Google Cloud Generative AI Leader Certification Exam Prep

What's inside

1. Level 1: The Exam and What Gen AI IsFree
   [Fundamentals of gen AI] Sets out the exam format (50-60 multiple-choice questions in 90 minutes, four weighted sections), states that this course is independent of Google and promises no pass, then places generative AI inside AI, machine learning and deep learning.
2. Level 2: Three ML ApproachesFree
   [Fundamentals of gen AI] Teaches supervised, unsupervised and reinforcement learning from first principles and what business problem each one fits.
3. Level 3: Foundation Models, LLMs and Multimodal
   [Fundamentals of gen AI] Explains what a foundation model is, how a large language model is one kind of foundation model, and what makes a model multimodal.
4. Level 4: Diffusion Models and Prompt Tuning
   [Fundamentals of gen AI] Completes the core concepts with diffusion models for image and video generation and the exam's contrast between prompt engineering and prompt tuning.
5. Level 5: ML Lifecycle Stages and Their Tools
   [Fundamentals of gen AI] Walks through the ML lifecycle stages the exam guide names (data ingestion, data preparation, model training, model deployment, model management) and the Google Cloud tool associated with each.
6. Level 6: Choosing a Foundation Model
   [Fundamentals of gen AI] Teaches the factors used to select a foundation model for a business use case: modality, context window, security, availability and reliability, cost, performance, fine-tuning and customization.
7. Level 7: Create, Summarize, Discover, Automate
   [Fundamentals of gen AI] Introduces the four categories of gen AI business use case (create, summarize, discover and automate) across text, image, code, audio and video.
8. Level 8: Data Quality for AI
   [Fundamentals of gen AI] Explains why data quality and accessibility determine AI results, using the dimensions the exam names: completeness, consistency, relevance, availability, cost and format.
9. Level 9: Data Types: Structured and Labeled
   [Fundamentals of gen AI] Distinguishes structured from unstructured data and labeled from unlabeled data, and links each to the business uses and ML approaches it supports.
10. Level 10: The Five Layers of Gen AI
    [Fundamentals of gen AI] Maps the gen AI landscape into its five layers (infrastructure, models, platforms, agents and gen AI-powered applications) and what each contributes to a business solution.
11. Level 11: Gemini and Gemma
    [Fundamentals of gen AI] Introduces Google's Gemini family of multimodal foundation models and the Gemma family of lightweight open models, and contrasts when each is the better fit.
12. Level 12: Imagen and Veo
    [Fundamentals of gen AI] Covers Imagen for image generation and Veo for video generation, then practises choosing between all four Google foundation models.
13. Level 13: Google Cloud's Three Strengths
    [Google Cloud's gen AI offerings] Explains the strengths the exam attributes to Google Cloud in gen AI: an AI-first approach, an enterprise-ready platform and an open ecosystem.
14. Level 14: AI Hypercomputer, TPUs and GPUs
    [Google Cloud's gen AI offerings] Covers the infrastructure under Google Cloud's gen AI: the AI Hypercomputer as an integrated system, and TPUs and GPUs as the accelerators that train and run models.
15. Level 15: Keeping Control of Your Data
    [Google Cloud's gen AI offerings] Explains how Google Cloud lets an organization keep control of its data when using gen AI, through security, privacy, governance and reliability commitments.
16. Level 16: Low-Code Paths to Gen AI
    [Google Cloud's gen AI offerings] Shows how Google Cloud's low-code and no-code options let business teams use and build gen AI without programming, and when a prebuilt product beats a custom build.
17. Level 17: Gemini App, Gemini Advanced and Gems
    [Google Cloud's gen AI offerings] Covers the Gemini app as Google's AI assistant, Gemini Advanced as the paid tier named in the exam guide, and Gems as customized versions of Gemini for repeated tasks.
18. Level 18: Gemini Enterprise
    [Google Cloud's gen AI offerings] Introduces Gemini Enterprise as the organization-wide product for searching company data and using agents, with the Gemini Notebook API, multimodal search and custom agents, noting any older name Google's documentation still uses.
19. Level 19: Gemini for Google Workspace
    [Google Cloud's gen AI offerings] Explains Gemini for Google Workspace as gen AI built into tools such as Gmail, Docs, Sheets, Slides and Meet, and contrasts it with the Gemini app and Gemini Enterprise.
20. Level 20: Agent Search vs Google Search
    [Google Cloud's gen AI offerings] Contrasts Agent Search (older name Vertex AI Search), which builds search over an organization's own data, with Google Search over public web information.
21. Level 21: Customer Engagement Suite: Four Products, Four Jobs
    [Google Cloud's gen AI offerings] Learn what the Customer Engagement Suite is for and tell apart its four parts: Conversational Agents (self-service virtual agents), Agent Assist (real-time help for human agents), Conversational Insights (analysis of customer conversations) and Contact Center as a Service (CCaaS, the cloud contact centre itself).
22. Level 22: Agent Platform: Model Garden and AutoML
    [Google Cloud's gen AI offerings] Learn what Agent Platform (older name Vertex AI) is as Google Cloud's unified platform for building with AI, and how Model Garden (a catalogue for discovering and choosing models) differs from AutoML (training a custom model on your own data without writing code).
23. Level 23: Agent Platform: RAG APIs and Custom Agents
    [Google Cloud's gen AI offerings] Learn how Agent Platform lets a business connect models to its own data through RAG APIs and build custom agents that reason, use tools and complete multi-step tasks.
24. Level 24: Agent Tools: Extensions, Functions, Data Stores, Plugins
    [Google Cloud's gen AI offerings] Learn the four kinds of agent tooling the exam guide names (extensions, functions, data stores and plugins) and what each lets an agent do beyond generating text.
25. Level 25: Cloud Services Behind Agents: Storage, Databases, Run
    [Google Cloud's gen AI offerings] Learn the supporting Google Cloud services an agent relies on: Cloud Storage for files and unstructured data, databases for structured records, Cloud Functions for small event-driven pieces of code, and Cloud Run for running containerised applications without managing servers.
26. Level 26: The Pre-Built AI APIs: Speech, Language, Documents, Vision
    [Google Cloud's gen AI offerings] Learn the eight pre-trained Google Cloud AI APIs the exam guide names and the single job each does: Speech-to-Text, Text-to-Speech, Translation, Document Translation, Document AI, Vision, Video Intelligence and Natural Language.
27. Level 27: Agent Studio vs Google AI Studio
    [Google Cloud's gen AI offerings] Learn when to use Agent Studio (older name Vertex AI Studio), the enterprise prompt-and-build workspace inside Agent Platform on Google Cloud, and when to use Google AI Studio, the lightweight tool for quickly trying Gemini models with an API key.
28. Level 28: Where Foundation Models Fall Short
    [Techniques to improve gen AI model output] Learn the built-in limits of foundation models that the later techniques exist to fix: dependence on training data, the knowledge cutoff, bias, hallucinations and poor handling of edge cases.
29. Level 29: Grounding: First-Party, Third-Party and World Data
    [Techniques to improve gen AI model output] Learn what grounding is (tying a model's answers to verifiable sources) and the three kinds of source: first-party enterprise data, third-party data and world data, such as grounding with Google Search.
30. Level 30: Retrieval-Augmented Generation (RAG)
    [Techniques to improve gen AI model output] Learn how RAG works step by step: retrieve relevant content, add it to the prompt, then generate the answer. Learn how Google Cloud offers this through Agent Search (older name Vertex AI Search) and Agent Platform's RAG APIs.
31. Level 31: Fine-Tuning and Choosing the Right Fix
    [Techniques to improve gen AI model output] Learn what fine-tuning is (further training a foundation model on your own examples to specialise its behaviour) and how to choose between prompt engineering, grounding or RAG, and fine-tuning by cost, effort and the problem at hand.
32. Level 32: Keeping Output Reliable: HITL, Monitoring and Drift
    [Techniques to improve gen AI model output] Learn how output quality is protected after launch: human in the loop (HITL) review for high-stakes decisions, continuous monitoring of model performance, detecting drift as real-world data changes, and Feature Store for managing and serving consistent model features.
33. Level 33: Prompting by Example: Zero-, One-, Few-Shot and Role
    [Techniques to improve gen AI model output] Learn the foundational prompting techniques: zero-shot (no examples), one-shot (one example), few-shot (several examples) and role prompting (assigning the model a persona or point of view).
34. Level 34: Advanced Prompting: Chaining, Chain-of-Thought, ReAct
    [Techniques to improve gen AI model output] Learn three techniques for complex tasks: prompt chaining (splitting work into a sequence of prompts), chain-of-thought (asking the model to reason step by step) and ReAct (reason and act, alternating reasoning with tool use).
35. Level 35: Sampling Parameters and Safety Settings
    [Techniques to improve gen AI model output] Learn how model settings shape output without changing the prompt: sampling parameters such as temperature, top-p and output length (token count) control randomness and size, while safety settings filter harmful content.
36. Level 36: Choosing the Right Gen AI Solution for a Need
    [Business strategies for a successful gen AI solution] Learn how to start from the business need rather than the technology, weighing requirements, available data, skills, cost, time and risk to decide between a ready-made product, a low-code build and a custom solution.
37. Level 37: Integrating Gen AI Into the Organisation
    [Business strategies for a successful gen AI solution] Learn the steps for bringing a gen AI solution into an organisation: connecting it to existing systems and workflows, aligning stakeholders, preparing people for change and scaling from pilot to production.
38. Level 38: Measuring the Impact of a Gen AI Initiative
    [Business strategies for a successful gen AI solution] Learn how to judge whether a gen AI solution is working by defining success up front and tracking business measures such as return on investment, productivity, cost, quality, adoption and customer satisfaction.
39. Level 39: Secure AI: SAIF, IAM and Security Command Center
    [Business strategies for a successful gen AI solution] Learn why security must cover the whole ML lifecycle and what each named item is for: Google's Secure AI Framework (SAIF) as the guiding framework, Identity and Access Management (IAM) for controlling who can do what, and Security Command Center for visibility into risks and threats.
40. Level 40: Responsible AI: Transparency and Privacy
    [Business strategies for a successful gen AI solution] Learn the first half of responsible AI: being transparent about how and when AI is used, protecting personal data, and the difference between anonymization (identity removed irreversibly) and pseudonymization (identifiers replaced, so re-identification is possible with a key).
41. Level 41: Responsible AI: Bias, Fairness, Accountability, Explainability
    [Business strategies for a successful gen AI solution] Completes responsible AI by teaching, at business level, how bias enters a gen AI system and what fairness requires, who is accountable for a model's outputs, and why explainability matters, each contrasted with the transparency and privacy ideas already covered so the four terms can be told apart in a scenario.
42. Level 42: Timed Mock Exam: 60 Questions in 90 Minutes Timed mock
    [All sections] A timed mock exam of 60 scenario-based multiple-choice practice questions in 90 minutes with a 70% pass mark, testing all four exam guide sections in proportion to their weightings (fundamentals ~30%, Google Cloud's offerings ~35%, techniques to improve output ~20%, business strategies ~15%); these are practice questions written for this course, which is independent of Google, not real exam questions, and passing the mock does not guarantee a pass on the real exam.

Access

The first 2 levels are free with a free account. Every level, the podcast edition and the AI tutor come with All Access at £4.99/month or any Creator plan — see pricing.

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