Professional Agentic Architect
Free Google Cloud Professional Agentic Architect Practice Test
12 exam-style questions with full explanations — no sign-up. Score yourself, then close your gaps with the full course.
Exam fee ~$2005 exam domainsLevel Advanced2 timed practice tests in the course
✅ Free practice — no sign-up📝 Real exam-style questions💡 Detailed explanations💸 30-day money-back via Udemy
A retail company wants to deploy a customer support agent that answers questions strictly from a large internal library of PDF product manuals and return policy documents. The team has no dedicated software engineers and wants the fastest path to a grounded, production-ready agent. Which approach BEST meets these requirements?
Vertex AI Agent Builder's data store agents let you point at unstructured documents, auto-index them with Vertex AI Search, and get a grounded conversational experience with citations — all configured through the console, matching the 'no engineers, fastest path' requirement.
A platform team wants Gemini Code Assist's code suggestions to reflect their organization's internal libraries, naming conventions, and architectural patterns stored across several private Git repositories. Which approach BEST achieves this?
Gemini Code Assist Enterprise's code customization feature indexes an organization's private repositories (connected via Developer Connect) and uses retrieval to ground suggestions in internal libraries and patterns, without requiring model fine-tuning.
A development team builds an agentic workflow where three independent subtasks (fetch weather data, fetch traffic data, fetch news headlines) must run at the same time and their outputs merged into a single summary before responding to the user. Which ADK construct should architect use to orchestrate this?
ParallelAgent is the ADK workflow agent designed to run multiple sub-agents concurrently and then make their combined results available to a subsequent step, which fits the requirement of running independent subtasks simultaneously before merging.
A team is evaluating a customer-support agent that must call a sequence of backend APIs (check order status, verify refund eligibility, issue refund) before returning a final answer. The team wants to verify that the agent invoked these tools in the correct order, not just that the final text response was correct. Which Vertex AI Gen AI evaluation approach should they use?
Trajectory evaluation in the Vertex AI Gen AI evaluation service is specifically designed to assess tool-use correctness by comparing the actual sequence of tool/function calls an agent made against a reference trajectory, using metrics such as trajectory_exact_match, trajectory_in_order_match, trajectory_any_order_match, trajectory_precision, and trajectory_recall.
A team is building a multi-agent system on Vertex AI Agent Builder where different agents call different internal APIs (billing, inventory, HR). They want to enforce least privilege for each agent's identity. Which approach BEST achieves this?
Per-agent service accounts with narrowly scoped IAM roles ensure each agent can only access the specific resources it needs, limiting blast radius if any single agent is compromised or misused via prompt injection.
An architect needs a conversational experience where the agent dynamically decides, using LLM reasoning, which steps to take toward a stated goal rather than following a rigid, pre-drawn flow diagram — and the configuration must be done without writing orchestration code. Which Conversational Agents feature should be used?
Playbooks in Conversational Agents let you define a goal, instructions, and available tools, and the LLM dynamically reasons about which steps to execute — configured entirely through console settings, no code.
A developer wants to run AI-assisted coding tasks directly from the terminal for scripting and quick automation, outside of a full IDE, and prefers a tool with a generous free usage tier for individual use. Which Google Cloud tool is the BEST fit?
Gemini CLI is the open-source, terminal-based AI agent designed for command-line and scripting workflows, offering a generous free tier for individual developers signing in with a personal Google account.
An organization has one agent built with Google's Agent Development Kit and another agent built by a partner team using a completely different framework, hosted on a separate service. The two agents need to discover each other's capabilities and exchange task requests and results across that network boundary. Which technology should be used to enable this interoperability?
A2A is the open protocol specifically designed for agent-to-agent communication across frameworks and vendors, using Agent Cards to advertise capabilities and standardized task/message exchange so heterogeneous agents can interoperate.
During agent evaluation, a 'groundedness' metric score is reported as low for several responses even though the agent's answers sound fluent and confident. What does a low groundedness score specifically indicate?
Groundedness specifically measures how well a generated response is supported by the retrieved context or reference documents provided to the model; a low score flags content that is fluent but not substantiated by the source material — a key hallucination signal.
A healthcare organization runs an agentic workflow on Vertex AI Agent Builder that processes patient records. They must ensure that even if a tool is compromised or a prompt injection attack occurs, agent data cannot be exfiltrated to resources outside their approved Google Cloud projects. Which control should they implement?
VPC Service Controls establish a service perimeter that restricts data movement to approved projects/services, directly preventing exfiltration of data to unauthorized destinations even if an agent or tool is compromised.
Within Vertex AI Agent Builder / Conversational Agents, how should an architect add the ability for the agent to call an external REST API mid-conversation, while staying within a low-code configuration approach?
Conversational Agents and Agent Builder support defining tools via an OpenAPI schema directly in the console, letting the agent call the REST API as part of a playbook step with no custom orchestration code.
An architect must ensure that no code produced by an autonomous coding agent is merged into the main branch without human oversight, while still allowing automated checks to run quickly. What should be configured in the CI/CD pipeline?
Treating agent-generated code like any other contribution—requiring a reviewed pull request plus automated scanning—enforces human accountability while still leveraging automation for speed, which is the recommended SDLC integration pattern for coding agents.
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How much does the Professional Agentic Architect exam cost?
The exam fee is approximately $200 and varies by region — confirm current pricing with the certification vendor before you book.
What topics are on the exam?
It covers 5 domains: Build agents using low-code tools (~20%), Use coding agents for application development (~18%), Develop custom agents (~24%), Evaluate and deploy agentic workflows (~20%), Secure and govern agentic workflows (~18%). The full course has a dedicated chapter, lab and practice-test coverage for each.
Is this practice test really free?
Yes — all questions on this page are free with explanations and no sign-up. The paid Udemy course adds two full-length timed exams, video lessons and hands-on labs.
Will this prepare me for the real exam?
The questions mirror the real exam's style and are mapped to the official domains. This is exam-focused preparation — combine the free test with the full course's timed simulations to gauge your readiness.