Generative AI Leader Exam Guide

A domain-by-domain Google Cloud Generative AI Leader study plan covering gen AI fundamentals, Google Cloud offerings, model-output techniques, and business strategy.

Exam Information

90 minutes

Duration

50–60 questions

Questions

Pass/fail (no published score)

Passing Score

$99 USD

Exam Cost

Recommended Study Plan

Week 1

Fundamentals of Gen AI

27% of exam

10-12 hours

Focus: Generative AI predicts the next token, so it does not look up facts unless it is grounded in trusted data
What generative AI is and how it differs from traditional ML
Large language models (LLMs): training, inference, tokens, and context windows
Foundation models and their capabilities (text, image, code, multimodal)
Prompt engineering techniques: zero-shot, few-shot, chain-of-thought
Retrieval-augmented generation (RAG) and grounding
Fine-tuning and model customization approaches
Embeddings and vector databases
AI agents and agentic workflows
Gen AI limitations: hallucinations, bias, data freshness
Responsible AI principles: fairness, interpretability, privacy, security

Recommended Resources:

Google Cloud official objectives

/certifications/google-cloud/generative-ai-leader/notes#fundamentals-of-gen-ai

/certifications/google-cloud/generative-ai-leader/practice

/certifications/google-cloud/generative-ai-leader/study-guide

Week 2

Google Cloud Gen AI Offerings

27% of exam

10-12 hours

Focus: Use AI Studio to prototype quickly and Vertex AI when you need enterprise security, governance, and MLOps
Vertex AI platform: Model Garden, Vertex AI Studio, Gemini API
Gemini model family (Gemini 1.5 Pro, Flash, Nano) and their use cases
Google Workspace AI features: Gemini for Workspace
Google AI products: Search AI, Ads AI, Maps AI
Vertex AI Agent Builder and gen AI agents
Google Cloud AI infrastructure: TPUs, GPUs, Cloud Storage for AI
NotebookLM and other Google AI tools
Grounding with Google Search in Vertex AI
Imagen for image generation and editing
Chirp for speech and audio generation

Recommended Resources:

Google Cloud official objectives

/certifications/google-cloud/generative-ai-leader/notes#google-cloud-gen-ai-offerings

/certifications/google-cloud/generative-ai-leader/practice

/certifications/google-cloud/generative-ai-leader/study-guide

Week 3

Improving Gen AI Output

23% of exam

6-8 hours

Focus: Work the levers in order: prompt engineering first, then grounding, and fine-tuning only when it is justified
Prompt design best practices: clarity, context, examples, formatting
System instructions and persona configuration
Temperature, top-P, top-K, and other inference parameters
RAG pipeline design: chunking, embedding, retrieval, generation
Fine-tuning vs. RAG vs. prompt engineering trade-offs
Evaluation metrics: BLEU, ROUGE, human evaluation, model-based evaluation
Grounding to reduce hallucinations
Output safety filters and content moderation
Citation and attribution in gen AI responses
Iterative prompt improvement and evaluation loops

Recommended Resources:

Google Cloud official objectives

/certifications/google-cloud/generative-ai-leader/notes#techniques-to-improve-gen-ai-output

/certifications/google-cloud/generative-ai-leader/practice

/certifications/google-cloud/generative-ai-leader/study-guide

Week 4

Business Strategies for Gen AI

23% of exam

6-8 hours

Focus: Prioritize use cases with high volume, language-heavy work, and an existing measurable baseline
Identifying gen AI use cases across business functions
Build vs. buy vs. customize decision framework
ROI and value measurement for gen AI initiatives
Data readiness and data governance for gen AI
Risk management: IP, privacy, compliance, hallucination risks
Change management and employee adoption strategies
AI governance frameworks and responsible AI policies
Pilot to production: scaling gen AI solutions
Cost optimization for gen AI workloads
Measuring gen AI success and KPIs

Recommended Resources:

Google Cloud official objectives

/certifications/google-cloud/generative-ai-leader/notes#business-strategies-for-gen-ai

/certifications/google-cloud/generative-ai-leader/practice

/certifications/google-cloud/generative-ai-leader/study-guide

Week 5

Final Review & Practice Exams

All domains

8-10 hours

Complete all three full-length practice exams
Review incorrect answers and weak domains
Revisit flashcards for key terms
Timed review of highest-weight domains

Recommended Resources:

Practice exams

Flashcards

Study notes PDF

Exam Tips & Strategies

Think Business, Not Engineering

This is a leader-level exam — favour answers about business value, adoption strategy, and governance over low-level implementation detail.

Keep a Steady Pace

You have 90 minutes for roughly 50–60 questions. Read each scenario once, decide, and flag only when two options genuinely tie.

Map Google Cloud Gen AI Offerings

Know which product fits each need — Vertex AI, Gemini, and Agent Builder for building; Google Workspace and Gemini for Google Cloud for productivity.

Ground Answers in Responsible AI

Prefer options that address grounding, safety filters, and responsible-AI principles when a scenario raises hallucination or misuse risk.

Key Topics to Master

What generative AI is and how it differs from traditional ML

Large language models (LLMs): training, inference, tokens, and context windows

Vertex AI platform: Model Garden, Vertex AI Studio, Gemini API

Gemini model family (Gemini 1.5 Pro, Flash, Nano) and their use cases

Prompt design best practices: clarity, context, examples, formatting

System instructions and persona configuration

Identifying gen AI use cases across business functions

Build vs. buy vs. customize decision framework

For the most up-to-date exam information, visit the official exam guide.

View Official Exam Guide

Recommended Study Plan

Week 1

Fundamentals of Gen AI

27% of exam

10-12 hours

Focus: Generative AI predicts the next token, so it does not look up facts unless it is grounded in trusted data
What generative AI is and how it differs from traditional ML
Large language models (LLMs): training, inference, tokens, and context windows
Foundation models and their capabilities (text, image, code, multimodal)
Prompt engineering techniques: zero-shot, few-shot, chain-of-thought
Retrieval-augmented generation (RAG) and grounding
Fine-tuning and model customization approaches
Embeddings and vector databases
AI agents and agentic workflows
Gen AI limitations: hallucinations, bias, data freshness
Responsible AI principles: fairness, interpretability, privacy, security

Recommended Resources:

Google Cloud official objectives

/certifications/google-cloud/generative-ai-leader/notes#fundamentals-of-gen-ai

/certifications/google-cloud/generative-ai-leader/practice

/certifications/google-cloud/generative-ai-leader/study-guide

Week 2

Google Cloud Gen AI Offerings

27% of exam

10-12 hours

Focus: Use AI Studio to prototype quickly and Vertex AI when you need enterprise security, governance, and MLOps
Vertex AI platform: Model Garden, Vertex AI Studio, Gemini API
Gemini model family (Gemini 1.5 Pro, Flash, Nano) and their use cases
Google Workspace AI features: Gemini for Workspace
Google AI products: Search AI, Ads AI, Maps AI
Vertex AI Agent Builder and gen AI agents
Google Cloud AI infrastructure: TPUs, GPUs, Cloud Storage for AI
NotebookLM and other Google AI tools
Grounding with Google Search in Vertex AI
Imagen for image generation and editing
Chirp for speech and audio generation

Recommended Resources:

Google Cloud official objectives

/certifications/google-cloud/generative-ai-leader/notes#google-cloud-gen-ai-offerings

/certifications/google-cloud/generative-ai-leader/practice

/certifications/google-cloud/generative-ai-leader/study-guide

Week 3

Improving Gen AI Output

23% of exam

6-8 hours

Focus: Work the levers in order: prompt engineering first, then grounding, and fine-tuning only when it is justified
Prompt design best practices: clarity, context, examples, formatting
System instructions and persona configuration
Temperature, top-P, top-K, and other inference parameters
RAG pipeline design: chunking, embedding, retrieval, generation
Fine-tuning vs. RAG vs. prompt engineering trade-offs
Evaluation metrics: BLEU, ROUGE, human evaluation, model-based evaluation
Grounding to reduce hallucinations
Output safety filters and content moderation
Citation and attribution in gen AI responses
Iterative prompt improvement and evaluation loops

Recommended Resources:

Google Cloud official objectives

/certifications/google-cloud/generative-ai-leader/notes#techniques-to-improve-gen-ai-output

/certifications/google-cloud/generative-ai-leader/practice

/certifications/google-cloud/generative-ai-leader/study-guide

Week 4

Business Strategies for Gen AI

23% of exam

6-8 hours

Focus: Prioritize use cases with high volume, language-heavy work, and an existing measurable baseline
Identifying gen AI use cases across business functions
Build vs. buy vs. customize decision framework
ROI and value measurement for gen AI initiatives
Data readiness and data governance for gen AI
Risk management: IP, privacy, compliance, hallucination risks
Change management and employee adoption strategies
AI governance frameworks and responsible AI policies
Pilot to production: scaling gen AI solutions
Cost optimization for gen AI workloads
Measuring gen AI success and KPIs

Recommended Resources:

Google Cloud official objectives

/certifications/google-cloud/generative-ai-leader/notes#business-strategies-for-gen-ai

/certifications/google-cloud/generative-ai-leader/practice

/certifications/google-cloud/generative-ai-leader/study-guide

Week 5

Final Review & Practice Exams

All domains

8-10 hours

Complete all three full-length practice exams
Review incorrect answers and weak domains
Revisit flashcards for key terms
Timed review of highest-weight domains

Recommended Resources:

Practice exams

Flashcards

Study notes PDF

Exam Tips & Strategies

Think Business, Not Engineering

This is a leader-level exam — favour answers about business value, adoption strategy, and governance over low-level implementation detail.

Keep a Steady Pace

You have 90 minutes for roughly 50–60 questions. Read each scenario once, decide, and flag only when two options genuinely tie.

Map Google Cloud Gen AI Offerings

Know which product fits each need — Vertex AI, Gemini, and Agent Builder for building; Google Workspace and Gemini for Google Cloud for productivity.

Ground Answers in Responsible AI

Prefer options that address grounding, safety filters, and responsible-AI principles when a scenario raises hallucination or misuse risk.

Key Topics to Master

What generative AI is and how it differs from traditional ML

Large language models (LLMs): training, inference, tokens, and context windows

Vertex AI platform: Model Garden, Vertex AI Studio, Gemini API

Gemini model family (Gemini 1.5 Pro, Flash, Nano) and their use cases

Prompt design best practices: clarity, context, examples, formatting

System instructions and persona configuration

Identifying gen AI use cases across business functions

Build vs. buy vs. customize decision framework