Google Cloud Generative AI Leader (Certification) — exam guide & mock tests | explainx.ai | explainx.ai
Google Cloud · certification guide
Google Cloud Generative AI Leader
Certification
Practice for Google Cloud's Generative AI Leader certification — a business-level, non-technical exam. Mock tests mirror the official domain weights, business scenarios, and multiple-choice format for Gemini, Gemini Enterprise, grounding, RAG, prompt engineering, SAIF, and responsible AI.
Study hub mapped to Google Cloud's Generative AI Leader Exam Guide — official proctored registration stays with Google Cloud.
Google Cloud positions the Generative AI Leader credential for business-level, non-technical professionals who champion, evaluate, or approve gen AI initiatives. There are no prerequisites and no coding — the exam tests gen AI literacy and strategy about Gemini, Google Cloud offerings, grounding, and responsible AI, not engineering.
→Business leaders, managers, and decision-makers who champion or approve gen AI initiatives
→Non-technical professionals — product, marketing, operations, legal, finance — who need gen AI literacy
→People who evaluate Google Cloud's gen AI offerings for their organization rather than build them
→Anyone framing gen AI business cases, ROI, and risk — not writing production model code
→Candidates who want to speak fluently about Gemini, grounding, RAG, prompt engineering, and responsible AI
→Teams standardizing on Google Cloud who need a shared vocabulary across technical and business roles
→No prerequisites required — the exam assumes no hands-on ML or cloud engineering background
Official exam format
Summarized from Google Cloud's public Generative AI Leader exam guide — confirm on Google Cloud Certification before you schedule.
Level
Business-level, non-technical — gen AI literacy and strategy, not engineering
Format
50–60 multiple choice questions · online-proctored or onsite-proctored · no prerequisites
Duration
90 minutes · online or onsite proctored
Pass score
Not officially published — explainx practice tests use a 70% convention
Official price
$99 plus tax per attempt
Validity
3 years · English, Japanese, Spanish (Latin America), Portuguese (Brazil)
The official guide lists four weighted domains and granular task statements. explainx practice questions target these competencies — use this map to plan study time and pick focused mock tests.
Fundamentals of gen AI (~30%)
1.1 Explain the fundamentals of gen AI
· Core concepts — AI, NLP, ML, gen AI, foundation models, multimodal foundation models, diffusion models
· Prompt tuning vs prompt engineering, and large language models (LLMs) as one category of foundation model
· ML approaches — supervised, unsupervised, and reinforcement learning
· ML lifecycle stages — data ingestion, preparation, training, deployment, management — and Google Cloud tools per stage
· Choosing a foundation model — modality, context window, security, availability/reliability, cost, performance, fine-tuning, customization
· Business use cases — create, summarize, discover, automate across text/image/code/video generation, data analysis, personalization
1.2 Describe the types of data used in gen AI
· Data quality and accessibility — completeness, consistency, relevance, availability, cost, format
Six production scenarios
Practice questions are scenario-framed around realistic business gen AI decisions — contact centers, compliance and grounding, content at scale, knowledge search, data readiness, and board-level investment.
The Retail Contact-Center Rollout
A VP of Customer Experience must decide between deterministic Conversational Agents for known intents and a generative agent for open-ended questions. Success is measured by deflection rate and CSAT, and the exam-style judgment is knowing that Conversational Agents are hybrid deterministic-plus-generative, not 'just a chatbot.'
Domains: Business Strategy · Google Cloud Offerings
Healthcare Compliance & Grounding
A hospital operations director wants gen AI to summarize patient intake notes but fears hallucinated clinical facts. The leader does not build the system but needs enough conceptual grasp of grounding and RAG to ask vendors the right questions and set guardrails.
Domains: Model Output Techniques · Business Strategy
Marketing Content at Scale
A CMO scaling content production hits inconsistent brand voice and occasional factual errors. The decision is whether prompt engineering, fine-tuning, or grounding is the right (and most cost-effective) fix — with fine-tuning being the classic over-reached-for answer.
Domains: Model Output Techniques
Legal Ops Knowledge Search Rollout
A law-firm operations lead wants natural-language search across contracts and SharePoint without hiring engineers. The evaluation pits Gemini Enterprise with Agent Search (broad org search with connectors) against a narrow point solution and NotebookLM Enterprise (curated deep research).
Domains: Google Cloud Offerings
In scope on the exam
✓ Gen AI core concepts — foundation models, LLMs, multimodal and diffusion models, ML approaches
✓ The ML lifecycle and matching Google Cloud tools to each stage, at a conceptual level
✓ Data literacy — quality dimensions, structured vs unstructured, labeled vs unlabeled
✓ The gen AI landscape layers and Google's models — Gemini, Gemma, Imagen, Veo
✓ Google Cloud's prebuilt and developer gen AI offerings and their current names
Customer Engagement Suite — Agent Assist, Conversational Insights, CCaaS
NotebookLM Enterprise — curated deep-research complement to Gemini Enterprise
Cloud Vision, Speech-to-Text/Text-to-Speech, Translation, Document AI, Natural Language APIs
SAIF (Secure AI Framework), IAM, and Security Command Center for governance
Practice on explainx — live now
Independent mock exams aligned to the Generative AI Leader exam guide — not affiliated with Google Cloud's proctored certification. 452 multiple-choice questions in the bank, shuffled every attempt, instant explanations after submit.
Quick answers for candidates preparing with explainx mock exams (not Anthropic support).
Anthropic's ~301-level proctored certification for solution architects building production apps with Claude Code, the Claude Agent SDK, MCP, and structured output. The official exam has 60 multiple-choice questions in 120 minutes, four scenario frames per sitting, and a scaled pass score of 720/1000.
No. explainx.ai offers independent practice mock tests aligned to the public Foundations Exam Guide domains, task statements, and scenarios. Official registration and proctoring are through Anthropic Academy / Skilljar.
Lifetime access to all Claude Certified Architect mock tests on explainx.ai is $5 USD (one-time). Each attempt draws a shuffled subset from a bank of 1,000+ practice questions with explanations after submit.
You can browse certification and test pages without an account. Starting a timed mock test requires signing in and purchasing lifetime practice access for that program.
Eight mock tests per program including a full exam simulation, plus 1,000+ banked multiple-choice items. Programs include Claude Certified Architect (agentic architecture, MCP, Claude Code) and AWS GenAI Developer Professional (Bedrock, RAG, agents, governance).
Questions mirror the six official production scenarios: customer support agents, Claude Code development, multi-agent research, developer productivity, CI/CD with Claude Code, and structured data extraction.
Use Anthropic's Foundations Exam Guide and Academy courses for the proctored exam. Use explainx mock tests for timed MCQ practice, domain drills, and instant feedback — especially scenario-framed items and tradeoff questions.
A business-level, non-technical Google Cloud certification. The 90-minute exam has 50-60 multiple-choice questions across four domains — gen AI fundamentals, Google Cloud's gen AI offerings, techniques to improve model output, and business strategy — with no prerequisites required.
Microsoft's Associate-level certification for Azure AI engineers building generative AI apps and agents with Microsoft Foundry and Python. The 120-minute exam covers five domains with a minimum passing score of 700/1000.
· Structured vs unstructured data and why unstructured data is not inherently lower quality
· Labeled vs unlabeled data and how each supports different ML approaches
1.3 Describe the core layers of the gen AI landscape
· Infrastructure layer — compute and storage that trains and serves models
· Models layer — foundation models such as Gemini, Gemma, Imagen, and Veo
· Platforms layer — tools to build, tune, and deploy on top of models
· Agents layer — systems that reason, use tools, and take action
· Applications layer — end-user products and business workflows
1.4 Describe Google's foundation models
· Gemini — Google's flagship multimodal foundation model family
· Gemma — open, lightweight models for flexible deployment
· Imagen — text-to-image generation
· Veo — text-to-video generation
Google Cloud's gen AI offerings (~35%)
2.1 Describe Google Cloud's strengths in gen AI
· AI-first approach and an enterprise-ready AI platform — responsible, secure, private, reliable, scalable
· A comprehensive AI ecosystem and an open approach with open models
· AI-optimized infrastructure — hypercomputer, custom-designed TPUs, GPUs, and global data centers
· User control over data — security, privacy, governance, open models, pre-built and customizable solutions, agents
· Democratizing AI development — low-code/no-code, pre-trained models, and APIs
2.2 Describe Google Cloud's prebuilt gen AI offerings
· Gemini app, Gemini Advanced, and Gems for individuals and teams
Business strategies for a successful gen AI solution (~15%)
4.1 Describe the steps to implement a gen AI solution
· Types of gen AI solutions — text, image, and code generation, and personalized experiences
· Key factors — business requirements and technical constraints
· Choosing the right solution, integration steps, and measuring business impact
4.2 Describe how to secure a gen AI solution
· Security throughout the ML lifecycle
· Google's Secure AI Framework (SAIF) — six non-sequential elements, not a strict checklist
· Google Cloud security tools — secure-by-design infrastructure, IAM, Security Command Center, workload monitoring
4.3 Describe responsible AI practices
· Transparency in how gen AI systems work and are used
· Privacy — risks, anonymization, and pseudonymization
· Data quality, bias, and fairness implications
· Accountability and explainability of gen AI decisions
Manufacturing Data Readiness Audit
An operations executive discovers that maintenance logs are unstructured and unlabeled before a gen AI pilot can succeed. The leader must reason about data quality dimensions and readiness — and understand that unstructured data is usable, not automatically low quality.
Domains: Fundamentals
Board-Level AI Investment Pitch
A CFO evaluates a seven-figure gen AI initiative — build-vs-buy, an ROI framework that includes quality, speed, and risk reduction (not just cost savings), and governance through SAIF, IAM, and Security Command Center.
