Google Professional-Agentic-Architect Deutsch Prüfung - Professional-Agentic-Architect Fragen&Antworten

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Google Professional-Agentic-Architect Exam Syllabus Topics:

Section	Weight	Objectives
Securing and governing agentic workflows	15%	- 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. 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
Developing custom agents	33%	- Integrating enterprise domain knowledge 1. Configuring agent permissions using Agent Identity 2. Designing and managing retrieval-augmented generation pipelines and vector retrieval systems 3. Integrating managed databases, APIs, third-party SaaS tools, and remote servers using Google Cloud MCP servers 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. Configuring sessions and memory using Agent Platform Memory Bank and managed sessions 2. Selecting and configuring language models based on cost, security, and architecture requirements 3. Building custom agents using open-source libraries such as Agent Development Kit 4. Configuring agent skills using Agents CLI, plugins, and agent or human modes
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. Configuring secure connections to enterprise proprietary data sources 2. Ingesting and processing unstructured multimodal data including video, audio, and images
Evaluating and deploying agentic workflows	22%	- Evaluating agents in development and production 1. Selecting evaluation frameworks and tools including ADK evaluation tooling, Gen AI evaluation service, and custom autoraters 2. Creating continuous evaluation pipelines for agent tool execution 3. Creating agent evaluation test sets using golden data, prompts, and edge cases 4. Evaluating agent responses and retrieval quality against golden datasets - Deploying and scaling production workloads 1. Troubleshooting agent drift, tool invocation latency, reasoning loops, and system failures 2. Selecting deployment runtimes including Agent Runtime, Cloud Run, and GKE 3. Identifying logic errors, latency bottlenecks, and hallucinations 4. Monitoring and optimizing agent performance, reliability, and cost
Using coding agents for application development	17%	- 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 - 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

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Google Cloud Certified - Professional Agentic Architect Professional-Agentic-Architect Prüfungsfragen mit Lösungen (Q12-Q17):

12. Frage
You are designing an enterprise agentic platform that connects autonomous agents to internal managed databases, custom API endpoints, and third-party software-as-a-service (SaaS) integrations. Individual product teams build redundant integration adapters and connect to remote services, resulting in fragmented security boundaries and high maintenance overhead. You need to implement a unified architecture to standardize these integrations securely.
What should you do?

A. Deploy MCP servers hosted alongside each agent instance. Store the configuration manifests in Artifact Registry. Grant the Service Account User IAM role to the agent identities.
B. Deploy MCP servers for data and API access. Register API endpoints in Cloud API Gateway using OpenAPI specifications. Enforce VPC Service Controls perimeters.
C. Deploy MCP servers for each external service. Index the endpoints in Agent Search data stores for dynamic agent discovery. Manage tool access using the Restrict Resource Service Usage constraint.
D. Deploy MCP servers for data and API access. Register custom endpoints using Agent Registry Service resources. Enforce authorization using IAM policies and Model Armor floor settings.

Antwort: D

Begründung:
Enterprise multiagent systems require standardized, reusable tool interfaces to eliminate fragmented point-to- point adapters and unmanaged credentials. Deploying Model Context Protocol (MCP) servers standardizes tool contracts and execution interfaces across diverse backends, managed databases, and SaaS APIs. To govern these capabilities centrally, custom MCP endpoints must be registered within Agent Registry as managed Service resources. Agent Registry acts as an enterprise catalog, providing a centralized control plane where security teams catalog tool schemas, monitor discovery, and enforce consistent IAM authorization policies on incoming agent identities. Furthermore, implementing Model Armor floor settings establishes mandatory, non-bypassable content inspection and data loss prevention baselines across all tool invocations.
This ensures that sensitive data leakage, prompt injection attacks, and malicious payloads are filtered uniformly across all agents. Co-locating MCP servers with individual agents duplicates infrastructure, while using Agent Search for tool discovery lacks native runtime schema management and IAM enforcement.
Reference: Securing and governing agentic workflows - Model Context Protocol (MCP) standardization, Agent Registry Service resources, and Model Armor floor settings.

13. Frage
You are customizing enterprise coding workflows by extending Antigravity CLI with Agents CLI. Your team needs to package reusable agent capabilities and safety guardrails across developer workspaces while running automated verifications before cloud deployment. You also need to maintain consistent governance and agent behavior across all local workspaces.
What should you do?

A. Transform system instructions into workspace context files, and use the agents-cli run command to validate behaviors before attaching them to Agent Gateway.
B. Deploy guardrails as server-side workspace hooks, and use the agents-cli scaffold command to validate behaviors before storing them in Model Armor.
C. Define persona subagents into workspace configuration files, and use the agents-cli deploy command to validate behaviors before publishing them to Agent Registry.
D. Convert the agent capabilities into shareable Agent Skills, and use the agents-cli eval command to validate behaviors before publishing them to Agent Registry.

Antwort: D

Begründung:
Agent Skills represent the standard format for packaging reusable instructions, workflows, and guardrails across workspaces. To govern these capabilities enterprise-wide, teams combine Agent Skills with the agents- cli framework. Before publishing capabilities to the centralized Agent Registry, teams execute automated evaluation suites using agents-cli eval. This command runs test cases against the packaged skills, evaluating trajectory accuracy, tool invocation compliance, and guardrail enforcement against golden datasets. Once validated, publishing the skills to Agent Registry centralizes capability discovery and version management, ensuring all developer workspaces operate under verified configurations.
Reference: Using coding agents for application development - Enterprise skill lifecycle, agents-cli evaluation framework, and Agent Registry publication.

14. Frage
You are preparing to deploy an enterprise technical troubleshooting agent built with Agent Development Kit (ADK). Your company's QA team has compiled 500 representative customer support test cases paired with authoritative reference documents and expected answer trajectories. You need to establish a continuous integration testing process to measure document retrieval precision and answer faithfulness before releasing software updates.
How should you configure the evaluation pipelines in ADK using the agents-cli eval run command?

A. Measure task trajectory completion rates and exact-match tool call argument accuracy against baseline test logs.
B. Implement a scoring script that computes raw cosine similarity between embedding vectors of agent responses and reference answers.
C. Measure context precision and recall for documents against the ground truth.
D. Assess model response trajectories using semantic BLEU and ROUGE n-gram overlap scoring against reference answers.

Antwort: C

Begründung:
Establishing a robust CI/CD evaluation pipeline for retrieval-augmented agents requires measuring the quality of the underlying information retrieval pipeline alongside answer fidelity. When evaluating an ADK agent using agents-cli eval run, the pipeline benchmarks candidate builds against a curated test dataset containing ground-truth source documents. Measuring context precision evaluates whether retrieved document chunks are relevant to the query and ranked appropriately near the top of the context payload. Measuring context recall assesses whether all necessary reference documents required to answer the query were successfully retrieved by the agent's search tools. Together, context precision and recall establish the baseline retrieval accuracy required for answer faithfulness. Cosine similarity, BLEU, and ROUGE metrics measure superficial token overlap or embedding closeness rather than evaluating retrieval precision or context coverage.
Reference: Evaluating and deploying agentic workflows - ADK evaluation framework, agents-cli eval run, context precision, and context recall metrics.

15. Frage
You are creating a support voice assistant for a telecommunications organization using Customer Experience Agent Studio (CX Agent Studio). Inbound calls must be dynamically routed according to caller intent. If a caller requests billing details or account changes, PIN/OTP verification is required to establish a secure data- access boundary before any generative resolution steps can be performed. You want to configure the solution with minimal custom code.
What should you do?

A. Build a single generative agent with authentication and troubleshooting components configured as parallel tools. When a customer requests account-sensitive help, execute both tools concurrently to minimize latency, and configure safety filters to redact any diagnostic output if authentication fails.
B. Build a sequential cascade of two independent generative agents. Configure the first agent to handle intent classification and the OTP verification, and store state in an external database. Have the second agent poll this database for customer ID and continue the conversation, triggering a custom session initiation protocol (SIP) transfer webhook if needed.
C. Build a single generative agent. Use system instructions to classify intent. Use prompt-based guardrails and the model's memory to enforce authentication before running troubleshooting steps. Execute a custom webhook tool to handle the OTP API call.
D. Build a hybrid architecture. Use a generative steering node to classify intent. Route account-sensitive queries to a deterministic flow component for PIN/OTP authentication. Pass the verified customer ID as a session parameter to the runtime.

Antwort: D

Begründung:
Production telecommunications assistants require strict boundaries around sensitive verification procedures.
Relying entirely on generative LLMs or prompt-based guardrails to enforce PIN/OTP validation introduces severe security vulnerabilities, such as instruction bypass and non-deterministic state management. In CX Agent Studio, the Google-recommended pattern is a hybrid architecture: a generative steering node dynamically interprets natural language caller intent, and upon recognizing an account-sensitive request, executes a transition to a deterministic flow. Deterministic flows enforce strict, sequential validation rules for credential input and OTP verification without risk of generative drift. Once the caller is successfully authenticated, the flow outputs the verified customer ID into the session parameters, allowing downstream generative agents and tools to execute authenticated tasks safely without custom database polling or complex webhook infrastructure.
Reference: Building agents using low-code tools - CX Agent Studio hybrid architectures, generative steering nodes, and deterministic flow transitions.

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

Antwort: B

Begründung:
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.

17. Frage
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