Overview
Google Cloud's gen AI offerings is about product fit. Its five objectives start with Google Cloud's strengths: an AI-first approach, an enterprise-ready and secure platform, an open ecosystem, AI-optimized infrastructure such as TPUs, and democratized development. Next come prebuilt offerings for employee productivity: the Gemini app, Gemini Enterprise and Gemini for Google Workspace. Customer experience covers external search with Agent Search and the Customer Engagement Suite for contact centers. Developer tools center on Agent Platform, with Model Garden, Agent Search, AutoML, RAG offerings and custom agents. The last objective defines agent tooling: extensions, functions, data stores and plugins, plus when to use Agent Studio or Google AI Studio.
At about 35%, this is the heaviest section. The exam uses Google's 2026 product names, so learn the current names first and the older ones only as a bridge.
How it's tested
Section 2 questions read like a short customer brief: an organization, a goal, the users involved and a constraint such as speed, skills or data control. You pick the Google Cloud offering that matches. The best answer is usually the least custom option that still meets every requirement.
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Employee productivity
Staff need help with email, documents, research or enterprise search, and you choose among the Gemini app, Gemini for Google Workspace and Gemini Enterprise.
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Customer experience
A company wants better site search, self-service or contact center support, and you choose Agent Search or a Customer Engagement Suite component.
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Build on the platform
Developers need model choice, grounding, tuning or a custom agent, and you choose Agent Platform features, RAG options or agent tools.
A bank wants its call center agents to see suggested answers and relevant policy snippets during live calls. Building a custom agent on Agent Platform could work but adds effort. Gemini for Google Workspace helps with documents, not live calls. Agent assist in the Customer Engagement Suite is designed for real-time guidance to human agents, so it is the expected answer.
Objectives 5
Official objectives from the Google Cloud outline, in outline order.
2.1 Describe Google Cloud's strengths in the field of gen AIAI-first innovation, enterprise readiness, open ecosystem, AI-optimized infrastructure, data control.
- What it means
- This objective covers why an organization would choose Google Cloud for gen AI: decades of AI research and an AI-first approach, an enterprise-ready platform that is secure and scalable, an integrated ecosystem with an open approach to models and partners, AI-optimized infrastructure such as TPUs, GPUs and AI Hypercomputer, data control and privacy commitments, and democratized development through low-code tools, pre-trained models and APIs.
- What to understand
- Openness means customers can use Google models, open models such as Gemma and third-party models through Model Garden rather than being locked to one provider. Data control means customer data is not used to train Google's models without permission and stays governed by the customer. TPUs are Google's custom AI accelerators. Democratized development means business users and developers of varying skill can build with prebuilt models and low-code tools.
- How it shows up
- A customer concern or priority is described, and you choose the Google Cloud strength that addresses it.
- Common confusion
- Thinking openness means only open-source models; it also means choice of third-party and partner models.
2.2 Describe how Google Cloud's prebuilt gen AI offerings enable AI powered workGemini app and Gemini Advanced, Gemini Enterprise, Gemini for Google Workspace.
- What it means
- This objective covers ready-made gen AI for employees. The Gemini app is Google's conversational assistant, with Gemini Advanced as the premium tier and Gems as saved custom versions for repeated tasks. Gemini Enterprise is the enterprise platform that brings multimodal search across company data, prebuilt and custom agents, and connectors to business systems into one interface. Gemini for Google Workspace embeds Gemini in Gmail, Docs, Sheets, Slides, Meet and other apps.
- What to understand
- The choice depends on where the work happens and what data is needed. Help writing inside Gmail or Docs points to Gemini for Google Workspace. Searching and acting across many enterprise systems such as file stores, ticketing and CRM, with agents, points to Gemini Enterprise. A general assistant for individual brainstorming or research points to the Gemini app, with Gems for reusable instructions.
- How it shows up
- An employee productivity need is described, and you choose the prebuilt offering that fits the data scope and the place the work happens.
- Common confusion
- Choosing Gemini for Google Workspace when the need is search across many non-Google enterprise systems, which is Gemini Enterprise.
2.3 Describe how Google Cloud's gen AI offerings improve the customer experienceAgent Search, Google Search, and the Customer Engagement Suite.
- What it means
- This objective covers customer-facing offerings. External search uses Agent Search (formerly Vertex AI Search) to give websites and apps Google-quality search and gen AI answers over the company's own content, while Google Search grounding brings in public web information. The Customer Engagement Suite covers conversational agents for self-service chat and voice, agent assist for live human agents, conversational insights for analyzing interactions, and a contact center platform.
- What to understand
- Match the component to the moment in the customer journey. Customers finding products or answers on a website points to Agent Search. Customers resolving issues without a human points to conversational agents. Human agents needing real-time help points to agent assist. Leaders wanting trends and sentiment from past calls points to conversational insights. All are prebuilt, so they deliver value faster than a fully custom build.
- How it shows up
- A customer experience goal is described, and you choose Agent Search or the Customer Engagement Suite component that serves it.
- Common confusion
- Confusing agent assist, which helps human agents, with conversational agents, which talk to customers directly.
2.4 Describe how Google Cloud empowers developers to build with AIAgent Platform features, RAG offerings and building custom agents.
- What it means
- This objective covers Agent Platform (Gemini Enterprise Agent Platform, formerly Vertex AI), Google Cloud's unified platform for building with models and agents. Key features include Model Garden for discovering and deploying Google, open and partner models, Agent Search for prebuilt search and retrieval, and AutoML for training custom models with little code. RAG options include prebuilt RAG through Agent Search and RAG APIs for more control. Developers can also build, deploy and manage custom agents on the platform.
- What to understand
- Model Garden is about choice of model, AutoML is about training a task-specific model without deep ML skills, and Agent Search is the fastest path to grounded search and RAG over enterprise content. RAG APIs suit teams that want to control chunking, embeddings and retrieval themselves. Custom agents are the right call when prebuilt offerings cannot meet a specific workflow, integration or control requirement.
- How it shows up
- A development team's need is described, and you choose the Agent Platform feature, RAG option or custom agent path.
- Common confusion
- Picking AutoML for generative tasks; AutoML trains custom predictive models, while Model Garden provides foundation models.
2.5 Define the purpose and types of tooling for gen AI agentsExtensions, functions, data stores, plugins, AI APIs, Agent Studio and Google AI Studio.
- What it means
- This objective covers what lets an agent act beyond its model: extensions that connect to external APIs, functions that the model calls with structured arguments, data stores that supply grounded knowledge, and plugins. It also covers Google Cloud services and pre-built AI APIs, such as Speech-to-Text, Translation, Vision and Natural Language, that agents can use as tools, and when to prototype in Google AI Studio versus Agent Studio on Agent Platform.
- What to understand
- A model alone can only generate text from what it knows; tools let an agent retrieve current data and take actions such as booking or updating a record. Function calling returns a structured request that your application executes. Data stores ground answers in enterprise content. Google AI Studio is a quick, free prototyping tool for developers using the Gemini API, while Agent Studio is the enterprise environment with governance, security and Google Cloud integration.
- How it shows up
- An agent needs to reach data or take an action, or a team must pick a prototyping environment, and you choose the right tool type or studio.
- Common confusion
- Choosing Google AI Studio for an enterprise production workload that needs Google Cloud security controls, which points to Agent Studio.
High-yield concepts
The terms, rules and formulas this domain turns into questions most often.
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AI-optimized infrastructure
TPUs, GPUs and AI Hypercomputer for training and serving.
- Remember
- TPUs are Google's custom accelerators.
- Tested as
- Strengths questions.
- Trap
- Thinking leaders must manage hardware.
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Open approach
Choice of Google, open and partner models.
- Remember
- Model Garden enables model choice.
- Tested as
- Strengths questions.
- Trap
- Assuming lock-in to Gemini.
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Gemini app and Gems
Conversational assistant; Gems save custom instructions.
- Remember
- Individual productivity.
- Tested as
- Prebuilt questions.
- Trap
- Using it for enterprise-wide search.
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Gemini Enterprise
Enterprise search, agents and connectors in one interface.
- Remember
- Spans many company systems.
- Tested as
- Prebuilt questions.
- Trap
- Confusing it with Workspace add-ons.
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Gemini for Google Workspace
Gemini inside Gmail, Docs, Sheets, Slides and Meet.
- Remember
- Help where the work happens.
- Tested as
- Prebuilt questions.
- Trap
- Choosing it for cross-system search.
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Agent Search
Google-quality search and RAG over company content.
- Remember
- Formerly Vertex AI Search.
- Tested as
- Search and RAG questions.
- Trap
- Forgetting it serves both customers and developers.
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Customer Engagement Suite
Conversational agents, agent assist, insights, contact center.
- Remember
- Prebuilt contact center AI.
- Tested as
- Customer experience questions.
- Trap
- Mixing up its components.
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Agent Platform
Unified platform for models and agents.
- Remember
- Formerly Vertex AI.
- Tested as
- Developer questions.
- Trap
- Looking for the old name.
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Model Garden
Catalog of Google, open and partner models.
- Remember
- Discover, test and deploy models.
- Tested as
- Developer questions.
- Trap
- Confusing it with AutoML.
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AutoML
Train custom models with minimal code.
- Remember
- Task-specific predictive models.
- Tested as
- Developer questions.
- Trap
- Using it for generative tasks.
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Function calling
Model returns a structured request your app executes.
- Remember
- The app performs the action.
- Tested as
- Agent tooling questions.
- Trap
- Thinking the model runs the API itself.
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Agent Studio vs. Google AI Studio
Enterprise build environment versus quick Gemini API prototyping.
- Remember
- Enterprise controls point to Agent Studio.
- Tested as
- Tooling questions.
- Trap
- Using AI Studio for governed production.
Side by side
Concepts candidates mix up, compared on the point that decides the answer.
Gemini app vs. Gemini for Google Workspace vs. Gemini Enterprise
| Concept | Meaning | Key difference | Common trap |
|---|---|---|---|
| Gemini app | Standalone conversational assistant | Individual use, Gems for reuse | Chosen for company-wide search |
| Gemini for Google Workspace | Gemini embedded in Workspace apps | Works inside Gmail, Docs, Meet | Expected to search non-Google systems |
| Gemini Enterprise | Enterprise search and agents platform | Connects many business systems | Confused with a Workspace add-on |
Customer Engagement Suite components
| Concept | Meaning | Key difference | Common trap |
|---|---|---|---|
| Conversational agents | Self-service chat and voice bots | Talk to customers directly | Chosen to coach human agents |
| Agent assist | Real-time help for human agents | Supports the employee | Confused with self-service |
| Conversational insights | Analytics over interactions | Looks back at conversations | Expected to answer customers |
Model Garden vs. AutoML vs. Agent Search
| Concept | Meaning | Key difference | Common trap |
|---|---|---|---|
| Model Garden | Catalog of foundation and partner models | Model choice | Used for training custom classifiers |
| AutoML | Low-code custom model training | Builds a task-specific model | Used for content generation |
| Agent Search | Prebuilt search and RAG | Fast grounded retrieval | Rebuilt by hand unnecessarily |
Exam traps
Each trap is an answer that looks right. Learn the clue that should change your mind.
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Overbuilding
- What candidates pick
- Build a custom agent on Agent Platform for basic website search over product pages.
- Why it looks right
- Custom builds feel flexible and powerful.
- Why it's wrong
- Agent Search delivers grounded site search out of the box, faster and with less effort than a custom agent.
Clue to watch for The need is standard search and speed matters.
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Workspace for everything
- What candidates pick
- Gemini for Google Workspace to search Salesforce, Jira and SharePoint together.
- Why it looks right
- Workspace is where staff already work.
- Why it's wrong
- Gemini Enterprise connects to many enterprise systems and provides search and agents across them.
Clue to watch for Data lives in several non-Google systems.
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Wrong contact center component
- What candidates pick
- Conversational agents to coach human representatives during calls.
- Why it looks right
- Both are in the same suite.
- Why it's wrong
- Agent assist gives live suggestions to human agents; conversational agents serve customers directly.
Clue to watch for A human agent is on the call.
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AutoML for generation
- What candidates pick
- AutoML to generate marketing copy.
- Why it looks right
- AutoML sounds like automatic AI.
- Why it's wrong
- AutoML trains custom predictive models; generating text calls for a foundation model from Model Garden.
Clue to watch for The output is new content.
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Model acts on its own
- What candidates pick
- Expect the model to directly execute an API call to update an order.
- Why it looks right
- Agents appear to take actions.
- Why it's wrong
- With function calling the model proposes a structured call and the application or agent framework executes it.
Clue to watch for The question asks how an action is carried out.
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Prototype tool in production
- What candidates pick
- Google AI Studio for a regulated production agent.
- Why it looks right
- It is quick and simple.
- Why it's wrong
- Agent Studio on Agent Platform provides the enterprise security, governance and Google Cloud integration that production requires.
Clue to watch for Compliance, IAM or enterprise controls are mentioned.
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Openness misread
- What candidates pick
- Assume Google Cloud only offers Google models.
- Why it looks right
- Gemini is front and center.
- Why it's wrong
- Model Garden offers Google, open and third-party models, which is part of Google Cloud's open approach.
Clue to watch for The customer wants to avoid lock-in.
Answer framework
For any Section 2 scenario, use these steps to pick the right Google Cloud offering.
- Identify the user: employees, end customers, contact center staff or developers, since each group maps to a different part of the portfolio.
- For employees, decide where the work happens: inside Workspace apps, across many enterprise systems, or as an individual assistant, then choose Workspace, Gemini Enterprise or the Gemini app.
- For customers, place the need in the journey: finding information calls for Agent Search, self-service calls for conversational agents, live support calls for agent assist, and analysis calls for insights.
- For developers, choose Model Garden for model choice, Agent Search or RAG APIs for grounding, AutoML for low-code custom models, and custom agents for unique workflows.
- If an agent must fetch data or act, add the right tools, and prefer the least custom option that meets every stated requirement for speed, skills and control.
Practice question
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Study strategy
Treat this section as a product map. Build a one-page grid with four columns for employees, customers, contact center and developers, and place each offering in it: Gemini app, Gemini for Google Workspace and Gemini Enterprise for employees; Agent Search for customer search; the Customer Engagement Suite components for contact centers; and Agent Platform with Model Garden, AutoML, RAG options and custom agents for developers.
Learn the current names first. The exam guide was updated for Google's 2026 branding, so drill Agent Platform, Agent Search and Agent Studio, and keep the older Vertex AI names only as memory hooks. Read the product overview pages on cloud.google.com for each offering and note the one sentence that says who it is for.
Then study agent tooling with a simple story: an agent that checks order status uses a data store or function, and an agent that books a return uses a function or extension. Practice deciding between prebuilt and custom, since many questions reward the least complex option that meets the requirements. Because this is about 35% of the exam, give it the most study time and review missed practice questions by offering.
FAQ
How much of the GCP Gen AI Leader exam is Google Cloud's gen AI offerings?
Google Cloud's gen AI offerings is about 35% of the exam, the largest of the four sections.
Is Vertex AI still on the exam?
The current exam guide uses Google's 2026 names. Agent Platform is the successor name for Vertex AI, and Agent Search replaces Vertex AI Search. Expect the new names in questions.
What is the difference between Gemini Enterprise and Gemini for Google Workspace?
Gemini for Google Workspace adds Gemini to Gmail, Docs, Sheets, Slides and Meet. Gemini Enterprise is a separate platform for search and agents across many enterprise systems, including non-Google ones.
What does the Customer Engagement Suite include?
Conversational agents for self-service, agent assist for live human agents, conversational insights for analytics, and a contact center platform.
When should a team build a custom agent?
When prebuilt offerings such as Gemini Enterprise or Agent Search cannot meet a specific workflow, integration or control requirement. The exam usually favors prebuilt options when they suffice.
What is the difference between Agent Studio and Google AI Studio?
Google AI Studio is a fast way to prototype with the Gemini API. Agent Studio runs on Agent Platform with enterprise security, governance and Google Cloud integration for production work.
Do I need hands-on experience with these products?
It helps but is not required. Google recommends familiarity with the products, and questions test product fit rather than configuration steps.
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Test your Domain 2 knowledge
You have covered Google Cloud's strengths, prebuilt offerings, customer experience tools, Agent Platform and agent tooling. See whether you can pick the right offering in exam-style scenarios.
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