Google Cloud Generative AI Leader

Learning plan

About 22 hours of focused study, split into 5 phases. Pick your weekly pace and the timeline adjusts.

Estimated finish: 3 weeks

What the exam covers

Source: official exam guide. Weights can change, so check before you start.

Before you start

• No technical background or hands-on experience required. The exam tests business-level understanding
• A personal Google account to try the Gemini app and Google AI Studio for free

1. Week 1 · 6 h

   Gen AI fundamentals and Google's models

   • Define foundation models, multimodal models, diffusion models, LLMs, prompt tuning, and supervised vs unsupervised vs reinforcement learning
   • Walk through the ML lifecycle (ingest, prepare, train, deploy, manage) and name the Google Cloud tool for each stage
   • Explain the five layers of the gen AI landscape (infrastructure, models, platforms, agents, applications) with a Google product for each
   • Match Gemini, Gemma, Imagen and Veo to use cases, and choose a model by modality, context window, cost and customisation needs

   Use: Generative AI Leader learning path (Google Cloud Skills Boost) · Generative AI Leader exam guide (PDF)

2. Weeks 1–2 · 8 h

   Google Cloud's gen AI portfolio (largest domain)

   • Tell apart the Gemini app (Gems), Gemini Enterprise (multimodal search, Gemini Notebook API, custom agents) and Gemini for Google Workspace, and say who each is for
   • Place Customer Engagement Suite products (Conversational Agents, Agent Assist, Conversational Insights, Contact Center as a Service) in a customer-experience scenario
   • Describe what Agent Platform (Model Garden, Agent Search, AutoML) offers developers, and when to use Agent Studio vs Google AI Studio
   • Name the AI-optimised infrastructure pieces (AI Hypercomputer, TPUs, GPUs) and the pre-built APIs agents use as tools (Document AI, Vision, Speech-to-Text, Translation)

   Hands-on: In Google AI Studio, give Gemini the same task three ways (zero-shot, few-shot, and with Grounding with Google Search turned on). Compare the outputs and note how temperature and output length change them.

   Use: Google AI Studio · 101 real-world gen AI use cases (Google Cloud)

3. Week 2 · 4 h

   Improving model output

   • List foundation-model limitations (knowledge cutoff, hallucination, bias, edge cases) and the fix for each (grounding, RAG, prompt engineering, fine-tuning, human in the loop)
   • Recognise zero-, one- and few-shot, role prompting, prompt chaining, chain-of-thought and ReAct
   • Distinguish grounding on first-party enterprise data, third-party data and world data, and the three Google offerings (pre-built RAG with Agent Search, RAG APIs, Grounding with Google Search)
   • Explain what temperature, top-p, token limits and safety settings do

   Use: Generative AI for Everyone (DeepLearning.AI)

4. Week 2 · 2 h

   Business strategy, secure AI and responsible AI

   • Outline the steps to adopt gen AI in an organisation and how to measure impact with KPIs
   • Explain Google's Secure AI Framework (SAIF) and the role of IAM and Security Command Center
   • Cover privacy (anonymisation vs pseudonymisation), fairness, accountability and explainability with Google's AI Principles

   Use: Secure AI Framework (SAIF) · Google AI Principles

5. Week 3 · 2 h

   Practice and review

   • Answer the official sample questions on the certification page and review each explanation
   • Re-read section 2 of the exam guide and quiz yourself on which product solves which business problem

   Use: Generative AI Leader certification page

You're ready to book when…

• Given a business problem, you can name the right Google offering (Gemini Enterprise, Workspace, Customer Engagement Suite, Agent Platform) without hesitation
• You can explain grounding vs RAG vs fine-tuning and when each is worth the cost
• You can describe SAIF and three responsible-AI risks with a mitigation for each
• You get 85% or more of the official sample questions right

Exam tips

• Section 2 (Google Cloud's offerings) carries ~35% of the exam, so product-to-use-case matching is where most points are.
• Foundational exams allow up to ten attempts a year, with at least 14 days between failed attempts.