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Google Professional-Agentic-Architect Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Evaluating and deploying agentic workflows22%- Evaluating agents in development and production
  • 1. Selecting evaluation frameworks and tools including ADK evaluation tooling, Gen AI evaluation service, and custom autoraters
  • 2. Evaluating agent responses and retrieval quality against golden datasets
  • 3. Creating agent evaluation test sets using golden data, prompts, and edge cases
  • 4. Creating continuous evaluation pipelines for agent tool execution
- Deploying and scaling production workloads
  • 1. Monitoring and optimizing agent performance, reliability, and cost
  • 2. Troubleshooting agent drift, tool invocation latency, reasoning loops, and system failures
  • 3. Identifying logic errors, latency bottlenecks, and hallucinations
  • 4. Selecting deployment runtimes including Agent Runtime, Cloud Run, and GKE
Topic 2: Using coding agents for application development17%- 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 3: Building agents using low-code tools13%- 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 4: Developing custom agents33%- Integrating enterprise domain knowledge
  • 1. Integrating managed databases, APIs, third-party SaaS tools, and remote servers using Google Cloud MCP servers
  • 2. Using embedding models, similarity scoring, reranking, Vector Search, and Agent Retrieval
  • 3. Designing and managing retrieval-augmented generation pipelines and vector retrieval systems
  • 4. Configuring agent permissions using Agent Identity
- Designing and building agentic workflows in code
  • 1. Configuring agent skills using Agents CLI, plugins, and agent or human modes
  • 2. Selecting and configuring language models based on cost, security, and architecture requirements
  • 3. Configuring sessions and memory using Agent Platform Memory Bank and managed sessions
  • 4. Building custom agents using open-source libraries such as Agent Development Kit
- Orchestrating and coordinating agentic workflows
  • 1. Coordinating multi-agent handoffs and workflows including parallel, sequential, and graph workflows
  • 2. Using Agent Identity, Agent Registry, Agent Runtime, and agent policies for workflow coordination
  • 3. Orchestrating agents using MCP and Agent2Agent protocols
Topic 5: Securing and governing agentic workflows15%- Implementing secure agent behavior and execution
  • 1. Configuring secure data access and identity propagation using Agent Gateway and Agent Registry
  • 2. Designing safety frameworks and guardrails using Agent Gateway, Model Armor, and human-in-the-loop controls
- Configuring agent security and governance
  • 1. Designing agent governance and policy enforcement using Agent Registry and Model Armor
  • 2. Configuring Agent Gateway to monitor traffic and track agents
  • 3. Configuring Principal Access Boundary policies using Agent Identity
  • 4. Implementing authentication and secure tool execution using OAuth 2.0

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Google Cloud Certified - Professional Agentic Architect Sample Questions (Q74-Q79):

NEW QUESTION # 74
You are deploying an autonomous coding agent on Google Kubernetes Engine (GKE) to run user-requested Python scripts. To isolate untrusted code execution, you enabled GKE Agent Sandbox with gVisor to intercept container system calls in user space. During a security audit, you find that an untrusted Python script generated by a prompt injection attack successfully fetched IAM identity tokens by sending an HTTP request to the link-local instance metadata server at 169.254.169.254.
You need to prevent sandboxed code from exfiltrating metadata tokens while maintaining container kernel isolation.
What should you do?

Answer: A

Explanation:
GKE Agent Sandbox with gVisor provides strong application kernel isolation by executing container system calls in a user-space kernel (Sentry), preventing malicious workloads from compromising the underlying host operating system. However, user-space system call virtualization does not restrict network-level IP egress traffic by default. When untrusted, generated code runs inside a pod, it can issue TCP connections to the link- local metadata server (169.254.169.254), potentially exfiltrating node service account tokens. To deterministically block metadata server exfiltration, administrators configure a Kubernetes NetworkPolicy attached to the sandbox Pod. Defining an egress rule that blocks outbound traffic to 169.254.169.254/32 (or restricting egress strictly to authorized internal subnets) closes the metadata vulnerability. Blocking ingress traffic does not prevent outbound token acquisition requests initiated by the pod, and disabling execve breaks the execution runtime needed to launch child Python processes.
Reference: Securing and governing agentic workflows - GKE Agent Sandbox, gVisor container isolation, and Kubernetes NetworkPolicy metadata protection.


NEW QUESTION # 75
You are configuring a secure agent-to-tool API integration for a financial agent on Gemini Enterprise Agent Platform. The agent calls an internal payment processing microservice protected by OAuth 2.0.
To comply with strict enterprise security policy, you must ensure that:
OAuth client credentials used for service-to-service tool authentication are encrypted at rest and never hardcoded in tool definitions or repository source code.
Tool execution is strictly limited to the minimum required API permissions, so the agent cannot perform unauthorized administrative operations on the payment microservice.
What should you do?
Choose two

Answer: A,C

Explanation:
Enterprise security governance requires strict separation of credentials, encryption at rest, and least-privilege permission scoping for autonomous agent tool invocations. Hardcoding credentials or encoding secrets in Base64 within environment variable files violates compliance standards and exposes sensitive keys in repository histories. To satisfy credential protection requirements, client secrets and certificates must be stored in Secret Manager, with their resource URIs linked to the Agent Identity auth manager configuration.
Agent Identity auth manager resolves secrets securely at runtime to obtain OAuth tokens, preventing credentials from being exposed in application code. To enforce least privilege and prevent unauthorized operations on downstream payment APIs, the auth provider definition must declare explicit OAuth 2.0 scopes (e.g., payments.read and payments.create). Binding this auth provider directly to the specific payment tool definition in Agent Runtime restricts the token's authorization boundary strictly to necessary operations, blocking wildcard access and administrative abuse.
Reference: Securing and governing agentic workflows - Agent Identity auth manager, Secret Manager credential integration, and OAuth 2.0 scope bounding.


NEW QUESTION # 76
You are designing an automated code-refactoring assistant with the open-source Agent Development Kit (ADK) library. The solution requires a multistep process in which a generator agent drafts modernized code, a critic agent evaluates the draft against strict security rules, and a refiner agent modifies the code until the critic agent approves the output or an exit condition is met. You need to select an agentic workflow architecture that natively supports this continuous generation, evaluation, and revision cycle.
What should you do?

Answer: C

Explanation:
Iterative generation, evaluation, and refinement workflows require an architecture that executes cyclic reasoning loops until explicit quality criteria are satisfied. The Agent Development Kit (ADK) provides the LoopAgent construct specifically for repetitive refinement cycles. A LoopAgent orchestrates child subagents-such as a critic agent and a refiner agent-executing them sequentially within an iterative loop.
To prevent runaway execution and control compute costs, the loop is bounded by configuring the max_iterations parameter. To enable dynamic loop termination when security checks pass, the critic subagent is equipped with an approval tool that triggers an escalation action (tool_context.actions.escalate), which signals the LoopAgent to exit successfully upon verifying code compliance. SequentialAgent pipelines execute strictly once and cannot cycle back natively without external event scaffolding, while ParallelAgent executions evaluate concurrently rather than iteratively.
Reference: Developing custom agents - Cyclic multiagent patterns, LoopAgent orchestration, max_iterations bounding, and escalation controls.


NEW QUESTION # 77
You are designing a secure, multiagent framework on Gemini Enterprise Agent Platform. An administrative assistant agent needs access to internal corporate wikis and external communication APIs to synthesize daily updates and draft emails.
You need to establish a security architecture integrated with Agent Gateway to protect against indirect prompt injections and data exfiltration. You want the framework to scan user input prompts and retrieved message content in real time, while enforcing an explicit authorization boundary for critical, state-modifying operations.
What should you do?

Answer: A

Explanation:
A secure multiagent framework requires dual-layer protection: real-time content sanitization and deterministic execution boundaries. External communications and corporate wikis present significant indirect prompt injection attack surfaces, where retrieved content can instruct the model to exfiltrate proprietary data via external APIs. Configuring Model Armor security templates across both ingress prompts and egress responses at the Agent Gateway ensures that incoming instructions, retrieved wiki context, and outbound model payloads are inspected in real time for injection attacks, jailbreaks, and sensitive data leakage. In tandem, critical state-modifying operations (such as sending emails or updating corporate databases) must be protected by human-in-the-loop (HITL) approval policies. When the agent initiates a mutating API call, execution suspends until explicit human authorization is granted, preventing automated exfiltration. Scanning ingress prompts alone leaves egress exfiltration uninspected, while network-level IP packet filtering cannot evaluate semantic LLM content.
Reference: Securing and governing agentic workflows - Model Armor ingress/egress inspection, indirect prompt injection defense, and HITL authorization boundaries.


NEW QUESTION # 78
You are deploying an agent on Gemini Enterprise Agent Platform in Project-A, a dedicated runtime Google Cloud project. The agent needs to retrieve database API credentials from Secret Manager in Project-B, a centralized governance project, and query an Agent Search data store also hosted in Project-B. You are using Agent Identity to govern the agent's permissions across projects. You need to grant the minimum required cross-project IAM access while following the principle of least privilege.
What should you do?
Choose two

Answer: A,D

Explanation:
Establishing cross-project access for autonomous agents under enterprise security standards requires adherence to the principle of least privilege through resource-level IAM bindings. Rather than granting project-wide administrative roles, Agent Identity principals must be granted only the specific roles necessary on the designated target resources. To allow the agent in Project-A to retrieve database API credentials from Secret Manager in Project-B without exposing other project secrets, the agent's identity principal is granted roles/secretmanager.secretAccessor scoped directly to the specific secret resource. This permits reading the secret payload (secretmanager.versions.access) while preventing secret modification or administrative deletion. Similarly, to enable grounded search against an enterprise knowledge corpus hosted in Project-B, the identity principal is granted roles/discoveryengine.viewer scoped strictly to the specific Agent Search data store resource. This grants search query permissions (discoveryengine.servingConfigs.search) without providing write access or broader project-level catalog management rights (discoveryengine.editor). Cross- project impersonation via serviceAccountTokenCreator introduces unnecessary privilege escalation paths.
Reference: Securing and governing agentic workflows - Cross-project Agent Identity, Secret Manager resource scoping, and Discovery Engine Viewer least-privilege IAM.


NEW QUESTION # 79
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