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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Domain 4: Evaluating and Deploying Agentic Workflows | 22% | - Design evaluation datasets and methodologies - Deploy and scale agentic workflows - Select deployment targets: Agent Runtime, Cloud Run, GKE - Implement continuous evaluation pipelines - Monitor, troubleshoot, and optimize agents in production |
| Topic 2: Domain 5: Securing and Governing Agentic Workflows | 15% | - Protect against prompt injection and data leakage - Implement authentication and authorization for agents - Use Model Armor, Agent Gateway, and Agent Registry - Monitor audit logs and compliance - Apply governance policies and human-in-the-loop controls |
| Topic 3: Domain 3: Developing Custom Agents | 33% | - Configure sessions, memory, and state management - Build agents with the Agent Development Kit (ADK) - Design agent architectures and model selection - Implement Agent Identity and permissions - Integrate enterprise data sources and APIs - Orchestrate multi-agent workflows using MCP and A2A - Implement RAG pipelines and vector retrieval |
| Topic 4: Domain 2: Using Coding Agents for Application Development | 17% | - Select and configure coding agents - Manage MCP servers and custom skills - Use coding agents in secure sandboxed environments - Integrate coding agents with development workflows |
| Topic 5: Domain 1: Building Agents Using Low-Code Tools | 13% | - Integrate data sources and knowledge bases - Design and configure agents using low-code platforms - Configure agent triggers, workflows, and responses - Test and iterate low-code agents |
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NEW QUESTION # 10
You are deploying an agent on Gemini Enterprise Agent Platform in Project-A, a dedicated runtime Google Cloud project. The agent needs to retrieve database API credentials from Secret Manager in Project-B, a centralized governance project, and query an Agent Search data store also hosted in Project-B. You are using Agent Identity to govern the agent's permissions across projects. You need to grant the minimum required cross-project IAM access while following the principle of least privilege.
What should you do?
Choose two
Answer: B,D
Explanation:
Establishing cross-project access for autonomous agents under enterprise security standards requires adherence to the principle of least privilege through resource-level IAM bindings. Rather than granting project-wide administrative roles, Agent Identity principals must be granted only the specific roles necessary on the designated target resources. To allow the agent in Project-A to retrieve database API credentials from Secret Manager in Project-B without exposing other project secrets, the agent's identity principal is granted roles/secretmanager.secretAccessor scoped directly to the specific secret resource. This permits reading the secret payload (secretmanager.versions.access) while preventing secret modification or administrative deletion. Similarly, to enable grounded search against an enterprise knowledge corpus hosted in Project-B, the identity principal is granted roles/discoveryengine.viewer scoped strictly to the specific Agent Search data store resource. This grants search query permissions (discoveryengine.servingConfigs.search) without providing write access or broader project-level catalog management rights (discoveryengine.editor). Cross- project impersonation via serviceAccountTokenCreator introduces unnecessary privilege escalation paths.
Reference: Securing and governing agentic workflows - Cross-project Agent Identity, Secret Manager resource scoping, and Discovery Engine Viewer least-privilege IAM.
NEW QUESTION # 11
You are designing an enterprise agent system to process sensitive financial records. The traffic consists of high-volume, high-token classification tasks. You need to design the system to minimize operational costs and limit the number of requests sent outside the bank network.
What should you do?
Answer: D
Explanation:
Processing high-volume, high-token financial records requires balancing data residency, network isolation, and inference cost optimization. Routing all high-volume classification tasks to external public LLM endpoints incurs substantial API token expenses and transfers sensitive payloads across public networks.
Conversely, hosting large foundation models entirely on self-managed infrastructure for every complex task requires heavy GPU capital expenditures. The Google-recommended architecture implements a hybrid tiering model. High-volume, routine classification tasks are served by a fine-tuned, open-weight Small Language Model (SLM, such as Gemma) hosted on a Google Kubernetes Engine (GKE) cluster enclosed within a private VPC Service Controls perimeter. This keeps sensitive financial records within the bank's private network perimeter and reduces per-token compute costs. Only non-routine subtasks requiring deep multi-step reasoning are dynamically routed to managed Gemini Enterprise endpoints over private interconnects.
Reference: Securing and governing agentic workflows - Data residency perimeters, VPC Service Controls, and hybrid SLM/frontier LLM tiered routing.
NEW QUESTION # 12
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: A
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 # 13
You are designing the enterprise governance and observability layer for a distributed multiagent deployment in Google Cloud. You want autonomous subagents to communicate across microservices and external capability servers using agentic communication protocols. You need a solution that meets the following compliance controls:
Provide unified visibility to catalog active agent instances.
Capture protocol interaction payloads for compliance auditing.
Monitor interagent communication traffic without requiring code modifications to individual agent services.
How should you design the solution?
Answer: B
Explanation:
Managing multiagent architectures across distributed microservices requires a dedicated protocol governance and observability plane. Autonomous agents collaborating via the Agent2Agent (A2A) protocol and Model Context Protocol (MCP) exchange structured operational payloads that must be audited without refactoring individual agent codebases. Agent Gateway functions as an agent-aware management proxy positioned directly within the communication paths between orchestrators, subagents, and capability servers. It integrates natively with Agent Registry to track active agent endpoints and verify discovery metadata. Operating as a centralized intermediary, Agent Gateway intercepts inter-service protocol traffic, records full interaction payloads for compliance auditing, and routes telemetry to Cloud Logging and Cloud Trace without requiring custom SDK logging within agent applications. General-purpose API gateways like Apigee or service meshes manage standard HTTP/REST or service-to-service routing but lack native parsing for agentic protocol semantics (such as A2A task negotiations, Agent Cards, and MCP tool sessions). Deploying Agent Gateway satisfies unified cataloging, transparent payload recording, and zero-code agent monitoring.
Reference: Securing and governing agentic workflows - Agent Gateway architecture, A2A/MCP protocol governance, and automated payload auditing.
NEW QUESTION # 14
You are troubleshooting a production customer device support agent built with Customer Experience Agent Studio (CX Agent Studio). Users report that the agent occasionally provides inaccurate warranty extension terms and device repair procedures that do not exist in the connected enterprise Knowledge Base datastore or tool outputs. You have inspected the affected sessions and raw JSON responses and verified the system instructions, variable states, and tool outputs.
You need to identify the root cause of the hallucination errors and enforce strict grounding while minimizing conversational latency.
What should you do?
Answer: A
Explanation:
When conversational agents generate ungrounded claims absent from connected datastores, the foundation model is relying on its internal parametric memory rather than retrieved grounding context. In CX Agent Studio, structuring system instructions with clear XML tags (e.g., < grounding_rules > , < constraints > ) establishes explicit boundaries that instruct the model to answer exclusively from retrieved Knowledge Base articles or tool payloads and state when context is missing. To systematically diagnose the root cause without adding runtime overhead or latency to production calls, teams utilize CX Agent Studio's evaluation framework. Enabling the 'Find issues with AI' capability runs evaluation suites that analyze conversational trajectories and produce a comprehensive loss_report. The loss_report isolates failure modes, detailing whether hallucinations stem from retrieval misses, prompt instruction ambiguity, or classification errors.
Intercepting turns with runtime Python callbacks adds significant latency, while tracking HTTP 500 errors cannot diagnose generative hallucinations.
Reference: Building agents using low-code tools - CX Agent Studio prompt engineering, XML instruction boundaries, and AI-assisted evaluation loss reports.
NEW QUESTION # 15
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