature, not high
Exam-Ready Checklist
Can explain the four exam domains and their weights (30%, 35%, 20%, 15%) and allocate study time accordingly
Know the core vocabulary cold: AI vs ML vs generative AI, foundation models, LLMs, multimodal models, diffusion models, prompt engineering vs prompt tuning
Can name Google's foundation models and their strengths: Gemini (multimodal flagship), Gemma (open/lightweight), Imagen (image), Veo (video)
Understand the five gen AI landscape layers (infrastructure, models, platforms, agents, applications) and the AI Hypercomputer / TPU advantage
Can distinguish the Gemini surfaces (app, Advanced/Gems, Workspace, Enterprise) and the Customer Engagement Suite components (Conversational Agents, Agent Assist, CCaaS)
Know the Agent Platform building blocks: Model Garden, Agent Search, AutoML, RAG offerings, agent tooling (extensions, functions, data stores, plugins), and the pre-built AI APIs
Can match a foundation model limitation to the right mitigation: grounding/RAG for hallucinations and knowledge cutoff, fine-tuning for style, HITL for high-stakes decisions
Know all prompt engineering techniques (zero/one/few-shot, role, prompt chaining, chain-of-thought, ReAct) and the three grounding data sources (first-party, third-party, world)
Understand sampling parameters (temperature, top-p, token count, safety settings) and their effect on output
Can explain SAIF, the Google Cloud security tools (IAM, Security Command Center, secure-by-design), and the five Responsible AI principles
Know how a leader chooses a solution (business requirements + technical constraints) and measures impact (KPIs and ROI, not just accuracy)
Scored 85%+ on at least two full practice exams before scheduling the real exam
Recommended Resources
Free & Official Resources
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These are recommended if you prefer a structured learning path. They can save time but are not required to pass.