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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 2: Domain 1: Building Agents Using Low-Code Tools | 13% | - Design and configure agents using low-code platforms - Test and iterate low-code agents - Configure agent triggers, workflows, and responses - Integrate data sources and knowledge bases |
| Topic 3: Domain 4: Evaluating and Deploying Agentic Workflows | 22% | - Monitor, troubleshoot, and optimize agents in production - Design evaluation datasets and methodologies - Implement continuous evaluation pipelines - Deploy and scale agentic workflows - Select deployment targets: Agent Runtime, Cloud Run, GKE |
| Topic 4: Domain 3: Developing Custom Agents | 33% | - Design agent architectures and model selection - Integrate enterprise data sources and APIs - Implement RAG pipelines and vector retrieval - Implement Agent Identity and permissions - Orchestrate multi-agent workflows using MCP and A2A - Configure sessions, memory, and state management - Build agents with the Agent Development Kit (ADK) |
| Topic 5: Domain 5: Securing and Governing Agentic Workflows | 15% | - Implement authentication and authorization for agents - Use Model Armor, Agent Gateway, and Agent Registry - Protect against prompt injection and data leakage - Monitor audit logs and compliance - Apply governance policies and human-in-the-loop controls |
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NEW QUESTION # 64
You are configuring an autonomous support agent in Agent Studio to handle multistep customer inquiries and invoke internal tools and connectors. During interactive testing in the console, the agent shows inconsistent execution trajectories and fails to trigger the correct tools. You need to improve the agent's reliability and accuracy.
What should you do first?
Answer: C
Explanation:
When configuring an autonomous agent in Agent Studio, execution trajectory inconsistencies and erroneous tool selection stem from structural ambiguity in the model's core planning cycle. Structured system instructions establish the baseline operating bounds, explicitly prescribing the workflow stages, tool trigger conditions, and parameter handling requirements. Augmenting these instructions with targeted few-shot prompt templates demonstrates step-by-step reasoning trajectories (e.g., ReAct patterns) that illustrate exact tool invocations and output interpretation. While adjusting hyper-parameters like temperature reduces sampling variance, it cannot resolve absent logic boundaries or missing procedural rules. Similarly, switching to a larger model family or enabling Agent Search grounding addresses factual retrieval and reasoning capacity rather than procedural tool orchestration. Long-term memory persistence via Memory Bank maintains conversational history across turns but does not define internal tool-calling mechanics. Therefore, refining system instructions and supplying grounded few-shot examples is the primary, non-invasive remediation.
Reference: Building agents using low-code tools - Agent Studio workflow definitions, tool routing instructions, and few-shot calibration.
NEW QUESTION # 65
You are deploying an autonomous coding agent on Google Kubernetes Engine (GKE) to run user-requested Python scripts. To isolate untrusted code execution, you enabled GKE Agent Sandbox with gVisor to intercept container system calls in user space. During a security audit, you find that an untrusted Python script generated by a prompt injection attack successfully fetched IAM identity tokens by sending an HTTP request to the link-local instance metadata server at 169.254.169.254.
You need to prevent sandboxed code from exfiltrating metadata tokens while maintaining container kernel isolation.
What should you do?
Answer: A
Explanation:
GKE Agent Sandbox with gVisor provides strong application kernel isolation by executing container system calls in a user-space kernel (Sentry), preventing malicious workloads from compromising the underlying host operating system. However, user-space system call virtualization does not restrict network-level IP egress traffic by default. When untrusted, generated code runs inside a pod, it can issue TCP connections to the link- local metadata server (169.254.169.254), potentially exfiltrating node service account tokens. To deterministically block metadata server exfiltration, administrators configure a Kubernetes NetworkPolicy attached to the sandbox Pod. Defining an egress rule that blocks outbound traffic to 169.254.169.254/32 (or restricting egress strictly to authorized internal subnets) closes the metadata vulnerability. Blocking ingress traffic does not prevent outbound token acquisition requests initiated by the pod, and disabling execve breaks the execution runtime needed to launch child Python processes.
Reference: Securing and governing agentic workflows - GKE Agent Sandbox, gVisor container isolation, and Kubernetes NetworkPolicy metadata protection.
NEW QUESTION # 66
You are designing a conversational agent in Gemini Enterprise to query proprietary internal engineering documentation stored across Cloud Storage and internal enterprise repositories. Your organization's security governance policy requires that individual users receive search results only from documents they are authorized to access based on their enterprise identity.
You need to connect this data source to your agent using a managed configuration that automatically enforces these access rules without custom code.
What should you do?
Answer: D
Explanation:
Agent Search (integrated within Gemini Enterprise and Vertex AI Search) supports enterprise data stores with native identity-aware access control list (ACL) synchronization. By connecting enterprise repositories to an access-controlled data store, the service synchronizes source document permissions alongside ingested content. During runtime retrieval, the system resolves the end user's authenticated enterprise identity (e.g., via Google Workspace or Cloud Identity integration) and filters retrieved candidates strictly against the synchronized ACLs before generating grounded responses. Client-side filtering is architecturally insecure and subject to tampering, while standard RAG pipelines on Cloud Storage enforce only bucket-level IAM rather than granular, document-level enterprise entitlements. Deploying custom vector databases on GKE introduces significant operational overhead and requires bespoke middleware to maintain permission synchronization.
An Agent Search data store with ACL synchronization provides managed, zero-code enforcement aligned with enterprise compliance standards.
Reference: Securing and governing agentic workflows - Enterprise search governance, identity propagation, and identity-aware ACL synchronization.
NEW QUESTION # 67
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: A
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 # 68
You are developing an autonomous agent flow deployed on Gemini Enterprise Agent Platform that executes function calling tools against backend cloud APIs. Changes to the API schemas have caused intermittent tool execution failures, leading the agent to generate invalid parameter payloads. You need to improve your diagnostics, identify regressions, and establish quality metrics to benchmark full user interactions.
What should you do?
Answer: B
Explanation:
API schema drift causes agents to generate malformed JSON structures, invalid parameter keys, or out-of- order tool calls. Benchmarking full user interactions and establishing regression baselines requires structured evaluation tracking. The Gen AI evaluation service provides continuous evaluation pipelines that compare the agent's full tool-calling history against reference trajectories using formal trajectory metrics. These metrics assess tool selection precision, argument validity, and parameter schema compliance across multi-turn interactions. Logging evaluation runs to Experiments on the Agent Platform provides visual tracking, allowing engineering teams to benchmark versioned prompts and schemas, detect quality regressions, and analyze diagnostic traces across model iterations. Cloud Logging and Monitoring alert on runtime errors after failures occur in production but do not provide benchmark metrics for pre-deployment regression testing.
Model Armor scans content safety rather than resolving API schema compliance.
Reference: Evaluating and deploying agentic workflows - Gen AI evaluation service, Experiments tracking, trajectory metrics, and regression diagnostics.
NEW QUESTION # 69
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