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Google Professional-Agentic-Architect Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Building agents using low-code tools13%- Connecting enterprise data to Gemini Enterprise
  • 1. Ingesting and processing unstructured multimodal data including video, audio, and images
  • 2. Configuring secure connections to enterprise proprietary data sources
- Configuring agentic workflows and behavior using low-code tools
  • 1. Configuring state-based workflows using Gemini Enterprise Workflow Builder and Customer Experience Agent Studio
  • 2. Creating system instructions and in-console prompt templates to guide agent behavior
Topic 2: Developing custom agents33%- 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. Using Agent Identity, Agent Registry, Agent Runtime, and agent policies for workflow coordination
  • 2. Orchestrating agents using MCP and Agent2Agent protocols
  • 3. Coordinating multi-agent handoffs and workflows including parallel, sequential, and graph workflows
- Designing and building agentic workflows in code
  • 1. Building custom agents using open-source libraries such as Agent Development Kit
  • 2. Configuring sessions and memory using Agent Platform Memory Bank and managed sessions
  • 3. Configuring agent skills using Agents CLI, plugins, and agent or human modes
  • 4. Selecting and configuring language models based on cost, security, and architecture requirements
Topic 3: Evaluating and deploying agentic workflows22%- Evaluating agents in development and production
  • 1. Evaluating agent responses and retrieval quality against golden datasets
  • 2. Creating continuous evaluation pipelines for agent tool execution
  • 3. Selecting evaluation frameworks and tools including ADK evaluation tooling, Gen AI evaluation service, and custom autoraters
  • 4. Creating agent evaluation test sets using golden data, prompts, and edge cases
- Deploying and scaling production workloads
  • 1. Monitoring and optimizing agent performance, reliability, and cost
  • 2. Selecting deployment runtimes including Agent Runtime, Cloud Run, and GKE
  • 3. Identifying logic errors, latency bottlenecks, and hallucinations
  • 4. Troubleshooting agent drift, tool invocation latency, reasoning loops, and system failures
Topic 4: Using coding agents for application development17%- Using coding agents effectively
  • 1. Configuring coding agents with Model Context Protocol servers, custom skills, and tools
  • 2. Refactoring source code, optimizing execution runtimes, and patching application-layer vulnerabilities
  • 3. Using coding agents in secure sandboxes
- Customizing coding agents for enterprise workflows
  • 1. Creating skills, plugins, extension hooks, rules, and subagents
  • 2. Using Agents CLI to build, scale, govern, and optimize deployed agents
Topic 5: Securing and governing agentic workflows15%- Implementing secure agent behavior and execution
  • 1. Designing safety frameworks and guardrails using Agent Gateway, Model Armor, and human-in-the-loop controls
  • 2. Configuring secure data access and identity propagation using Agent Gateway and Agent Registry
- Configuring agent security and governance
  • 1. Designing agent governance and policy enforcement using Agent Registry and Model Armor
  • 2. Configuring Agent Gateway to monitor traffic and track agents
  • 3. Configuring Principal Access Boundary policies using Agent Identity
  • 4. Implementing authentication and secure tool execution using OAuth 2.0

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Google Cloud Certified - Professional Agentic Architect Sample Questions (Q42-Q47):

NEW QUESTION # 42
You are designing research AI infrastructure for a university computing facility that supports two distinct academic workloads:
A high-volume, 24/7 data extraction pipeline handling sensitive student records that are subject to strict data residency rules.
An intermittent, complex research workload requiring advanced multimodal reasoning over large context windows, with unpredictable usage spikes.
You need to minimize total operational expenditure and meet data governance requirements while satisfying the latency and capability needs of both workloads.
What should you do?

Answer: D

Explanation:
Designing dual-workload AI infrastructure requires aligning infrastructure characteristics with workload patterns and regulatory compliance. The continuous 24/7 data extraction pipeline processes sensitive student data with strict data residency constraints. Deploying an open-weight Gemma model on Google Kubernetes Engine (GKE) within a private VPC guarantees continuous resource availability, satisfies local data residency rules, and maximizes hardware utilization, lowering unit processing costs. Conversely, the academic research workload is intermittent, features unpredictable spikes, and requires long-context multimodal reasoning that small open-weight models cannot support natively. Maintaining dedicated GPU clusters for intermittent research results in expensive idle capacity. Utilizing the managed Gemini API on Agent Platform provides serverless access to frontier multimodal reasoning with large context windows on a pay-as-you-go basis, eliminating infrastructure management while meeting performance demands.
Reference: Evaluating and deploying agentic workflows - Hybrid compute architectures, GKE workload hosting, and serverless managed API utilization.


NEW QUESTION # 43
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: A,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 # 44
You are implementing a continuous evaluation pipeline for an agent built with Agent Development Kit (ADK) that invokes multiple external API tools. During evaluation runs, the agent often selects the wrong tools or executes them in the wrong order, causing errors in multistep workflows. You need to establish automated evaluation gates in your CI/CD pipeline to improve execution reliability before promoting model or prompt changes to production.
What should you do?

Answer: B

Explanation:
In complex multi-tool agentic workflows, functional reliability depends on the correct selection and execution order of intermediate tool calls. When prompt updates or model revisions cause an agent to trigger incorrect tools or scramble execution order, response-level text metrics (such as BLEU or final answer similarity) often fail to detect underlying workflow errors. The Google-recommended CI/CD gating pattern utilizes the Gen AI evaluation service integrated into automated build pipelines (e.g., Cloud Build). The evaluation service compares the agent's intermediate execution steps against a curated golden trajectory dataset using specialized trajectory metrics. Metrics such as Trajectory Exact Match, In-Order Tool Match, and Tool Call Accuracy evaluate whether the agent selected the expected tools and invoked them in the required operational sequence. Thresholds set on these metrics serve as programmatic quality gates, blocking deployment of breaking changes. Model Armor filters content rather than validating tool workflows.
Reference: Evaluating and deploying agentic workflows - Continuous evaluation pipelines, Gen AI evaluation trajectory metrics, and CI/CD deployment gating.


NEW QUESTION # 45
You are deploying a custom AI assistant written in Python with Agent Development Kit (ADK). You want to maximize developer velocity by deploying the agent code with minimal infrastructure plumbing, while ensuring that the agent has built-in, out-of-the-box access to manage conversational session history and memory persistence.
How should you deploy the agent?

Answer: A

Explanation:
Agent Runtime is Google Cloud's fully managed, serverless execution platform built specifically for deploying ADK-based agentic workloads. Deploying an ADK application directly to Agent Runtime maximizes developer velocity by eliminating the operational overhead of provisioning virtual machines, managing container clusters, or configuring custom load balancers. Agent Runtime features native, out-of-the- box integration with Agent Platform Sessions and Agent Platform Memory Bank. The runtime automatically manages conversation lifecycles, persists session state across multi-turn dialogues, and connects to Memory Bank to handle long-term memory extraction and retrieval without requiring custom database persistence layers. Deploying to Cloud Run or Google Kubernetes Engine (GKE) with external BigQuery or Bigtable databases requires designing custom state synchronization middleware, schema migrations, and connection pooling, introducing unnecessary infrastructure plumbing and operational complexity.
Reference: Evaluating and deploying agentic workflows - Agent Runtime managed deployment, Agent Platform Sessions, and Memory Bank integration.


NEW QUESTION # 46
You are designing a human-in-the-loop architecture for an enterprise agent built with Agent Development Kit (ADK) that executes high-risk business operations. You must pause agent execution during sensitive tool invocations, present approval requests out-of-band without risk of prompt injection bypass, and reliably resume execution upon human confirmation.
What should you do?

Answer: B

Explanation:
Executing high-risk operations in autonomous agentic workflows requires strict human-in-the-loop (HITL) authorization guardrails. Allowing the agent's language model to parse conversational approval prompts from the chat stream introduces prompt injection vulnerabilities, where adversarial inputs can manipulate the model into approving unauthorized actions. Under the ADK security architecture, deterministic policy interception is implemented via SecureRunner (and policy enforcement hooks). When a sensitive tool call is evaluated, the runner pauses execution, emits an out-of-band confirmation request, and suspends the workflow while persisting state via VertexAiSessionService. Human compliance officers review the request inside a trusted, authenticated UI external to the conversation stream. Upon explicit approval, the trusted interface returns the exact tool execution call ID to the session runner, resuming the suspended execution context securely. Using natural language confirmations or LLM classification scripts undermines deterministic access governance.
Reference: Securing and governing agentic workflows - Human-in-the-loop (HITL) governance, SecureRunner policy interception, and out-of-band call ID resumption.


NEW QUESTION # 47
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