Professional-Agentic-Architect시험준비자료시험최신기출문제

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Google Professional-Agentic-Architect Exam Syllabus Topics:

Section	Weight	Objectives
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. Connecting agents securely to enterprise data sources using Gemini Enterprise and Agent Search - Configuring agentic workflows and behavior using low-code tools 1. Configuring state-based workflows, transitions, and event handlers using Gemini Enterprise Workflow Builder and Customer Experience Agent Studio 2. Creating system instructions and prompt templates to guide agent behavior
Securing and governing agentic workflows	15%	- Configuring agent security and governance 1. Using 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. Implementing agent governance and policy enforcement using Agent Registry and Model Armor - Implementing secure agent behavior and execution 1. Configuring secure data access and identity propagation 2. Designing safety frameworks, guardrails, and human-in-the-loop controls
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 Antigravity - Using coding agents effectively 1. Using secure sandboxes for coding agents with GKE, Cloud Workstations, and Antigravity 2. Configuring coding agents with MCP servers, custom skills, and tools 3. Refactoring source code, optimizing execution runtimes, and patching application vulnerabilities
Evaluating and deploying agentic workflows	22%	- Evaluating agents in development and production 1. Building continuous evaluation pipelines for agent tool execution 2. Using ADK evaluation tools, Gen AI evaluation services, and custom autoraters 3. Creating test datasets, golden datasets, prompts, and edge cases 4. Evaluating agent responses and retrieval quality against reference datasets - Deploying and scaling production workloads 1. Monitoring and optimizing agent performance, reliability, latency, and cost 2. Troubleshooting agent drift, tool invocation latency, reasoning loops, and system failures 3. Selecting deployment environments including Agent Runtime, Cloud Run, and GKE
Developing custom agents	33%	- Orchestrating and coordinating agentic workflows 1. Designing multi-agent handoffs and sequential, parallel, and graph-based workflows 2. Implementing agentic protocols including MCP and Agent2Agent (A2A) - Designing and building agentic workflows in code 1. Selecting and configuring LLMs and SLMs based on cost, security, and architecture requirements 2. Configuring agent sessions, memory, and managed state 3. Building custom agents using open-source libraries such as Agent Development Kit (ADK) 4. Configuring agent skills, plugins, and execution modes using Agents CLI - Integrating enterprise domain knowledge 1. Integrating databases, APIs, third-party SaaS tools, and MCP servers 2. Designing RAG pipelines, embeddings, vector retrieval, similarity scoring, and reranking 3. Configuring agent permissions and Agent Identity

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Professional-Agentic-Architect시험패스 가능한 공부 - Professional-Agentic-Architect최신버전 덤프데모문제

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최신 Agentic Architect Professional-Agentic-Architect 무료샘플문제 (Q65-Q70):

질문 # 65
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. 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.
B. 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.
C. 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.
D. 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.

정답：B

설명：
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.

질문 # 66
You are building a customer support agent. To meet corporate governance policies, you must ensure that the agent never exposes users' personally identifiable information (PII) in its responses. You want to enforce these guardrails without changing the agent's core app code.
How should you configure this governance?

A. Configure an Agent Gateway routing rule to intercept traffic and route the traffic through a custom Cloud Run webhook that performs regex string matching for PII.
B. Update the agent's system prompt with strict negative instructions forbidding the generation of PII.
C. Create a Model Armor policy configured with Sensitive Data Protection, and attach this policy to the agent's configuration.
D. Deploy the agent within a VPC Service Controls perimeter, and configure an egress rule that drops HTTP responses containing PII payloads.

정답：C

설명：
Enforcing privacy compliance and PII protection without modifying core agent application code is accomplished through managed governance policies on Gemini Enterprise Agent Platform. Model Armor provides a centralized guardrail engine that integrates natively with Google Cloud Sensitive Data Protection (formerly DLP API). By creating a Model Armor policy configured with Sensitive Data Protection inspection and de-identification templates, administrators define exact PII detectors (such as credit card numbers, national identification numbers, emails, and phone numbers). Attaching this policy directly to the agent's deployment configuration ensures that all incoming user prompts and outgoing model responses are inspected and sanitized automatically before being returned to the end user. Negative system prompt instructions are non-deterministic and susceptible to adversarial jailbreaks, custom Cloud Run webhooks introduce operational maintenance overhead, and VPC Service Controls enforce network perimeter boundaries rather than application-layer PII token inspection.
Reference: Securing and governing agentic workflows - Model Armor guardrails, Sensitive Data Protection integration, and zero-code PII sanitization.

질문 # 67
You are configuring Agent Platform Memory Bank for a wealth management advisor agent deployed on Gemini Enterprise Agent Platform.
During the initial evaluation, you observe that Memory Bank creates duplicate and conflicting memory entries when users update their investment risk tolerance. You need to configure Memory Bank to properly consolidate updated user preferences over time, resolve conflicting information, and restrict memory extraction strictly to relevant personal information and user preferences.
What should you do?
Choose two

A. Set an in-memory FIFO eviction policy on the managed session service to automatically purge older session event logs when user profile updates occur.
B. Configure memory topic customization in Memory Bank to enable the USER_PERSONAL_INFO and USER-PREFERENCES managed topics.
C. Disable identity scoping on the Memory Bank instance to do a similarity search for user information during memory consolidation.
D. Add user risk tolerance values into the system instruction prompt as immutable string constants during session creation.
E. Specify a consistent user identity scope during memory generation and retrieval.

정답：B,E

질문 # 68
You maintain an inventory management agent that uses multiple APIs as tools. You need to automatically verify that the agent chooses the correct tool and forms the correct API arguments whenever a new agent configuration is pushed to the code repository. The evaluation must run programmatically and determine pass
/fail status using semantic match criteria instead of exact string matching.
What should you do?

A. Configure a Cloud Scheduler job that invokes the agent during the pipeline build with a set of test prompts, and write a custom script to verify if the agent's final text response matches a hardcoded string.
B. Configure a Cloud Monitoring alerting policy on the Agent Runtime metrics to automatically halt the CI
/CD deployment if the rate of HTTP 5xx errors from tool invocations exceeds your established threshold.
C. Enable Model Monitoring on the deployed agent to continuously track prediction drift and alert the pipeline if the frequency of specific tool invocations deviates from the baseline distribution.
D. Integrate the gen AI evaluation service into your CI/CD pipeline to automatically execute a golden dataset of queries against the agent, and use autorater metrics for tool selection accuracy and parameter validity.

정답：D

설명：
Validating autonomous agent tool calling requires evaluating reasoning trajectories and semantic parameter validity rather than performing rigid lexical string matching. The Google Cloud Gen AI evaluation service provides continuous evaluation capabilities that integrate directly into automated CI/CD pipelines (e.g., Cloud Build). By running a curated golden dataset of test queries against the candidate agent configuration, the evaluation service employs model-based autoraters to grade tool calling behavior. Specific autorater metrics- such as tool selection accuracy and tool parameter validity-evaluate whether the agent invoked the appropriate API and generated semantically and structurally valid arguments matching the required schema.
This automated scoring determines pass/fail quality gates before code is merged or promoted. Model Monitoring tracks drift across production serving traffic after deployment rather than evaluating pre- deployment builds. Hardcoded string comparisons produce false negatives due to generative variability, and tracking HTTP 5xx errors cannot detect silent semantic errors in tool selection.
Reference: Evaluating and deploying agentic workflows - Gen AI evaluation service, CI/CD automated quality gates, autorater metrics, and trajectory validation.

질문 # 69
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?

A. Configure Model Armor post-execution log sink filters in Cloud Logging, and implement a Cloud Run functions webhook to notify compliance officers after transaction completion.
B. Configure Model Armor security templates on downstream tool invocation outputs, and implement an asynchronous Workflows execution logic with a human-approval step using a persistent database.
C. Configure Model Armor baseline harm category filters in Model Garden, and implement prompt rules directing the model to ignore embedded commands.
D. Configure Model Armor security templates at Agent Gateway for dynamic payload inspection, and implement human-in-the-loop approval policies on tools.

정답：D

설명：
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.

질문 # 70
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