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Google Professional-Agentic-Architect Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Developing custom agents	33%	- Integrating enterprise domain knowledge 1. Using embedding models, similarity scoring, reranking, Vector Search, and Agent Retrieval 2. Configuring agent permissions using Agent Identity 3. Integrating managed databases, APIs, third-party SaaS tools, and remote servers using Google Cloud MCP servers 4. Designing and managing retrieval-augmented generation pipelines and vector retrieval systems - Orchestrating and coordinating agentic workflows 1. Orchestrating agents using MCP and Agent2Agent protocols 2. Coordinating multi-agent handoffs and workflows including parallel, sequential, and graph workflows 3. Using Agent Identity, Agent Registry, Agent Runtime, and agent policies for workflow coordination - Designing and building agentic workflows in code 1. Configuring agent skills using Agents CLI, plugins, and agent or human modes 2. Configuring sessions and memory using Agent Platform Memory Bank and managed sessions 3. Selecting and configuring language models based on cost, security, and architecture requirements 4. Building custom agents using open-source libraries such as Agent Development Kit
Topic 2: Securing and governing agentic workflows	15%	- Configuring agent security and governance 1. Configuring Agent Gateway to monitor traffic and track agents 2. Implementing authentication and secure tool execution using OAuth 2.0 3. Configuring Principal Access Boundary policies using Agent Identity 4. Designing agent governance and policy enforcement using Agent Registry and Model Armor - Implementing secure agent behavior and execution 1. Designing safety frameworks and guardrails using Agent Gateway, Model Armor, and human-in-the-loop controls 2. Configuring secure data access and identity propagation using Agent Gateway and Agent Registry
Topic 3: Using coding agents for application development	17%	- Customizing coding agents for enterprise workflows 1. Creating skills, plugins, extension hooks, rules, and subagents 2. Using Agents CLI to build, scale, govern, and optimize deployed agents - Using coding agents effectively 1. Refactoring source code, optimizing execution runtimes, and patching application-layer vulnerabilities 2. Using coding agents in secure sandboxes 3. Configuring coding agents with Model Context Protocol servers, custom skills, and tools
Topic 4: Building agents using low-code tools	13%	- Configuring agentic workflows and behavior using low-code tools 1. Creating system instructions and in-console prompt templates to guide agent behavior 2. Configuring state-based workflows using Gemini Enterprise Workflow Builder and Customer Experience Agent Studio - Connecting enterprise data to Gemini Enterprise 1. Ingesting and processing unstructured multimodal data including video, audio, and images 2. Configuring secure connections to enterprise proprietary data sources
Topic 5: Evaluating and deploying agentic workflows	22%	- Evaluating agents in development and production 1. Evaluating agent responses and retrieval quality against golden datasets 2. Selecting evaluation frameworks and tools including ADK evaluation tooling, Gen AI evaluation service, and custom autoraters 3. Creating continuous evaluation pipelines for agent tool execution 4. Creating agent evaluation test sets using golden data, prompts, and edge cases - Deploying and scaling production workloads 1. Troubleshooting agent drift, tool invocation latency, reasoning loops, and system failures 2. Monitoring and optimizing agent performance, reliability, and cost 3. Selecting deployment runtimes including Agent Runtime, Cloud Run, and GKE 4. Identifying logic errors, latency bottlenecks, and hallucinations

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Google Cloud Certified - Professional Agentic Architect Sample Questions (Q48-Q53):

NEW QUESTION # 48
You are designing an enterprise agent system to process sensitive financial records. The traffic consists of high-volume, high-token classification tasks. You need to design the system to minimize operational costs and limit the number of requests sent outside the bank network.
What should you do?

A. Deploy an open-weight small language model (SLM) on Google Kubernetes Engine (GKE) in a private Google Cloud perimeter for routine subtasks. Dynamically route complex reasoning steps to a managed enterprise LLM API.
B. Configure Agent Platform Provisioned Throughput on a managed Gemini model to handle both routine high-volume subtasks and complex agent reasoning under a fixed monthly quota.
C. Deploy an open-weight small language model (SLM) on Cloud Run functions with public HTTPS endpoints. Forward unparsed financial payloads to Gemini using LLM Gateway.
D. Provision dedicated Google Kubernetes Engine (GKE) GPU nodes to host an open-source LLM that processes all agent subtasks within the VPC.

Answer: A

Explanation:
Processing high-volume, high-token financial records requires balancing data residency, network isolation, and inference cost optimization. Routing all high-volume classification tasks to external public LLM endpoints incurs substantial API token expenses and transfers sensitive payloads across public networks.
Conversely, hosting large foundation models entirely on self-managed infrastructure for every complex task requires heavy GPU capital expenditures. The Google-recommended architecture implements a hybrid tiering model. High-volume, routine classification tasks are served by a fine-tuned, open-weight Small Language Model (SLM, such as Gemma) hosted on a Google Kubernetes Engine (GKE) cluster enclosed within a private VPC Service Controls perimeter. This keeps sensitive financial records within the bank's private network perimeter and reduces per-token compute costs. Only non-routine subtasks requiring deep multi-step reasoning are dynamically routed to managed Gemini Enterprise endpoints over private interconnects.
Reference: Securing and governing agentic workflows - Data residency perimeters, VPC Service Controls, and hybrid SLM/frontier LLM tiered routing.

NEW QUESTION # 49
You are setting up an automated unit testing pipeline in which Antigravity generates and executes test scripts for third-party Python packages. Your organization's security requirements state that dynamic test suites generated by the LLM must never access local host credentials, private developer files, or unapproved internal network services. You need to configure a secure execution runtime for the coding agent.
What should you do?

A. Execute agent-generated test scripts inside isolated, containerized execution sandboxes.
B. Assign a Google Cloud service account with restricted, least-privilege IAM roles to the pipeline execution host.
C. Configure a VPC Service Controls perimeter around the pipeline's execution subnet.
D. Configure a .antigravityrules markdown file in the repository instructing the agent to never generate code that reads host files or connects to external domains.

Answer: A

Explanation:
LLM-generated code running within automated pipelines constitutes untrusted code execution. Generative models are vulnerable to indirect prompt injection embedded within third-party packages, making soft prompt instructions (such as .antigravityrules) insufficient for enterprise security. Host-level IAM permissions limit cloud resource interaction but fail to isolate local developer credential files, SSH keys, or link-local endpoints.
The Google-recommended security pattern is executing agent-generated test scripts inside isolated, containerized sandboxes (such as gVisor-based sandboxes or ephemeral containers). Sandboxes isolate system calls from the host kernel, restrict filesystem mounts to necessary ephemeral workspaces, and enforce strict network egress boundaries, deterministically blocking access to host credentials and internal enterprise networks.
Reference: Securing and governing agentic workflows - Secure execution environments, agent sandboxing, and untrusted code isolation.

NEW QUESTION # 50
You are evaluating a multitool customer support agent built with Agent Development Kit (ADK) against a curated golden dataset of complex, multiturn user queries. During initial testing, the agent often returns plausible final answers but skips intermediate knowledge retrieval steps or produces responses unsupported by retrieved context, leading to ungrounded claims in production. You need to systematically isolate retrieval quality failures from response generation errors while preventing regressions in tool sequence trajectory accuracy.
What should you do?

A. Execute a gen AI evaluation service EvalTask using the golden dataset, and specify Groundedness metrics alongside trajectory sequence match metrics to separately evaluate retrieval context fidelity and tool call execution.
B. Configure supervised fine-tuning using the golden dataset as training pairs to enforce deterministic tool call parameters and eliminate ungrounded response generation. Execute a gen AI evaluation service EvalTask.
C. Implement Agent Designer guardrails with mandatory retrieval filters to automatically drop agent responses that do not achieve high lexical similarity to reference documents. Add a human-in-the-loop approval workflow.
D. Deploy a Cloud Monitoring dashboard linked to Cloud Logging telemetry to alert on low vector similarity scores from Agent Search retrieval calls. Execute a gen AI evaluation service EvalTask.

Answer: A

Explanation:
Evaluating multiturn, tool-augmented agents requires decoupling retrieval fidelity from intermediate reasoning and tool execution trajectories. When an agent outputs plausible text while skipping retrieval or hallucinating facts, standard end-to-end response evaluation metrics fail to identify the breakdown point. The Gen AI evaluation service supports running an EvalTask that benchmarks the agent against a curated golden dataset using multi-dimensional metrics. Groundedness metrics evaluate whether the generated output is factually supported by the retrieved context, isolating generation hallucinations. Simultaneously, trajectory sequence match metrics (such as tool trajectory exact match and tool call precision) evaluate whether the agent invoked the required retrieval tools in the correct sequence before formulating an answer. Combining these autoraters distinguishes retrieval failures from generation errors and detects trajectory regressions. Fine- tuning models solely to enforce tool sequencing reduces reasoning adaptability.
Reference: Evaluating and deploying agentic workflows - Gen AI evaluation EvalTask, Groundedness autoraters, and trajectory sequence evaluation.

NEW QUESTION # 51
You are designing an automated hotel reservation workflow with Customer Experience Agent Studio (CX Agent Studio). On a dedicated booking page, the agent prompts the guest for check-in dates, room tier, and rewards membership status. If a guest chooses a luxury suite, the conversation flow should immediately branch to a specialized concierge flow, but only after the loyalty status parameter is confirmed. You need to configure this routing logic in the visual console without custom webhook middleware.
What should you do?

A. Configure a global intent route that overrides active page form parameters during conversation turns.
B. Configure a conditional transition route on the page that evaluates form parameters before setting the target flow.
C. Configure a conditional transition route in a session event handler that resets form parameters before setting the target flow.
D. Attach a custom JavaScript event handler to the start page to redirect the incoming user session.

Answer: B

Explanation:
In CX Agent Studio, pages represent state nodes in a conversational graph, managing form parameters and state transitions. Conditional transition routes configured at the page level evaluate conversation variables and parameter states dynamically during each user turn. To satisfy the routing requirement without middleware, a conditional route is placed on the booking page checking that $page.params.room-tier = "luxury suite" and
$page.params.loyalty-status is resolved or confirmed. Because this condition remains in scope while the booking page is active, the engine automatically triggers the transition to the target concierge flow as soon as both criteria evaluate to true. Global intent routes bypass active form collection, and event handlers handle system interruptions or timeouts rather than expected parameter logic.
Reference: Building agents using low-code tools - CX Agent Studio visual state machines, page form parameters, and conditional transition routes.

NEW QUESTION # 52
Your team uses the Antigravity coding agent for both app development and infrastructure as code (IaC) provisioning. Your organization maintains an extensive internal infrastructure style guide that the agent currently includes in its persistent workspace configuration, congesting the context window and degrading reasoning performance during noninfrastructure coding tasks. You need to configure the agent to automatically load and follow the style guide only when infrastructure tasks are initiated.
What should you do?

A. Add the style guide to the global system instructions within the Antigravity agent settings.
B. Add the style guide as a documentation resource by deploying an MCP server for the agent.
C. Add the style guide to a .gemini/rules file in the root of the project workspace.
D. Create a custom skill with the style guide containing a SKILL.md file that defines activation criteria in its frontmatter.

Answer: D

Explanation:
Persistent workspace configuration files (such as .gemini/rules or global system instructions) load directives into the agent's context window on every turn. Including an extensive IaC style guide across all tasks consumes large token budgets, degrading model reasoning on standard application development tasks.
Packaging the style guide as a custom Agent Skill resolves this context bloat. The skill is defined with a SKILL.md file whose YAML frontmatter specifies explicit activation criteria, keywords, and descriptions.
Antigravity monitors the conversational intent and dynamically injects the skill's detailed style instructions into the active context window only when infrastructure provisioning tasks are detected, keeping unrelated interactions uncluttered.
Reference: Using coding agents for application development - Skill frontmatter activation criteria, context window optimization, and just-in-time instruction loading.

NEW QUESTION # 53
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