Unit 2 roadmap — Google Cloud’s gen AI offerings

Under each topic, Google's own considerations are quoted from Google's Generative AI Leader exam guide, retrieved 2026-10-06; the numbered objectives beneath them are this hive's learning objectives, written over those considerations. Each topic closes with its own summary of what the exam actually tests, taken from its lecture.

Topic 1 — Assess Google Cloud platform value

GAIL-U2.T1 · 4 objectives · lecture deck of 12 slides

The exam guide's considerations, quoted. Describing how Google's AI-first approach and commitment to future innovation translate into cutting-edge gen AI solutions. Describing how Google Cloud has an enterprise-ready AI platform (e.g., responsible, secure, private, reliable, scalable). Recognizing the advantages of Google's comprehensive AI ecosystem (e.g., integration of gen AI across Google products and services). Describing the benefits of Google Cloud's open approach.

1. Interpret Google AI-first positioning without treating marketing claims as independent comparisons.
2. Evaluate responsible, secure, private, reliable and scalable operation as configuration-dependent requirements.
3. Explain potential integration benefits across the Google AI ecosystem.
4. Explain the benefits and responsibilities of model and tooling choice in an open ecosystem.

What this topic actually tests. AI-first? whose claim is it, and what would test it. Enterprise-ready? which controls must we configure. Ecosystem? which of our tools does it reach. Open? which choices does it hand us, and who will evaluate them.

Topic 2 — Connect infrastructure and data controls to delivery

GAIL-U2.T2 · 3 objectives · lecture deck of 11 slides

The exam guide's considerations, quoted. Identifying the essential components of Google Cloud’s AI-optimized infrastructure and its benefits (e.g., hypercomputer, Google’s custom-designed TPUs, GPUs, data centers, cloud computing). Explaining how Google Cloud's AI platform provides users with control over their data (e.g., security, privacy, governance, open and leading first party models, pre-built and customizable solutions, agents). Describing how Google Cloud's AI platform democratizes AI development (e.g., low-code and no-code tools, pre-trained models, APIs).

1. Explain AI Hypercomputer, TPU, GPU, data-center and cloud roles at a business level.
2. Identify data-control choices across models, governance, prebuilt solutions and agents.
3. Choose low-code, no-code, pretrained-model or API paths for an organization capability.

What this topic actually tests. Which layer fits? an integrated system; TPUs for specific workloads, GPUs and CPUs for others. Which control applies? ask per layer — models, governance, prebuilt solutions, agents. Which path? a pre-trained API first, then no-code, low-code, code.

Topic 3 — Choose an AI tool for workplace tasks

GAIL-U2.T3 · 3 objectives · lecture deck of 11 slides

The exam guide's considerations, quoted. Recognizing the functionality, use cases, and business value of the Gemini app and Gemini Advanced (e.g., Gems). Recognizing the functionality, use cases, and business value of Gemini Enterprise (e.g., Gemini Notebook API, multimodal search, and custom agent capabilities). Recognizing the functionality, use cases, and business value of Gemini for Google Workspace.

1. Match Gemini app and Gems use cases to current account and subscription scope, retaining the guide term Gemini Advanced.
2. Recognize Gemini Enterprise search, notebook and custom-agent use cases with edition and preview boundaries.
3. Match Gemini for Google Workspace assistance to work within Workspace applications.

What this topic actually tests. Gemini app — a general assistant; the work account brings enterprise protections; Gems customise it, skills replace them. Gemini Enterprise — search, assistant and agents across company systems, within edition and preview limits. Gemini in Workspace — help inside the app where the work is.

Topic 4 — Improve customer discovery and service

GAIL-U2.T4 · 2 objectives · lecture deck of 10 slides

The exam guide's considerations, quoted. Recognizing the functionality, use cases, and business benefits of Google Cloud’s external search offerings (e.g., Agent Search on Gemini Enterprise Agent Platform , Google Search). Recognizing the functionality, use cases, and business value of Google’s Customer Engagement Suite (e.g., Conversational Agents, Agent Assist, Conversational Insights, Google Cloud Contact Center as a Service).

1. Distinguish enterprise search, public Google Search and grounding use cases.
2. Choose conversational agents, agent assistance, conversation insights or contact-center services for a service need.

What this topic actually tests. Where does the answer live? your own sites and data → Agent Search; the public, current web → Grounding with Google Search; your documents inside a model's answer → grounding on your data. Who is being helped, and when? customers on their own → CX Agent Studio; a person mid-conversation → Agent Assist; managers afterwards → CX Insights; the whole queue → CCAI Platform.

Topic 5 — Choose building blocks for a custom AI solution

GAIL-U2.T5 · 3 objectives · lecture deck of 12 slides

The exam guide's considerations, quoted. Recognizing the functionality, use cases, and business value of Agent Platform (e.g., Model Garden, Agent Search, Agent Platform AutoML). Recognizing the functionality, use cases, and business value of Google Cloud’s RAG offerings (e.g., prebuilt RAG with Agent Search, RAG APIs). Recognizing the functionality, use cases, and business value of using Agent Platform to build custom agents.

1. Match Model Garden, Agent Search and AutoML to their distinct developer tasks.
2. Choose prebuilt search-based RAG or RAG APIs according to retrieval needs.
3. Recognize when a custom agent is appropriate and identify its tools and operating controls.

What this topic actually tests. Model? Model Garden to find and deploy; AutoML to train with minimal effort. Your data? Agent Search's prebuilt RAG unless you must control the parts — then RAG APIs or RAG Engine. Action? a custom agent with ADK on Agent Runtime, under identity, gateway and registry controls.

Topic 6 — Give agents the right tools

GAIL-U2.T6 · 3 objectives · lecture deck of 12 slides

The exam guide's considerations, quoted. Identifying how agents use tools to interact with the external environment and achieve tasks (e.g., extensions, functions, data stores, and plugins). Identifying relevant Google Cloud services and pre-built AI APIs for agent tooling (e.g., Cloud Storage, databases, Cloud Functions, Cloud Run, Agent Platform, Speech-to-Text API, Text-to-Speech API, Translation API, Document Translation API, Document AI API, Cloud Vision API, Cloud Video Intelligence API, Natural Language API, Google Cloud API Library). Determining when to use Agent Studio and Google AI Studio.

1. Explain how functions, extensions, data stores and plugins connect agents to external tasks.
2. Match Google storage, database, execution and prebuilt AI services to tool inputs and outputs.
3. Distinguish Agent Studio and Google AI Studio by intended workflow and service context.

What this topic actually tests. Who runs it? a function is run by your application; an extension by the platform — and extensions are retiring. What goes in and out? audio, text, documents, images and video each name their API; code runs on Cloud Run; data lands in Cloud Storage or a database. Where do you build? Google AI Studio to try Gemini fast; Agent Studio for production on Google Cloud.