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| Section | Weight | Objectives |
|---|---|---|
| Domain 3: Developing Custom Agents | 33% | - Build agents with the Agent Development Kit (ADK) - Design agent architectures and model selection - Configure sessions, memory, and state management - Integrate enterprise data sources and APIs - Implement RAG pipelines and vector retrieval - Orchestrate multi-agent workflows using MCP and A2A - Implement Agent Identity and permissions |
| Domain 5: Securing and Governing Agentic Workflows | 15% | - Use Model Armor, Agent Gateway, and Agent Registry - Implement authentication and authorization for agents - Monitor audit logs and compliance - Protect against prompt injection and data leakage - Apply governance policies and human-in-the-loop controls |
| Domain 1: Building Agents Using Low-Code Tools | 13% | - Integrate data sources and knowledge bases - Design and configure agents using low-code platforms - Configure agent triggers, workflows, and responses - Test and iterate low-code agents |
| Domain 4: Evaluating and Deploying Agentic Workflows | 22% | - Monitor, troubleshoot, and optimize agents in production - Implement continuous evaluation pipelines - Design evaluation datasets and methodologies - Deploy and scale agentic workflows - Select deployment targets: Agent Runtime, Cloud Run, GKE |
| Domain 2: Using Coding Agents for Application Development | 17% | - Integrate coding agents with development workflows - Use coding agents in secure sandboxed environments - Select and configure coding agents - Manage MCP servers and custom skills |
>> Professional-Agentic-Architect考試大綱 <<
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問題 #28
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?
答案：C
解題說明：
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.
問題 #29
Your organization requires a scanner that checks for secrets and API keys to run before Antigravity writes to any file in a repository. You added an Antigravity workspace rule instructing the agent to run the scanner first.
However, you find some commits where the scanner did not run. You need to ensure the scanner runs before every file write, regardless of what the agent considers relevant.
What should you do?
答案：B
解題說明：
Antigravity enforces developer governance and security policies through lifecycle hooks declared within the .
agents/hooks.json configuration file. While markdown rules provide high-level contextual guidance, generative agents interpret them probabilistically and can bypass instructions during complex file manipulation workflows. To establish a deterministic, non-bypassable control, an organization must implement a PreToolUse hook targeting file-writing actions (such as write or edit tools). The PreToolUse hook intercepts the proposed file mutation before it is executed on the local filesystem or committed to the repository. It passes the file diff or target content to an external secret scanner script, halting execution if secrets or API keys are detected. Workspace rules-even when set to 'Always On'-only adjust prompt context and cannot programmatically gate underlying operating system or tool actions. Creating custom subagents or skills similarly relies on model invocation decisions rather than platform-enforced boundaries.
Therefore, configuring a native PreToolUse hook in hooks.json provides the necessary deterministic pre- execution validation gate.
Reference: Using coding agents for application development - Antigravity lifecycle hooks, PreToolUse interception, and deterministic policy enforcement.
問題 #30
You are designing a multiagent system on Google Cloud in which multiple autonomous agents deployed in Agent Runtime collaborate through a graph workflow and pass context along sequential task routes. You want to ensure that each agent operates with least-privilege access and that its specific actions can be traced independently.
What should you do?
答案：B
解題說明：
In graph-based multiagent systems where tasks transition sequentially across distinct agents, sharing a single service account or passing monolithic credentials violates least privilege and obscures attribution. Agent Runtime provides native support for Agent Identity, enabling each deployed agent instance to receive a unique cryptographic identity principal. By registering each agent within Agent Registry and assigning distinct IAM role bindings to each agent's identity, architects enforce granular least privilege. Each agent possesses permissions only for its specific backend endpoints and tools. Furthermore, using dedicated Agent Identities ensures that all inter-agent calls and tool invocations generate distinct audit traces in Cloud Audit Logs, attributing actions directly to the specific executing subagent. Propagating shared user credentials via local session state introduces security risks and privilege escalation, while API keys lack identity-based access control and granular auditable attribution.
Reference: Securing and governing agentic workflows - Agent Identity, Agent Runtime least privilege, and Agent Registry capability registration.
問題 #31
You are building a custom coding assistant agent with Agent Development Kit (ADK). The agent must recall user preferences, past architectural decisions, and previous debugging steps across completely independent interactive sessions over multiple weeks. You need to complete these tasks without continually growing the LLM context window and inflating inference costs, and you want the simplest solution.
How should you build the agent?
答案：C
解題說明：
Maintaining user preferences, architectural context, and historical debugging steps across multiple independent sessions over extended periods requires a long-term memory system. Prepending full conversation histories into subsequent session prompts causes context window saturation, degrades reasoning quality, and inflates inference costs exponentially. Agent Platform Memory Bank provides a fully managed, long-term memory persistence service designed specifically for ADK agents. Memory Bank continuously extracts salient user preferences, decisions, and factual knowledge from interactive sessions, persists them into managed memory stores, and consolidates related information over time. In subsequent sessions, Memory Bank performs semantic retrieval to inject only the relevant historical memories into the agent's working context, delivering long-term personalization with minimal developer plumbing. Building custom vector retrieval pipelines on Vector Search introduces unnecessary infrastructure overhead, while Agent Runtime sessions are ephemeral and subject to TTL expiration.
Reference: Developing custom agents - Agent Platform Memory Bank, long-term memory extraction, and conversational state consolidation.
問題 #32
You are deploying a custom Antigravity CLI plugin to enforce enterprise data loss prevention compliance across developer workstations. You must configure an event hook that intercepts tool calls immediately before file execution, automatically redacts sensitive API keys from generated code diffs in transit, and stops tool execution if sanitization fails.
What should you do?
答案：D
解題說明：
Antigravity CLI enforces developer guardrails via lifecycle hooks declared within a plugin's hooks.json file.
To prevent secrets or API keys from being written to disk, security validation must execute synchronously prior to file tool invocation. In the CLI hook lifecycle, a handler bound to the pre-tool execution event intercepts the incoming tool payload before execution. The handler inspects the generated code diff, identifies sensitive credentials against DLP rule sets, and rewrites the parameters in transit to redact detected keys. If redaction succeeds, the handler returns an explicit success status, permitting tool execution to proceed with sanitized parameters. If sanitization fails or encounters an uncorrectable violation, the handler halts execution deterministically, preventing sensitive tokens from being written to the workstation filesystem. PostToolUse hooks execute after the tool has completed, which would allow credentials to be committed before cleanup runs.
Reference: Using coding agents for application development - Antigravity lifecycle hooks, hooks.json configuration, PreToolUse interception, and in-transit sanitization.
問題 #33
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Professional-Agentic-Architect最新題庫: https://www.kaoguti.com/Professional-Agentic-Architect_exam-pdf.html