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Google Professional-Agentic-Architect Exam Syllabus Topics:

SectionWeightObjectives
Evaluating and deploying agentic workflows22%- 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. Identifying logic errors, latency bottlenecks, and hallucinations
  • 4. Selecting deployment runtimes including Agent Runtime, Cloud Run, and GKE
Building agents using low-code tools13%- Connecting enterprise data to Gemini Enterprise
  • 1. Configuring secure connections to enterprise proprietary data sources
  • 2. Ingesting and processing unstructured multimodal data including video, audio, and images
- Configuring agentic workflows and behavior using low-code tools
  • 1. Configuring state-based workflows using Gemini Enterprise Workflow Builder and Customer Experience Agent Studio
  • 2. Creating system instructions and in-console prompt templates to guide agent behavior
Developing custom agents33%- Orchestrating and coordinating agentic workflows
  • 1. Using Agent Identity, Agent Registry, Agent Runtime, and agent policies for workflow coordination
  • 2. Coordinating multi-agent handoffs and workflows including parallel, sequential, and graph workflows
  • 3. Orchestrating agents using MCP and Agent2Agent protocols
- Designing and building agentic workflows in code
  • 1. Configuring sessions and memory using Agent Platform Memory Bank and managed sessions
  • 2. Configuring agent skills using Agents CLI, plugins, and agent or human modes
  • 3. Building custom agents using open-source libraries such as Agent Development Kit
  • 4. Selecting and configuring language models based on cost, security, and architecture requirements
- 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
Using coding agents for application development17%- Using coding agents effectively
  • 1. Using coding agents in secure sandboxes
  • 2. Configuring coding agents with Model Context Protocol servers, custom skills, and tools
  • 3. Refactoring source code, optimizing execution runtimes, and patching application-layer vulnerabilities
- Customizing coding agents for enterprise workflows
  • 1. Using Agents CLI to build, scale, govern, and optimize deployed agents
  • 2. Creating skills, plugins, extension hooks, rules, and subagents
Securing and governing agentic workflows15%- Configuring agent security and governance
  • 1. Configuring Principal Access Boundary policies using Agent Identity
  • 2. Implementing authentication and secure tool execution using OAuth 2.0
  • 3. Configuring Agent Gateway to monitor traffic and track agents
  • 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

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Google Cloud Certified - Professional Agentic Architect Sample Questions (Q56-Q61):

NEW QUESTION # 56
You are building a multiagent workflow in Antigravity in which a primary coordinator agent delegates code editing and testing tasks to concurrent subagents. During initial execution, the subagents edit files in the shared workspace simultaneously, causing write conflicts and filling the main chat history with verbose build logs. You want to eliminate file modification conflicts and prevent context rot in the primary coordinator agent.
What should you do?

Answer: B

Explanation:
Parallel coding subagents executing in an unpartitioned workspace inevitably generate concurrent file write collisions and git merge conflicts. To ensure safe concurrent execution, subagents must operate within branched workspace isolation (such as dedicated Git worktrees or temporary filesystem sandboxes). This isolates each subagent's disk modifications until changes are reviewed and integrated. Furthermore, long- running compilation runs, test suites, and linter executions produce verbose stdout/stderr output. Passing these raw logs into the primary coordinator's conversational stream causes context rot, diluting core instructions and saturating the token budget. Offloading build executions to isolated worker context sessions restricts raw diagnostic output to subagent runtimes, enabling workers to report only concise status summaries back to the coordinator.
Reference: Using coding agents for application development - Multiagent orchestration in Antigravity, workspace branching isolation, and context rot mitigation.


NEW QUESTION # 57
You are designing authentication controls for an Agent Development Kit (ADK) agent on Gemini Enterprise Agent Platform. The agent queries internal BigQuery datasets and will soon expand to call external third-party APIs. You want to enable secure, granular outbound tool authentication without managing custom credential vaults or hardcoding secrets in backend code.
What should you do?

Answer: C

Explanation:
Secure outbound tool integration in modern agent platforms requires identity-first authentication and managed credential brokering. The Gemini Enterprise Agent Platform utilizes SPIFFE-based (Secure Production Identity Framework for Everyone) agent identities, providing cryptographic workload identities directly to ADK agents. For native Google Cloud resources like BigQuery, architects grant required IAM permissions directly to the agent's SPIFFE-based identity principal, establishing access without managing long-lived service account keys. For external third-party APIs, Agent Identity auth manager acts as a managed credential vault and token broker. External API keys and OAuth tokens are registered within the auth manager; during tool execution, ADK coordinates with the auth manager to automatically fetch valid tokens and inject authentication headers into outbound requests. This eliminates custom vault management code, prevents hardcoded secrets, and maintains a secure identity boundary across both first-party and third-party tools.
Reference: Securing and governing agentic workflows - SPIFFE agent identities, Agent Identity auth manager, and automated token injection.


NEW QUESTION # 58
You are deploying a data analysis agent with a dedicated agent identity. The agent needs access to a specific Cloud Storage bucket containing financial data in the same project as the agent. You must ensure that the agent identity cannot access any other storage buckets, even if overly permissive project-wide IAM roles are accidentally granted.
What should you do?

Answer: B

Explanation:
In Google Cloud identity architecture, Principal Access Boundaries (PAB) establish guardrail policies that restrict the resource set a principal can access, regardless of broad IAM allow permissions granted at the project, folder, or organization levels. When deploying autonomous data analysis agents handling sensitive financial records, accidental grants of overly permissive project-level roles (e.g., roles/storage.admin or roles
/editor) risk granting the agent access to unauthorized buckets across the project. Attaching a Principal Access Boundary policy to the agent's identity principal defines an explicit, non-bypassable perimeter that limits the principal's effective access exclusively to the specified Cloud Storage bucket resource. Even if a project administrator accidentally assigns broad storage permissions to the identity later, the PAB policy evaluates first and denies access to any resource outside the boundary. IAM Conditions on allow bindings do not prevent other unconditional grants from taking effect, and VPC Service Controls enforce network perimeter boundaries rather than per-identity resource limits.
Reference: Securing and governing agentic workflows - Principal Access Boundaries (PAB), IAM guardrails, and least-privilege agent identity governance.


NEW QUESTION # 59
You are designing an enterprise multiagent procurement system in Google Cloud. An orchestrator agent processes vendor purchase orders, retrieves contract terms, and invokes downstream tools. Your organization' s security policies require you to stop adversarial prompt injection in vendor documents, and high-risk financial actions must receive explicit compliance officer authorization before execution.
How should you meet these requirements?

Answer: B

Explanation:
Enterprise procurement systems processing external vendor contracts face indirect prompt injection attacks hidden within untrusted purchase orders and contract documents. Implementing dynamic payload inspection via Model Armor security templates at Agent Gateway intercepts ingress user prompts, retrieved document payloads, and egress tool traffic in real time. Model Armor detects adversarial injection patterns and jailbreaks before untrusted tokens reach the foundation model's reasoning loop. Furthermore, to govern high- risk financial actions (such as disbursements or contract sign-offs), the architecture binds human-in-the-loop (HITL) approval policies directly to the sensitive tool definitions within the agent framework. When the orchestrator attempts to invoke a high-risk tool, execution pauses deterministically, requiring explicit authorization from a compliance officer before the transaction executes. Post-execution log sinks alert after unauthorized transactions have already executed, and prompt instructions fail against adversarial attacks.
Reference: Securing and governing agentic workflows - Agent Gateway security, Model Armor injection protection, and tool-level HITL approval policies.


NEW QUESTION # 60
You are building a custom customer service agent and have compiled a golden dataset of historical user questions and their ideal answers. Before deploying the agent, you need to programmatically evaluate both the relevance of retrieved documents and the groundedness of the agent's generated responses. You want to minimize custom evaluation code.
What should you do?

Answer: D

Explanation:
Assessing retrieval-augmented conversational agents requires evaluating both the retrieval pipeline (context relevance) and generative synthesis (response groundedness). Implementing custom evaluation scripts or relying on lexical metrics (BLEU/ROUGE) fails to capture semantic fidelity and context alignment. The Agent Development Kit (ADK) provides native eval set tooling designed for automated benchmarking against golden datasets with minimal custom code. By executing evaluations via ADK eval tools, architects configure managed, LLM-based autorater metrics. These autoraters evaluate intermediate retrieval context to grade whether fetched documents are relevant to the user query, and evaluate final response tokens against source documents to score groundedness (faithfulness) and detect ungrounded hallucinations. This provides automated, multi-dimensional assessment within a unified framework before promoting changes to production. The Ranking API reranks chunks rather than evaluating groundedness, and Google Cloud Observability tracks runtime infrastructure telemetry rather than semantic retrieval fidelity.
Reference: Evaluating and deploying agentic workflows - ADK eval set tooling, LLM-based autorater metrics, context relevance, and groundedness scoring.


NEW QUESTION # 61
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