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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Domain 4: Evaluating and Deploying Agentic Workflows | 22% | - Design evaluation datasets and methodologies - Implement continuous evaluation pipelines - Select deployment targets: Agent Runtime, Cloud Run, GKE - Deploy and scale agentic workflows - Monitor, troubleshoot, and optimize agents in production |
| Topic 2: Domain 1: Building Agents Using Low-Code Tools | 13% | - Configure agent triggers, workflows, and responses - Integrate data sources and knowledge bases - Design and configure agents using low-code platforms - Test and iterate low-code agents |
| Topic 3: Domain 2: Using Coding Agents for Application Development | 17% | - Manage MCP servers and custom skills - Integrate coding agents with development workflows - Use coding agents in secure sandboxed environments - Select and configure coding agents |
| Topic 4: Domain 3: Developing Custom Agents | 33% | - Orchestrate multi-agent workflows using MCP and A2A - Configure sessions, memory, and state management - Design agent architectures and model selection - Implement RAG pipelines and vector retrieval - Integrate enterprise data sources and APIs - Build agents with the Agent Development Kit (ADK) - Implement Agent Identity and permissions |
| Topic 5: Domain 5: Securing and Governing Agentic Workflows | 15% | - Implement authentication and authorization for agents - Apply governance policies and human-in-the-loop controls - Protect against prompt injection and data leakage - Use Model Armor, Agent Gateway, and Agent Registry - Monitor audit logs and compliance |
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NEW QUESTION # 31
You are developing a custom retail assistant agent using Agent Development Kit (ADK). The agent checks real-time stock levels by querying a proprietary internal inventory database. You have written a secure Python function that performs this query. You need to integrate the function so that the underlying Gemini model can autonomously decide when to invoke it and automatically format the required parameters based on user input.
What should you do?
Answer: C
Explanation:
The Agent Development Kit (ADK) integrates custom business logic and internal databases through its function tool abstraction. When converting a native Python function into an agent tool, the framework automatically extracts the underlying tool schema, parameter types, and semantic intent directly from the function's signature. By applying explicit Python type annotations (such as str, int, or Pydantic models) and comprehensive docstrings that detail the function's purpose, parameter definitions, and return types, ADK generates a compliant OpenAPI tool schema for the Gemini model. During reasoning loops, the foundation model analyzes user prompts, references the tool's docstring to evaluate applicability, and autonomously generates properly structured function arguments that adhere to the annotated parameter constraints. Injecting raw Python code with eval() introduces severe security vulnerabilities, while client-side regex matching circumvents the agent's autonomous reasoning loop entirely.
Reference: Developing custom agents - Tool definition patterns, FunctionTool wrapping, Python type hints, and parameter docstring schemas.
NEW QUESTION # 32
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?
Answer: C
Explanation:
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.
NEW QUESTION # 33
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?
Answer: A
Explanation:
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.
NEW QUESTION # 34
You are configuring enterprise agents that must dynamically discover and invoke capabilities across managed databases, third-party SaaS tools, and Google Cloud services. Your architecture hosts custom MCP servers on Cloud Run alongside pre-built Google Cloud MCP servers. Your operations team requires a centralized catalog to govern access controls, register JSON tool specifications, and manage IAM identities across all remote HTTP transport endpoints.
You need to maximize operational efficiency and maintain strict governance by establishing a single point of discovery and access control for both native and custom agent capabilities.
What should you do?
Answer: A
Explanation:
Heterogeneous agent environments combining first-party Google Cloud capabilities with custom business services require a centralized control plane for tool governance and catalog discovery. Agent Registry is designed as the single source of truth for agent capabilities, supporting the Model Context Protocol (MCP).
By registering custom Cloud Run-hosted MCP servers as Service resources in Agent Registry, administrators publish standardized JSON tool schemas and declare remote HTTP/SSE transport endpoints. Simultaneously, enabling Google Cloud MCP servers automatically indexes native connectors (e.g., BigQuery, Cloud Logging) within the registry. This unified catalog allows agents to dynamically discover available tools while centralizing IAM access governance, ensuring that callers are authenticated and authorized to invoke specific capabilities. Storing endpoint configuration maps in Secret Manager or using Cloud Endpoints requires bespoke service discovery plumbing and lacks native MCP schema integration, increasing maintenance overhead.
Reference: Securing and governing agentic workflows - Agent Registry Service resources, MCP server centralization, and unified capability governance.
NEW QUESTION # 35
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: C
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 # 36
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