The way we build software is beginning to change. Traditionally, computer programs have mostly done what people explicitly tell them to do: a user selects an option, enters information, clicks a button, and the application performs a defined action. Agentic systems work differently. Instead of requiring a person to specify every individual step, an agent can be given a goal, understand what needs to be accomplished, determine which steps may be required, and use different tools and sources of information to complete the task.

For example, instead of asking an employee to manually collect information from several systems, check the data, prepare a report, and send it to the appropriate person, an agentic system can be designed to perform multiple steps of that process. It can access the required information, use appropriate tools, perform authorized actions, and return a result. People can still define rules, approve important actions, and supervise the system, but they do not necessarily have to control every individual step.

This is why it is important to understand how these systems are designed. Building an agent and connecting it to a few tools is only part of the problem. Architects need to determine what the agent should do, which information it can access, which tools it can use, how it interacts with other systems, how its results are evaluated, and what should happen when something goes wrong.

The Google Professional Agentic Architect certification focuses on these architectural challenges. It requires an understanding of how different parts of an agentic system are designed and connected — from agents and AI models to tools, data, enterprise applications, evaluation, monitoring, security, and production operations.

In other words, the goal is not simply to understand what an agent can do, but to understand how to design the complete system around the agent so that it can reliably perform its intended role.

This practice test focuses on these real-world architectural decisions. Through scenario-based questions, you will practice identifying requirements, selecting appropriate approaches, connecting agents with the tools and information they need, and considering reliability, security, scalability, and production requirements.

The practice test contains 1,500 questions divided into six sections of 250 questions each, covering the major areas required to understand professional agentic architecture.

The first section, Architecting the Agentic Frontier: AI Systems, Models & Design, establishes the foundation for understanding how agentic systems are designed. Questions cover agentic architectures, AI models, foundation models, model capabilities, planning, reasoning, context management, architectural patterns, and system design. The goal is to understand how different parts of an agentic system should be organized and how architectural decisions affect system behavior.

The second section, The Autonomous Builder: Gemini Agents & Enterprise Intelligence, focuses on building agents that can operate in real business environments. Questions cover Gemini, agents, enterprise AI systems, orchestration, multi-agent systems, integration with business applications, and different approaches to using intelligent agents. Scenarios explore how agents can be incorporated into existing business processes and enterprise systems.

The third section, Beyond the Prompt: Custom Agents, Code Execution & Tool-Driven Intelligence, focuses on agents that do more than provide responses and can also take actions. Questions cover tools, function calling, APIs, code execution, custom tools, workflow automation, task planning, and connecting agents with external systems. The focus is on understanding how agents can safely use available capabilities to complete complex tasks.

The fourth section, The Context Engine: Grounded Knowledge, Enterprise Data & Living Agent Memory, focuses on the information that enables agents to produce useful and relevant results. Questions cover grounding, RAG, enterprise data, retrieval, vector search, embeddings, context management, agent memory, and knowledge sources. The goal is to understand how agents can use relevant information instead of relying only on what a model already knows.

The fifth section, From Prototype to Autonomous Production: Evaluation, Deployment & Agent Operations, focuses on what happens when an agent moves from an experiment into a real production system. Questions cover agent evaluation, testing, monitoring, observability, tracing, deployment, scalability, performance, reliability, failure handling, and production operations. You will examine how to determine whether an agent is performing as expected and how to operate and maintain the system after deployment.

The sixth section, The Trust Layer: Secure, Governed & Responsible Autonomous AI, focuses on security and control of agentic systems. Questions cover identity and access management, authentication, authorization, data protection, privacy, encryption, governance, compliance, permission controls, agent boundaries, and responsible AI. Scenarios examine how agents can be given the capabilities they need without providing unnecessary access to systems or information.

Each question includes multiple answer choices, the correct answer, and a detailed explanation. The explanations are designed not only to identify the correct answer, but also to explain why a particular approach fits the scenario and why alternative options may not satisfy the stated requirements.

The questions vary in how they test your knowledge. Some focus directly on agentic architecture, models, tools, data, and security, while others present realistic situations where you must analyze requirements and determine how the system should be designed.

Across all 1,500 questions, you will encounter topics including agent architecture, AI models, Gemini, planning, reasoning, orchestration, multi-agent systems, enterprise agents, tools, function calling, code execution, API integration, grounding, RAG, vector search, embeddings, enterprise data, agent memory, evaluation, observability, deployment, scalability, reliability, security, identity and access management, privacy, governance, and responsible AI.

All six sections can be retaken as many times as needed. This allows you to return to difficult areas, review explanations, identify knowledge gaps, and continue practicing until the concepts become familiar.

This practice test is designed for professionals preparing for the Google Professional Agentic Architect certification, as well as learners who want to develop a stronger understanding of how modern agentic systems are designed and operated. It can also be useful for professionals working in cloud architecture, software engineering, artificial intelligence, machine learning, enterprise technology, data, automation, and security.

After completing all 1,500 questions, you will have practiced a broad range of topics required to understand professional agentic architecture — from agent design and model selection to tools, data, context, memory, evaluation, deployment, operations, security, and governance.

The questions are designed to develop a practical architectural approach: understand what the system needs to accomplish, identify the required components, select an appropriate architecture, determine how the agent should use data and tools, anticipate potential problems, and ensure that the system can operate reliably in a real-world environment.

The goal is not simply to memorize terminology. The goal is to understand how the different parts of an agentic system work together and how architectural decisions should change according to requirements, constraints, and expected outcomes.

Whether you are preparing for the Google Professional Agentic Architect certification, developing deeper expertise in agentic architecture, working with enterprise AI systems, or learning how reliable and scalable agentic systems are designed, this practice test provides 1,500 questions across six focused sections to help you systematically test and strengthen your knowledge.