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Google Professional-Agentic-Architect Exam Syllabus Topics:

Section	Weight	Objectives
Developing custom agents	33%	- Integrating enterprise domain knowledge 1. Integrating managed databases, APIs, third-party SaaS tools, and remote servers using Google Cloud MCP servers 2. Configuring agent permissions using Agent Identity 3. Designing and managing retrieval-augmented generation pipelines and vector retrieval systems 4. Using embedding models, similarity scoring, reranking, Vector Search, and Agent Retrieval - 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 - Designing and building agentic workflows in code 1. Building custom agents using open-source libraries such as Agent Development Kit 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. Configuring agent skills using Agents CLI, plugins, and agent or human modes
Evaluating and deploying agentic workflows	22%	- 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 - Evaluating agents in development and production 1. Creating agent evaluation test sets using golden data, prompts, and edge cases 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. Evaluating agent responses and retrieval quality against golden datasets
Building agents using low-code tools	13%	- 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 - 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
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
Using coding agents for application development	17%	- 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 - Using coding agents effectively 1. Using coding agents in secure sandboxes 2. Refactoring source code, optimizing execution runtimes, and patching application-layer vulnerabilities 3. Configuring coding agents with Model Context Protocol servers, custom skills, and tools

>> Professional-Agentic-Architect試験問題 <<

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Google Cloud Certified - Professional Agentic Architect 認定 Professional-Agentic-Architect 試験問題 (Q76-Q81):

質問 # 76
You are developing an enterprise customer support agent in Customer Experience Agent Studio (CX Agent Studio). The agent handles an order cancellation workflow that requires customer identity verification before invoking an external payment refund tool. During testing, when end users submit complex, multi-sentence requests, the agent frequently skips the intermediate identity verification step and calls the refund tool prematurely.
You need to configure the agent's behavior to enforce strict, sequential execution of every workflow step.
What should you do?

A. Define global instructions in CX Agent Studio's advanced application settings with broad behavioral constraints to apply to every conversational turn.
B. Attach a Model Armor security template to the endpoint to sanitize user conversational inputs.
C. Restructure in-console system instructions in CX Agent Studio into XML tags.
D. Configure a backward handoff rule on the agent node using a boolean variable check condition to handle transfer errors.

正解：C

解説：
Generative agents in CX Agent Studio parse natural language system prompts to guide task execution. When user utterances contain multiple intents, models can skip intermediate operational steps if the workflow instructions lack clear syntactic structure. Restructuring system instructions using explicit XML tags (e.g., < workflow > , < step > , < prerequisites > , < action > ) establishes rigid boundaries that Gemini foundation models parse with high fidelity. Encapsulating the verification step within explicit prerequisite tags and specifying that tool execution is gated on verification success forces the model to respect the required sequential order. Unstructured global text instructions are easily bypassed in complex multi-intent dialogues.
Model Armor provides content safety filtering rather than execution-flow enforcement, and backward handoff rules handle transfer exceptions rather than intra-agent sequence constraints.
Reference: Building agents using low-code tools - CX Agent Studio instruction engineering, XML schema structuring, and deterministic workflow gating.

質問 # 77
You are troubleshooting a hierarchical multiagent architecture built with Agent Development Kit (ADK), in which a primary orchestrator delegates complex search tasks to specialized subagents. Execution traces show that subagents append their full tool execution histories to the orchestrator's context window across multiturn exchanges, causing rapid context window bloat, agent drift, and severe tool invocation latency. You need to isolate subagent execution history and prevent context pollution in the orchestrator.
What should you do?

A. Configure a Pub/Sub topic to decouple subagent events from the orchestrator, and configure the orchestrator agent in Agent Runtime to prevent transferring control back to a parent agent.
B. Wrap subagents using agent tool encapsulation, configure the subagent LLM settings to exclude previous conversation contents, and persist subagent findings to the session state.
C. Increase the maximum iterations limit on the orchestrator's looping agent definition, and configure Cloud Trace sampling to automatically drop duplicate tool payload traces.
D. Deploy subagents inside a sequential pipeline, and enable summarization compaction on the orchestrator's Cloud Storage log bucket in Agent Designer.

正解：B

解説：
Hierarchical multiagent systems frequently suffer from context pollution when subagents append raw, voluminous tool execution histories and intermediate scratchpad traces directly into the parent orchestrator's context window. Over multiple turns, this leads to rapid token bloat, increased inference latency, and cognitive drift in the orchestrator. To solve this in Agent Development Kit (ADK), specialized subagents should be wrapped using agent tool encapsulation (AgentTool), which executes the subagent within an isolated, nested sub-session. By configuring the subagent's language model settings with include_contents='none', the runtime prevents prior conversational history and internal tool execution events from being reflected into subsequent reasoning loops. Essential intermediate findings, extracted parameters, or final task outputs are selectively written to the shared session state rather than accumulating unpruned messages in the active prompt context. Compacting logs via Cloud Storage or adjusting tracing sampling rates only affects telemetry, failing to reduce the orchestrator's in-memory prompt size.
Reference: Developing custom agents - Context boundary management, AgentTool encapsulation, and session state persistence.

質問 # 78
You are designing an agentic workflow in Gemini Enterprise for a media organization. The agent will enable natural language search across thousands of hours of archived video training content. End users must be able to search the video library with text queries and receive precise video segment matches. Your solution must process video files stored in Cloud Storage, map visual content and user text queries into a shared vector space, and perform real-time similarity retrieval at scale.
What should you do?
Choose two

A. Index the generated embedding vectors in Vector Search to perform approximate nearest neighbor (ANN) retrieval for user query embeddings.
B. Store video files with embeddings in BigQuery, create a vector index, and execute pattern matching queries against the index.
C. Convert video files into Base64-encoded strings, and store them in the vector store. Send query requests to the Gemini API.
D. Generate vector representations for video segments using the Multimodal embeddings API in Agent Platform.
E. Use the Text embeddings API with the text-embedding-005 model to generate vectors from Cloud Storage video object path strings and file names.

正解：A、D

解説：
Building an agentic video search system across extensive training archives requires shared semantic representation and scalable vector indexing. The Multimodal Embeddings API on Agent Platform generates dense vector representations across both text and video inputs, projecting visual features, spoken dialogue, and textual search queries into a unified, shared embedding space. By segmenting archived video content and generating segment-level multimodal embeddings with preserved timestamp metadata, the pipeline enables granular temporal retrieval. To support real-time querying across thousands of hours of video, these generated vectors are indexed in Vertex AI Vector Search. Vector Search provides low-latency Approximate Nearest Neighbor (ANN) search at massive enterprise scale, matching user query embeddings against indexed video segments in milliseconds. Text-only embedding models cannot extract visual feature semantics, and storing multi-gigabyte Base64 strings or raw video arrays directly inside vector databases is technically invalid and architecturally prohibitive.
Reference: Developing custom agents - Multimodal Embeddings API, shared vector spaces, and Vertex AI Vector Search indexing.

質問 # 79
You are designing a human-in-the-loop architecture for an enterprise agent built with Agent Development Kit (ADK) that executes high-risk business operations. You must pause agent execution during sensitive tool invocations, present approval requests out-of-band without risk of prompt injection bypass, and reliably resume execution upon human confirmation.
What should you do?

A. Configure Agent Gateway using GatewayPolicyInterceptor to intercept tool payloads, log execution traces using CloudLoggingService, and allow the agent model to parse user natural language confirmation responses from the chat stream.
B. Configure policy interception using SecureRunner to emit confirmation requests, persist execution state using VertexAiSessionService, and require confirmation through a trusted UI component that returns the exact call ID to resume the session.
C. Configure event notifications using PubSubEventBridge, persist pending execution state using a custom GcsStateStore, and deploy a Cloud Run function that evaluates user approvals using an LLM classification prompt to update the confirmation flag.
D. Configure Model Armor security templates using FilterConfig to detect sensitive tool requests, pause agent containers using ContainerLifecycleManager, and resume execution when a user submits a natural language approval prompt.

正解：B

解説：
Executing high-risk operations in autonomous agentic workflows requires strict human-in-the-loop (HITL) authorization guardrails. Allowing the agent's language model to parse conversational approval prompts from the chat stream introduces prompt injection vulnerabilities, where adversarial inputs can manipulate the model into approving unauthorized actions. Under the ADK security architecture, deterministic policy interception is implemented via SecureRunner (and policy enforcement hooks). When a sensitive tool call is evaluated, the runner pauses execution, emits an out-of-band confirmation request, and suspends the workflow while persisting state via VertexAiSessionService. Human compliance officers review the request inside a trusted, authenticated UI external to the conversation stream. Upon explicit approval, the trusted interface returns the exact tool execution call ID to the session runner, resuming the suspended execution context securely. Using natural language confirmations or LLM classification scripts undermines deterministic access governance.
Reference: Securing and governing agentic workflows - Human-in-the-loop (HITL) governance, SecureRunner policy interception, and out-of-band call ID resumption.

質問 # 80
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. Configure a .antigravityrules markdown file in the repository instructing the agent to never generate code that reads host files or connects to external domains.
C. Configure a VPC Service Controls perimeter around the pipeline's execution subnet.
D. Assign a Google Cloud service account with restricted, least-privilege IAM roles to the pipeline execution host.

正解：A

解説：
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.

質問 # 81
......

「今の生活と仕事は我慢できない。他の仕事をやってみたい。」このような考えがありますか。しかし、どのようにより良い仕事を行うことができますか。ITが好きですか。ITを通して自分の実力を証明したいのですか。IT業界に従事したいなら、IT認定試験を受験して認証資格を取得することは必要になります。あなたが今しなければならないのは、広く認識された価値があるIT認定試験を受けることです。そうすれば、新たなキャリアへの扉を開くことができます。GoogleのProfessional-Agentic-Architect認定試験というと、きっとわかっているでしょう。この資格を取得したら、新しい仕事を探す時、あなたが大きなヘルプを得ることができます。何ですか。自信を持っていないからProfessional-Agentic-Architect試験を受けるのは無理ですか。それは問題ではないですよ。あなたはJPNTestのProfessional-Agentic-Architect問題集を利用することができますから。

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