Google Cloud Gen AI Leader: Exam Mastery - Opus Max

What's inside

1. Level 1: Exam Blueprint: Format, Weightings, TacticsFree
   Decode the Google Cloud Generative AI Leader exam structure — 90 minutes, 50-60 multiple-choice items, four weighted sections — and the business-level lens the questions are written from.
2. Level 2: AI, ML, Deep Learning and NLPFree
   Build the core vocabulary hierarchy that every other exam term nests inside, and learn to place NLP correctly within it.
3. Level 3: Supervised, Unsupervised, Reinforcement Learning
   Distinguish the three learning paradigms by the data they require and the business problems each solves.
4. Level 4: Structured, Unstructured, Labelled Data
   Classify enterprise data along the two axes the exam tests — structure and labelling — and connect each type to a gen AI opportunity.
5. Level 5: Data Quality: The Foundation of Output
   Assess data against the quality dimensions that determine model performance, and recognise the failure mode each dimension prevents.
6. Level 6: How Generative AI Works: Tokens and Prediction
   Contrast predictive/discriminative AI with generative AI and explain, at business level, how an LLM produces text one token at a time.
7. Level 7: Foundation Models and Multimodality
   Define the foundation model concept — trained broadly, adapted many ways — and what makes a model multimodal.
8. Level 8: Diffusion Models for Images and Video
   Explain how diffusion models generate images and video through denoising, and when they are the right model class.
9. Level 9: Choosing a Foundation Model
   Apply the six selection criteria — modality, context window, security, cost, performance and fine-tuning support — to pick a model for a business scenario.
10. Level 10: Prompt Engineering vs Prompt Tuning vs Fine-Tuning
    Separate the three customisation approaches by what changes, who does it, and what it costs.
11. Level 11: Google's Model Family: Gemini, Gemma, Imagen, Veo
    Map each first-party Google model to its modality, licensing model and best-fit use case so you never confuse them under time pressure.
12. Level 12: The Gen AI Layer Stack
    Learn the five-layer architecture — infrastructure, models, platforms, agents, applications — and place any Google product on the correct layer.
13. Level 13: Use Case Patterns: Create, Summarise, Discover, Automate
    Classify gen AI opportunities into the four canonical business patterns and identify the value each unlocks.
14. Level 14: ML Lifecycle Part 1: Data Stages and Tooling
    Walk the data-side lifecycle stages — ingestion, storage, preparation, feature engineering — and name the Google Cloud tool for each.
15. Level 15: ML Lifecycle Part 2: Train, Deploy, Manage
    Complete the lifecycle through training, evaluation, deployment and ongoing management, mapping each stage to its Vertex AI capability.
16. Level 16: Google's AI-First Approach
    Explain what 'AI-first' means at Google — research-to-product pipeline and full-stack ownership from silicon to application.
17. Level 17: Enterprise-Ready Platform and Democratised Development
    Learn the enterprise-readiness pillars — model choice, openness, governance, no lock-in — and how Google lowers the skill barrier from no-code to custom code.
18. Level 18: AI Hypercomputer, TPUs and GPUs
    Understand the AI-optimised infrastructure layer and when a workload calls for TPUs versus GPUs.
19. Level 19: Data Control, Residency and Privacy Commitments
    Explain Google Cloud's data governance commitments — customer data ownership, no training on your data, residency and sovereignty controls.
20. Level 20: Gemini App, Gemini Advanced and Gems
    Distinguish the individual-productivity Gemini tiers and learn what Gems add for repeatable personal workflows.
21. Level 21: Gemini for Workspace
    Learn how Gemini is embedded across Gmail, Docs, Sheets, Slides, Meet and Vids, and which productivity scenarios it is the correct answer for.
22. Level 22: Gemini Enterprise: The Agentic Front Door
    Understand Gemini Enterprise as the governed enterprise platform where employees find company knowledge and run business agents.
23. Level 23: Agent Search and Enterprise Data Connectors
    Learn how Agent Search indexes structured and unstructured enterprise content through connectors to deliver grounded, cited answers.
24. Level 24: Customer Engagement Suite and CCaaS
    Map the Customer Engagement Suite as Google's contact-centre-as-a-service architecture spanning self-service, agent support and analytics.
25. Level 25: Conversational Agents vs Assist vs Insights
    Drill the three most-confused customer engagement products by who they serve: the customer, the human agent, or the manager.
26. Level 26: Agent Platform and Model Garden
    Explore the Vertex AI-based Agent Platform and Model Garden as the developer path for building, deploying and orchestrating custom agents.
27. Level 27: Democratised Development: AutoML to Custom Code
    Position AutoML, low-code agent builders and full custom training on a single spectrum of control versus effort and skills required.
28. Level 28: Agent Tooling: Extensions, Functions, Data Stores
    Learn the four ways an agent reaches outside the model — extensions, function calling, data stores and plugins — and who executes each call.
29. Level 29: Pre-Built AI APIs and Studio Showdown
    Cover the task-specific pre-built AI APIs and settle the Agent Studio versus Google AI Studio distinction that closes the portfolio phase.
30. Level 30: Diagnosing Model Limitations
    Identify the four examinable failure modes of generative models — knowledge cutoff, bias, hallucination and edge cases — from symptom descriptions.
31. Level 31: Grounding and RAG Offerings
    Learn grounding as the fix for stale and invented answers, and the Google Cloud RAG offerings that implement it.
32. Level 32: Prompt Engineering and Shot Counts
    Build well-structured prompts and apply zero-shot, one-shot and few-shot patterns to control format and quality.
33. Level 33: Role Prompting and Prompt Chaining
    Use persona instructions to set tone and expertise, and decompose complex work into chained prompt steps.
34. Level 34: Chain-of-Thought and ReAct
    Apply reasoning patterns that make the model think in steps or interleave reasoning with tool use.
35. Level 35: Sampling Parameters and Safety Settings
    Tune output tokens, temperature, top-p and safety filters to match deterministic or creative business requirements.
36. Level 36: Choosing Your Tuning Lever
    Compare prompt engineering, RAG, prompt/parameter-efficient tuning and full fine-tuning on cost, data, effort and the problem each actually solves.
37. Level 37: Human-in-the-Loop, Monitoring and Drift
    Keep deployed gen AI reliable with review checkpoints, evaluation metrics, feedback loops and drift detection.
38. Level 38: Choosing and Integrating a Gen AI Solution
    Apply a repeatable decision framework to select buy, extend or build and integrate the solution into existing business workflows.
39. Level 39: Measuring Impact and Proving ROI
    Define the metrics, baselines and pilot design that demonstrate business value from a gen AI deployment.
40. Level 40: SAIF: Securing the AI Lifecycle
    Use Google's Secure AI Framework to identify AI-specific threats and the controls that mitigate them across the lifecycle.
41. Level 41: Privacy by Design: Anonymisation & Pseudonymisation
    Learn how to protect personal data in gen AI workflows by distinguishing anonymisation from pseudonymisation and applying Google Cloud data-control and de-identification practices.
42. Level 42: Bias & Fairness Across the AI Lifecycle
    Trace where bias enters a gen AI system — data, labelling, model, prompt and deployment — and choose mitigations that improve fairness without over-relying on a single control.
43. Level 43: Transparency, Explainability & Accountability
    Complete the responsible AI toolkit by applying transparency artefacts, explainability tooling and clear accountability structures to gen AI deployments.
44. Level 44: Question Dissection & Timing Strategy
    Apply a repeatable four-step method for dismantling scenario questions and a per-question pacing plan for the 90-minute, 50–60 question exam.
45. Level 45: Weighted Mock Sections: All Four Domains
    Work through domain-weighted mock question sets (30/35/20/15) with rationale review to expose and close remaining knowledge gaps.
46. Level 46: Final Challenge: Full Exam Mastery Exam
    Sit a comprehensive, exam-realistic challenge spanning gen AI fundamentals, the Google Cloud portfolio, output improvement, and business strategy, security and responsible AI.

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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