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Google Professional-Agentic-Architect Exam Syllabus Topics:

Section	Weight	Objectives
Evaluating and deploying agentic workflows	22%	- Evaluating agents in development and production 1. Evaluating agent responses and retrieval quality against reference datasets 2. Using ADK evaluation tools, Gen AI evaluation services, and custom autoraters 3. Creating test datasets, golden datasets, prompts, and edge cases 4. Building continuous evaluation pipelines for agent tool execution - Deploying and scaling production workloads 1. Troubleshooting agent drift, tool invocation latency, reasoning loops, and system failures 2. Monitoring and optimizing agent performance, reliability, latency, and cost 3. Selecting deployment environments including Agent Runtime, Cloud Run, and GKE
Building agents using low-code tools	13%	- Connecting enterprise data to Gemini Enterprise 1. Ingesting and processing unstructured multimodal data including video, audio, and images 2. Connecting agents securely to enterprise data sources using Gemini Enterprise and Agent Search - 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
Developing custom agents	33%	- Orchestrating and coordinating agentic workflows 1. Implementing agentic protocols including MCP and Agent2Agent (A2A) 2. Designing multi-agent handoffs and sequential, parallel, and graph-based workflows - Designing and building agentic workflows in code 1. Configuring agent skills, plugins, and execution modes using Agents CLI 2. Building custom agents using open-source libraries such as Agent Development Kit (ADK) 3. Configuring agent sessions, memory, and managed state 4. Selecting and configuring LLMs and SLMs based on cost, security, and architecture requirements - Integrating enterprise domain knowledge 1. Integrating databases, APIs, third-party SaaS tools, and MCP servers 2. Designing RAG pipelines, embeddings, vector retrieval, similarity scoring, and reranking 3. Configuring agent permissions and Agent Identity
Using coding agents for application development	17%	- Customizing coding agents for enterprise workflows 1. Using Agents CLI to build, scale, govern, and optimize deployed agents 2. Creating skills, plugins, extension hooks, rules, and subagents using Antigravity - Using coding agents effectively 1. Refactoring source code, optimizing execution runtimes, and patching application vulnerabilities 2. Configuring coding agents with MCP servers, custom skills, and tools 3. Using secure sandboxes for coding agents with GKE, Cloud Workstations, and Antigravity
Securing and governing agentic workflows	15%	- Configuring agent security and governance 1. Using Agent Gateway to monitor traffic and track agents 2. Implementing authentication and secure tool execution using OAuth 2.0 3. Implementing agent governance and policy enforcement using Agent Registry and Model Armor 4. Configuring Principal Access Boundary policies using Agent Identity - Implementing secure agent behavior and execution 1. Configuring secure data access and identity propagation 2. Designing safety frameworks, guardrails, and human-in-the-loop controls

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Google Cloud Certified - Professional Agentic Architect Sample Questions (Q18-Q23):

NEW QUESTION # 18
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?

A. Configure Cloud Key Management Service (Cloud KMS) to encrypt third-party API credentials in backend configuration files, and grant the Cloud KMS Decrypter IAM role to the agent's compute service account.
B. Attach a service account to the ADK agent runtime for BigQuery access, and store third-party API keys in Secret Manager for backend code to retrieve during tool execution.
C. Enable Identity-Aware Proxy (IAP) on the Agent Platform host to secure BigQuery access, and configure OAuth 2.0 client IDs to broker outbound tokens for external APIs.
D. Assign IAM permissions for BigQuery to the agent's Secure Production Identity Framework for Everyone (SPIFFE)-based agent identity, and store third-party credentials in Agent Identity auth manager to let ADK handle token retrieval and header injection.

Answer: D

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 # 19
You are designing a multiagent workflow with Agent Development Kit (ADK) on Agent Runtime that includes an orchestrator agent and an interactive claim adjuster agent. The claim adjuster agent is currently implemented as an agent-as-a-tool, but it treats turns as isolated requests, loses conversation history, and cannot hand control back to the orchestrator agent.
You need to rearchitect the coordination pattern so that the claim adjuster maintains stateful multiturn history, accesses shared context, and supports fluid, bidirectional handoffs.
What should you do?

A. Deploy the claim adjuster agent as a stateful Agent Runtime instance, and configure Agent Identity auth manager to pass session state tokens in the HTTP payload body.
B. Configure the agent-as-a-tool execution policy with an asynchronous polling webhook that continuously queries the Agent Registry for updated stateful session context logs.
C. Enable use_shared_memory=True in the tool parameter schema to force the tool container to inherit the orchestrator agent's active session history.
D. Rearchitect the claim adjuster agent as a stateful subagent within a hierarchical multiagent graph workflow, enabling shared session state and model-driven delegation for context preservation and bidirectional handoffs.

Answer: D

Explanation:
In the Agent Development Kit (ADK) architecture, implementing an agent as a tool ('agent-as-a-tool') encapsulates the nested agent inside a stateless function call boundary. Under this pattern, each invocation is treated as an isolated request: the parent orchestrator cannot maintain a continuous dialogue, intermediate context is discarded upon return, and bidirectional control transfer is impossible. To achieve fluid, multiturn collaboration where the claim adjuster agent remembers historical customer interactions and accesses shared variables, the architecture must transition to a hierarchical multiagent graph workflow. In this topology, the claim adjuster is defined as a stateful subagent that shares a persistent session state with the orchestrator.
ADK's model-driven delegation mechanisms enable dynamic control handoffs between the orchestrator and the subagent, allowing conversations to flow bidirectionally while retaining complete conversational context and turn history across session events. Polling webhooks, custom tokens, or arbitrary tool parameters fail to provide native graph coordination.
Reference: Developing custom agents - Multiagent graph architectures, hierarchical subagents, state sharing, and bidirectional handoff patterns.

NEW QUESTION # 20
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?

A. Query Cloud Logging for the Cloud Run service, and aggregate the httpRequest.latency field to calculate the average response time of the agent.
B. Configure the gen AI evaluation service to execute a batch latency test against a golden dataset to find the slowest prompt.
C. Instrument the ADK agent with OpenTelemetry to export distributed traces to Cloud Trace, and analyze the individual trace spans.
D. Enable Model Monitoring on the deployed agent to track prediction latency anomalies and feature drift across the LLM.

Answer: C

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 # 21
You are building a technical support agent with Gemini Enterprise Agent Platform to help internal field engineers. You need to ground the agent's responses strictly in internal proprietary equipment manuals. You want to minimize hallucinations and maximize response accuracy.
What should you do?

A. Enable the googleSearch tool in the agent's runtime setup.
B. Export the proprietary manuals to a relational database, and define a custom Python FunctionTool to fetch document snippets.
C. Convert the proprietary manuals into structured JSON arrays, and inject them as system_instructions.
D. Implement a retrieval tool in the agent's configuration, and point the vertex_ai_search parameter to the resource path of your Agent Search data store.

Answer: D

Explanation:
Grounding custom agents in authoritative enterprise documentation requires integrating managed retrieval- augmented generation (RAG) architectures. On the Gemini Enterprise Agent Platform, Agent Search (Vertex AI Search) ingests, parses, chunks, and indexes unstructured proprietary documentation-such as complex technical manuals and PDF schematics-into an enterprise data store. To link this corpus to an agent, developers declare a retrieval tool within the agent configuration and set the vertex_ai_search parameter to the fully qualified resource path of the target data store. When field engineers submit troubleshooting queries, the agent queries the data store, retrieves semantically relevant manual excerpts, and passes the grounded context to the underlying Gemini foundation model. This restricts response generation to verified reference passages, mitigating parametric hallucinations and improving operational accuracy. Injecting manuals into system_instructions exhausts the context window and degrades reasoning, while enabling public web search fails to ground the model on private, proprietary equipment specifications.
Reference: Developing custom agents - Grounding mechanisms, Agent Search enterprise data stores, and retrieval tool configuration.

NEW QUESTION # 22
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?

A. Build a hybrid architecture. Use a generative steering node to classify intent. Route account-sensitive queries to a deterministic flow component for PIN/OTP authentication. Pass the verified customer ID as a session parameter to the runtime.
B. Build a single generative agent. Use system instructions to classify intent. Use prompt-based guardrails and the model's memory to enforce authentication before running troubleshooting steps. Execute a custom webhook tool to handle the OTP API call.
C. Build a single generative agent with authentication and troubleshooting components configured as parallel tools. When a customer requests account-sensitive help, execute both tools concurrently to minimize latency, and configure safety filters to redact any diagnostic output if authentication fails.
D. Build a sequential cascade of two independent generative agents. Configure the first agent to handle intent classification and the OTP verification, and store state in an external database. Have the second agent poll this database for customer ID and continue the conversation, triggering a custom session initiation protocol (SIP) transfer webhook if needed.

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 # 23
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