Generative AI Leader — Quick review

Reviewed 10 October 2026. Use the linked official exam guide for your exam version. These are condensed revision notes; the topic pages provide worked distinctions and more recall practice. Google’s 2026 guides use newer Gemini Enterprise Agent Platform names while some APIs and documentation still use Vertex AI.

Memory hook: Select the task, improve the evidence, measure the outcome, govern the action.

1. AI fundamentals — 1.1–1.4

AI includes ML; supervised learning uses labels, unsupervised learning finds structure, reinforcement learning learns from rewards. Generative models create content; predictive models estimate a target. LLMs process tokens; multimodal models also work with image/audio/video; diffusion models generate through denoising.
A foundation model is broadly pretrained. Choose modality, task quality, context capacity, cost, latency, privacy, availability and customization together. Gemini is multimodal, Gemma provides open models, Imagen generates images, Veo generates video. Open weights retain licensing and hosting responsibilities.
Lifecycle: ingest → clean/prepare → train or configure → evaluate → deploy → monitor. Structured tables, semi-structured records and unstructured media require different extraction. Labels, completeness, consistency, relevance and access rights matter more than raw volume.
Infrastructure supplies compute; models learned capability; platforms development/operations; agents tool-driven workflows; applications user outcomes. GPUs/TPUs and AI Hypercomputer accelerate suitable workloads; infrastructure alone cannot fix unsuitable data.

2. Match Google offerings to the workflow — 2.1–2.5

Business need	Product family / distinction
Personal conversational assistance or reusable task instructions	Gemini app and supported Gems; check the actual account and data terms.
Writing, spreadsheets and meetings in everyday work	Gemini for Workspace, governed by edition and administrator settings.
Search and agents over company knowledge	Gemini Enterprise, authorized connectors and Agent Search.
Build and operate custom models/agents	Gemini Enterprise Agent Platform, Model Garden and supported AutoML/agent tools.
Rapid Gemini model/prompt prototyping	Google AI Studio; distinguish it from enterprise deployment and governance.
Customer self-service, human-agent assistance, conversation analysis	Conversational Agents, Agent Assist and Conversational Insights in the customer-experience portfolio.
Known speech/translation/document/vision task	A suitable pretrained API rather than training the general capability again.

Agent Studio/design tools build agent experiences; an agent’s functions/extensions/MCP integrations provide tool access. Function calling creates structured requests; authorized application code executes them. Search retrieves evidence, while an order-update tool changes state. Preserve user/document access controls through every connector.

3. Improve output — 3.1–3.3

State task, audience, constraints, evidence and expected output. Zero-shot gives no example, one-shot one example, few-shot several; role prompting sets perspective; prompt chaining splits dependent tasks into stages.
Chain-of-thought-style prompting encourages intermediate reasoning; ReAct combines reasoning with tool actions/observations. Judge verifiable outcomes and authorized actions, not how convincing a reasoning narrative sounds.
Prompt engineering changes instructions. Prompt tuning learns a small task-specific prompt representation where supported; fine-tuning trains model behavior. Neither is simply attaching current reference documents.
Grounding adds evidence from enterprise, third-party or world information. RAG retrieves current passages; Google Search grounding supports current world information; managed Agent Search/RAG APIs reduce retrieval plumbing. Evaluate retrieval quality and final-answer faithfulness separately.
Temperature changes sampling variability; top-p limits candidates by cumulative probability; maximum output tokens limit response size; safety settings control supported filtering. Lower temperature does not guarantee factual truth. Context capacity is not a promise to use every supplied fact.
Mitigate hallucination, stale knowledge, bias and injection using appropriate retrieval, clear prompts, evaluation, human review and controlled tools. Version models/prompts, observe KPIs and test upgrades before broad rollout.

4. Adoption, secure AI and responsibility — 4.1–4.3

Prioritize value × feasibility × data readiness × risk. Establish a baseline, choose a bounded pilot, include business/users/engineering/security stakeholders, measure results, train users, then scale with ownership. Measure task completion, time saved, error cost, satisfaction, latency and total cost—not only model benchmarks.

Google’s Secure AI Framework (SAIF) frames lifecycle defense across systems, data and models. Use IAM, isolation, encryption, monitoring and SCC alongside AI-specific controls. Pseudonymization substitutes identifiers and can remain reversible/linkable; anonymization aims to prevent re-identification. Neither should be claimed merely because names were removed. Responsible AI includes privacy, fairness, safety, transparency, accountability and proportionate human oversight.

Traps to catch

A demo is not a business case; a larger model is not always better.
Fine-tuning is usually not the first answer to rapidly changing policy facts.
Consumer and enterprise product terms, licenses and data handling are not interchangeable.

Last-pass self-check

1. The business needs current policies, not a new writing style. Which intervention?

Permission-aware RAG with fresh sources and evidence checks, before fine-tuning.

2. What separates an agent tool call from a generated suggestion?

Application code validates and authorizes an actual external operation; model text alone is not execution authority.

3. When is recall more useful than a flashy model benchmark?

Whenever you need evidence about your own representative business tasks, user groups and failure cases.

4. Does lower temperature eliminate hallucination?

No. It changes sampling behavior; evaluate factual grounding and handle missing evidence explicitly.

5. Which numbers should prove a pilot is useful?

Business outcomes against the baseline plus quality, safety, latency, adoption and total operating cost.

Sources

Every topic at a glance

Open any topic to revisit its essential facts, decisions and exam traps. Use the full topic for active recall and supporting references.

01 · AI fundamentals and model selection

Memory hook: Task, data, model, measure.

Must remember

AI is the umbrella; ML learns patterns; generative AI produces new content. Supervised learning uses labelled examples, unsupervised learning discovers structure, and reinforcement learning learns from rewards.
An LLM handles language tokens; multimodal models also consume or produce images, audio or video. Diffusion models progressively denoise a representation to generate content.
Choose a model by modality, quality on representative tasks, context capacity, latency, cost, availability, deployment constraints and customization needs. A larger model is not automatically the best business choice.
Gemini is Google's multimodal model family; Gemma provides open models; Imagen targets images; Veo targets video. Open model weights still have licensing and operational obligations.
Structured tables, semi-structured JSON and unstructured documents need different preparation. Check completeness, accuracy, consent, relevance, freshness and accessibility before training or retrieval.
Remember the stack: infrastructure supplies compute; models supply learned capability; platforms manage development; agents coordinate actions; applications deliver a user outcome. Ingest, prepare, train, deploy, monitor is a lifecycle, not a one-time launch.

Choose under exam pressure

Requirement	Choice and reason
Classify labelled support tickets	Supervised learning; assess errors by class.
Produce a short product video	A video-capable model such as Veo, with rights and safety review.

Traps

A context window is capacity, not a guarantee that every fact will be used correctly.
An open model does not eliminate hosting costs or make training data unrestricted.

Practise this topic

02 · Google AI products and agents

Memory hook: Buy the workflow; build the differentiator.

Must remember

Gemini in Workspace supports everyday writing, meetings and analysis; the Gemini app provides conversational assistance. Managed enterprise offerings add organizational access and administration controls; do not assume consumer and enterprise terms match.
Gemini Enterprise brings enterprise search and agent experiences to organizational information. Notebook-style experiences ground work in selected sources; connectors still need identity-aware access and freshness.
Customer experience agents combine conversation, knowledge retrieval, escalation and business-system actions. A deterministic flow suits strict transactions; generative conversation suits flexible requests.
Code assistants help explain, generate and review code. Human review, tests, dependency scanning and secret protection remain necessary; generated code is not automatically production-ready.
The managed AI platform supplies model access, development, evaluation and deployment. Model Garden offers model choices; managed APIs avoid building every capability from scratch. TPU/GPU infrastructure helps demanding training and inference workloads.
An agent combines a model, instructions, tools, context and an execution loop. Function calling proposes structured tool arguments; application code authorizes and executes them. Search grounds answers, while tools can change external state.

Review details

For customer experience, Conversational Agents supports customer-facing conversation, Agent Assist helps a human support representative, and Conversational Insights analyzes conversations for trends. Do not choose an analytics tool when the requirement is live transaction handling. Google AI Studio is a model/prompt prototyping environment; enterprise Agent Studio/designer and platform capabilities address agent construction, deployment and governance under their supported models.

Speech-to-Text transcribes; Text-to-Speech synthesizes audio; Translation/Document Translation handles language; Document AI extracts structured meaning from documents; Vision/Video Intelligence interprets images/video; Natural Language handles supported text analysis. Select an existing API when it meets the task before commissioning new model training.

Choose under exam pressure

Requirement	Choice and reason
Standard meeting summaries	Workspace AI capabilities before a bespoke model pipeline.
An assistant must update an order	Authorized tool integration with validation, audit and appropriate approval.

Traps

Adding a connector does not authorize every employee to every connected document.
A chatbot that only returns text is not necessarily an autonomous agent.

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03 · Prompting and grounded answers

Memory hook: Instructions guide; evidence grounds; tests decide.

Must remember

Write the task, audience, context, constraints and output format explicitly. Separate instructions from supplied documents so quoted text is not mistaken for authority.
Zero-shot prompts specify a task without examples; few-shot prompts demonstrate desired patterns. Structured output helps downstream validation but does not establish factual correctness.
Decompose a complex task into verifiable stages. Request concise explanations and evidence where useful; do not treat an apparent reasoning narrative as proof.
Grounding connects generation to trustworthy information. RAG retrieves selected passages at request time; search grounding helps with current information; database tools answer structured queries.
Prompt engineering changes input instructions. Fine-tuning changes model behavior through additional training. RAG supplies reference information without retraining the model for each document update.
Hallucination, bias, stale knowledge, inconsistent answers and prompt injection are different failure modes. Evaluate representative cases and adversarial cases; use citations, abstention, filters and human review where appropriate.

Review details

Sampling controls are different knobs: temperature changes randomness, top-p restricts candidates by cumulative probability, and maximum output tokens limits response size. The model must support the chosen parameters. A lower temperature does not establish truth or make untrusted instructions safe.

One-shot supplies one example. Role prompting sets a perspective; prompt chaining divides tasks into steps; ReAct combines reasoning with actions/observations. Chain-of-thought-style prompts can elicit intermediate reasoning, but judge the verifiable result rather than apparent thought quality. Prompt tuning learns a compact prompt representation for a supported model/task, while fine-tuning adapts supported model behavior through training; neither means simply writing a better user prompt.

Choose under exam pressure

Requirement	Choice and reason
Policies change every week	Permission-aware RAG with document refresh and citation checks.
Responses have inconsistent format	Clear schema, examples, validation and measured prompt revisions.

Traps

Lower temperature does not guarantee truth.
Fine-tuning is not the default solution for frequently changing private facts.

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04 · Business adoption and responsible AI

Memory hook: Value, evidence, ownership, rollout.

Must remember

Prioritize opportunities by measurable value, feasibility, data readiness, risk and adoption effort. Start with a bounded pilot and an existing baseline rather than an organization-wide autonomous rollout.
Measure business outcomes alongside model quality: time saved, resolution rate, error cost, user satisfaction, latency and total operating cost. Include integration, review, change management and monitoring in ROI.
Build a cross-functional team with business owners, users, data specialists, engineering, security and legal expertise. Train users and define escalation paths before scaling.
Secure AI covers the model supply chain, data, infrastructure, identities and runtime interactions. Apply least privilege, encryption, logging, tool authorization and defenses against injection and exfiltration.
Responsible AI requires fairness, transparency, privacy, accountability, safety and human oversight proportionate to impact. Evaluate different user groups and document known limitations.
An adoption roadmap moves from opportunity discovery through pilot evaluation to controlled production and continuous improvement. A successful demo is evidence to investigate, not proof of reliable business value.

Choose under exam pressure

Requirement	Choice and reason
High-impact automated decision	Human oversight, documented criteria, bias testing and appeal paths.
Promising pilot with poor adoption	Investigate workflow fit and training before buying more model capacity.

Traps

Efficiency alone does not establish fairness or legality.
An AI security framework does not remove the customer's responsibility for access and configuration.

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