PAA Practice Exams / Exam guide
Professional Agentic Architect exam guide, section by section
The official guide is four pages: five sections, eleven subsections and 25 considerations, plus a list of tools in scope. Section 3 (custom agents) and Section 4 (evaluation and deployment) together are 55% of the exam.
| Section | Weight | Of 50 questions | Considerations | Main tools |
|---|---|---|---|---|
| Section 1: Building agents using low-code tools | 13% | 6 to 7 | 4 | Workflow Builder, CX Agent Studio, Agent Search, Gemini Enterprise connectors |
| Section 2: Using coding agents for application development | 17% | 8 to 9 | 5 | Antigravity, Claude Code on Google Cloud, Agents CLI, GKE, Cloud Workstations |
| Section 3: Developing custom agents | 33% | 16 to 17 | 9 | ADK, Memory Bank, RAG Engine, Vector Search, Agent Identity, Agent Registry, MCP, A2A |
| Section 4: Evaluating and deploying agentic workflows | 22% | 11 | 7 | ADK evaluation, Gen AI evaluation service, Agent Runtime, Cloud Run, GKE, Cloud Trace |
| Section 5: Securing and governing agentic workflows | 15% | 7 to 8 | 6 | Agent Identity, Principal Access Boundary, Agent Gateway, Model Armor, OAuth 2.0 |
Source: Google Cloud Professional Agentic Architect certification exam guide (PDF), read 2026-11-06. Google has not published the GA question count; "of 50" applies the weights to a 50-question exam.
What each section asks you to decide
Section 1: Building agents using low-code tools (about 13%)
Section 1 is about 13% of the exam and lists four considerations under 1.1 (low-code workflows and instructions) and 1.2 (enterprise data in Gemini Enterprise). Most questions turn on three decisions: whether a rule belongs in instructions or in a deterministic control such as a flow route, handoff rule or Condition step; whether a data source is ingested or federated and which identity setup it needs; and how PDFs, video and audio become retrievable text.
Section 1 study page: decision tables, commonly confused products and 5 free questions with answers.
Section 2: Using coding agents for application development (about 17%)
Section 2 is about 17% of the exam and lists five considerations under 2.1 (using coding agents) and 2.2 (customizing them for enterprise workflows). Most questions turn on where a setting lives (a workspace file committed to the repository, a global file on one machine, or a managed setting or IAM policy enforced centrally), which customization is deterministic (hooks, IAM deny policies, managed settings) rather than model-dependent (rules, skills), and which sandbox setting keeps agent commands isolated while still reaching what they need.
Section 2 study page: decision tables, commonly confused products and 7 free questions with answers.
Section 3: Developing custom agents (about 33%)
Section 3 is about 33% of the exam and lists nine considerations across 3.1, 3.2 and 3.3. Most questions turn on where a control belongs: in code or configuration (callbacks, plugins, workflow agents, graph nodes, IAM and deny policies), not in the model's instructions. The rest turn on which identity holds a permission (the agent's own identity, not a developer account or a shared service account) and which protocol fits the boundary (MCP for tools, A2A for agents owned by others).
Section 3 study page: decision tables, commonly confused products and 13 free questions with answers.
Section 4: Evaluating and deploying agentic workflows (about 22%)
Section 4 is about 22% of the exam and lists seven considerations: four under 4.1 (evaluation) and three under 4.2 (deployment and scaling). Most questions turn on three choices: which evaluation tool and metric fits the data you have (reference answer or rubrics, LLM judge or deterministic, offline evalset or online monitor), whether the agent belongs on Agent Runtime, a Cloud Run resource type or GKE, and which setting or observability view fixes a production symptom such as drift, slow tools or a reasoning loop.
Section 4 study page: decision tables, commonly confused products and 9 free questions with answers.
Section 5: Securing and governing agentic workflows (about 15%)
Section 5 is about 15% of the exam and lists six considerations across 5.1 (configuring security and governance) and 5.2 (secure agent behavior and execution). Most questions turn on three choices: which Agent Identity credential model fits the call (3-legged OAuth, 2-legged OAuth, OIDC ID token or API key), where a control must live (Principal Access Boundary on a principal set, Unified Access Policy on the egress gateway, Model Armor on ingress, egress or inside the agent), and the Agent Gateway placement and hostname allowlisting rules that decide whether agent traffic is allowed at all.
Section 5 study page: decision tables, commonly confused products and 6 free questions with answers.
Tools in scope, with their former names
The current guide uses the 2026 names. Copies of the guide from the beta period name Agent Designer in Section 1, the product now called Workflow Builder; older courses also use Agent Engine and Vertex AI Search. When a study resource uses a name in the "Formerly" column, it describes the same product.
| Tool | Formerly | What it is for |
|---|---|---|
| Agent Development Kit (ADK) | Open-source framework for building, evaluating and deploying agents in code, including sequential, parallel and loop workflow agents and multi-agent systems | |
| Agent Runtime | Agent Engine | Managed execution environment in Gemini Enterprise Agent Platform for running agents; deploys any framework that can be containerized and follows the runtime contract |
| Agent Search | Vertex AI Search | Enterprise search and grounding over company data |
| Agent Retrieval and Vector Search | Vector retrieval for RAG: embeddings, hybrid dense and sparse search, and reranking over enterprise content | |
| RAG Engine | Managed retrieval-augmented generation pipeline: ingestion, chunking, embedding and retrieval | |
| Workflow Builder | Agent Designer | No-code and low-code builder for chat agents and triggered workflows in the Gemini Enterprise web app |
| CX Agent Studio | the evolution of Dialogflow CX | Minimal-code builder for customer-facing conversational agents, built on ADK; can reuse Dialogflow CX flows |
| Agent Identity | A unique, trackable identity for each agent, used for IAM permissions, Principal Access Boundary policies and OAuth to external tools | |
| Agent Registry | Central library of approved agents and tools, including third-party MCP servers | |
| Agent Gateway | Network entry and exit point for agent traffic (client-to-agent ingress and agent-to-anywhere egress) where governance policies are enforced | |
| Skill Registry | Stores agent skills with immutable revisions so agents use validated, versioned skill content | |
| Model Armor | Screens prompts and responses for prompt injection, jailbreaks, harmful content and sensitive data | |
| Antigravity (CLI, SDK, App) | Google's agentic development platform; skills, rules, hooks, plugins and subagents customise its coding agent | |
| Agents CLI in Agent Platform | Command-line tool for creating, evaluating, deploying and publishing agents on Google Cloud | |
| Gen AI evaluation service | Managed evaluation of model and agent output with computation-based metrics, model-based rubric metrics and custom metrics, offline or online |
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