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Google Professional-Agentic-Architect Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Developing custom agents33%- Designing and building agentic workflows in code
  • 1. Building custom agents using open-source libraries such as Agent Development Kit (ADK)
    • 2. Selecting and configuring LLMs and SLMs based on cost, security, and architecture requirements
      • 3. Configuring agent skills, plugins, and execution modes using Agents CLI
        • 4. Configuring agent sessions, memory, and managed state
          - Integrating enterprise domain knowledge
          • 1. Configuring agent permissions and Agent Identity
            • 2. Designing RAG pipelines, embeddings, vector retrieval, similarity scoring, and reranking
              • 3. Integrating databases, APIs, third-party SaaS tools, and MCP servers
                - 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)
                    Topic 2: Building agents using low-code tools13%- 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
                        - Connecting enterprise data to Gemini Enterprise
                        • 1. Connecting agents securely to enterprise data sources using Gemini Enterprise and Agent Search
                          • 2. Ingesting and processing unstructured multimodal data including video, audio, and images
                            Topic 3: Securing and governing agentic workflows15%- Configuring agent security and governance
                            • 1. Implementing agent governance and policy enforcement using Agent Registry and Model Armor
                              • 2. Configuring Principal Access Boundary policies using Agent Identity
                                • 3. Implementing authentication and secure tool execution using OAuth 2.0
                                  • 4. Using Agent Gateway to monitor traffic and track agents
                                    - Implementing secure agent behavior and execution
                                    • 1. Designing safety frameworks, guardrails, and human-in-the-loop controls
                                      • 2. Configuring secure data access and identity propagation
                                        Topic 4: Evaluating and deploying agentic workflows22%- Evaluating agents in development and production
                                        • 1. Evaluating agent responses and retrieval quality against reference datasets
                                          • 2. Creating test datasets, golden datasets, prompts, and edge cases
                                            • 3. Building continuous evaluation pipelines for agent tool execution
                                              • 4. Using ADK evaluation tools, Gen AI evaluation services, and custom autoraters
                                                - Deploying and scaling production workloads
                                                • 1. Selecting deployment environments including Agent Runtime, Cloud Run, and GKE
                                                  • 2. Troubleshooting agent drift, tool invocation latency, reasoning loops, and system failures
                                                    • 3. Monitoring and optimizing agent performance, reliability, latency, and cost
                                                      Topic 5: Using coding agents for application development17%- 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
                                                            - Customizing coding agents for enterprise workflows
                                                            • 1. Creating skills, plugins, extension hooks, rules, and subagents using Antigravity
                                                              • 2. Using Agents CLI to build, scale, govern, and optimize deployed agents

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                                                                Google Cloud Certified - Professional Agentic Architect Sample Questions (Q43-Q48):

                                                                NEW QUESTION # 43
                                                                You are designing authentication controls for an Agent Development Kit (ADK) agent on Gemini Enterprise Agent Platform. The agent queries internal BigQuery datasets and will soon expand to call external third-party APIs. You want to enable secure, granular outbound tool authentication without managing custom credential vaults or hardcoding secrets in backend code.
                                                                What should you do?

                                                                Answer: A

                                                                Explanation:
                                                                Secure outbound tool integration in modern agent platforms requires identity-first authentication and managed credential brokering. The Gemini Enterprise Agent Platform utilizes SPIFFE-based (Secure Production Identity Framework for Everyone) agent identities, providing cryptographic workload identities directly to ADK agents. For native Google Cloud resources like BigQuery, architects grant required IAM permissions directly to the agent's SPIFFE-based identity principal, establishing access without managing long-lived service account keys. For external third-party APIs, Agent Identity auth manager acts as a managed credential vault and token broker. External API keys and OAuth tokens are registered within the auth manager; during tool execution, ADK coordinates with the auth manager to automatically fetch valid tokens and inject authentication headers into outbound requests. This eliminates custom vault management code, prevents hardcoded secrets, and maintains a secure identity boundary across both first-party and third-party tools.
                                                                Reference: Securing and governing agentic workflows - SPIFFE agent identities, Agent Identity auth manager, and automated token injection.


                                                                NEW QUESTION # 44
                                                                You have deployed a custom, multistep financial agent on Cloud Run. The agent queries a Vector Search index, calls an external stock market API, and uses a Gemini LLM to synthesize an answer, in that order. In production, you find that responses have intermittent latency spikes. You need to identify which step in the agent's execution workflow is causing the bottleneck.
                                                                What should you do?

                                                                Answer: D

                                                                Explanation:
                                                                In a multistep agentic workflow hosted on Cloud Run, request execution alternates sequentially through disparate distributed components: vector retrieval, third-party network APIs, and generative model inference.
                                                                Coarse-grained metrics like Cloud Run's httpRequest.latency report only gross container execution duration, providing no diagnostic visibility into internal processing phases. To isolate intermittent latency spikes, the agent must be instrumented with OpenTelemetry to export distributed traces to Cloud Trace. OpenTelemetry automatically segments the execution path into discrete, contextualized trace spans representing the Vector Search query, the external stock market API call, and the Gemini inference call. By inspecting these individual spans within Cloud Trace for anomalous, high-latency traces, architects can definitively identify which sub-operation caused the delay. Running offline batch latency tests in the evaluation service evaluates pre-production datasets rather than transient live infrastructure conditions, while Model Monitoring tracks parametric drift and serving anomalies rather than inter-service distributed execution latencies.
                                                                Reference: Evaluating and deploying agentic workflows - Cloud Run agent observability, OpenTelemetry instrumentation, Cloud Trace spans, and performance bottleneck isolation.


                                                                NEW QUESTION # 45
                                                                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: A

                                                                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 # 46
                                                                Your organization maintains an internal service registry listing the endpoints exposed by teams across the organization. Endpoints are added and removed throughout the day. Your team uses Antigravity to write client code for the endpoints. You discover that the agent is generating code that references stale, decommissioned endpoints. You need Antigravity to dynamically verify active endpoints from the registry in real time while generating code.
                                                                What should you do?

                                                                Answer: B

                                                                Explanation:
                                                                Static context configurations (such as workspace rules or markdown documentation) cannot reflect high- velocity environment changes where microservice endpoints mutate throughout the day. The Model Context Protocol (MCP) provides a standardized, open architecture for exposing live tools and dynamic enterprise data to AI coding assistants. By deploying an MCP server that interfaces with the internal service registry, Antigravity integrates dynamic endpoint lookup capabilities directly into its tool schema. When generating client stubs, the agent autonomously queries the MCP server over standard transports to retrieve current, active service contracts. PostToolUse hooks execute only after tool actions occur and cannot proactively ground code generation, while static rules lead directly to context staleness.
                                                                Reference: Using coding agents for application development - Model Context Protocol (MCP) tool integration, live context grounding, and dynamic service catalogs.


                                                                NEW QUESTION # 47
                                                                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: B

                                                                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 # 48
                                                                ......

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