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Google Professional-Agentic-Architect Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Securing and governing agentic workflows	15%	- 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 - 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
Topic 2: Using coding agents for application development	17%	- Using coding agents effectively 1. Configuring coding agents with MCP servers, custom skills, and tools 2. Using secure sandboxes for coding agents with GKE, Cloud Workstations, and Antigravity 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
Topic 3: Evaluating and deploying agentic workflows	22%	- Deploying and scaling production workloads 1. Monitoring and optimizing agent performance, reliability, latency, and cost 2. Troubleshooting agent drift, tool invocation latency, reasoning loops, and system failures 3. Selecting deployment environments including Agent Runtime, Cloud Run, and GKE - Evaluating agents in development and production 1. Creating test datasets, golden datasets, prompts, and edge cases 2. Evaluating agent responses and retrieval quality against reference datasets 3. Using ADK evaluation tools, Gen AI evaluation services, and custom autoraters 4. Building continuous evaluation pipelines for agent tool execution
Topic 4: Building agents using low-code tools	13%	- 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 - 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
Topic 5: 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 - 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 - Designing and building agentic workflows in code 1. Configuring agent sessions, memory, and managed state 2. Configuring agent skills, plugins, and execution modes using Agents CLI 3. Selecting and configuring LLMs and SLMs based on cost, security, and architecture requirements 4. Building custom agents using open-source libraries such as Agent Development Kit (ADK)

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Google Cloud Certified - Professional Agentic Architect Sample Questions (Q11-Q16):

NEW QUESTION # 11
You are building a custom customer service agent and have compiled a golden dataset of historical user questions and their ideal answers. Before deploying the agent, you need to programmatically evaluate both the relevance of retrieved documents and the groundedness of the agent's generated responses. You want to minimize custom evaluation code.
What should you do?

A. Execute the evaluation of the golden dataset against the agent using ADK eval set tooling, and configure LLM-based autorater metrics.
B. Deploy the agent to Agent Runtime, run the agent against the golden dataset, and review the metrics using Google Cloud Observability.
C. Run a batch evaluation job against the agent using the golden dataset to calculate BLEU and ROUGE scores for the generated responses.
D. Configure a continuous evaluation pipeline using the Agent Search ranking API to compare the semantic similarity between the agent's generated response and the golden dataset's ideal answer.

Answer: A

Explanation:
Assessing retrieval-augmented conversational agents requires evaluating both the retrieval pipeline (context relevance) and generative synthesis (response groundedness). Implementing custom evaluation scripts or relying on lexical metrics (BLEU/ROUGE) fails to capture semantic fidelity and context alignment. The Agent Development Kit (ADK) provides native eval set tooling designed for automated benchmarking against golden datasets with minimal custom code. By executing evaluations via ADK eval tools, architects configure managed, LLM-based autorater metrics. These autoraters evaluate intermediate retrieval context to grade whether fetched documents are relevant to the user query, and evaluate final response tokens against source documents to score groundedness (faithfulness) and detect ungrounded hallucinations. This provides automated, multi-dimensional assessment within a unified framework before promoting changes to production. The Ranking API reranks chunks rather than evaluating groundedness, and Google Cloud Observability tracks runtime infrastructure telemetry rather than semantic retrieval fidelity.
Reference: Evaluating and deploying agentic workflows - ADK eval set tooling, LLM-based autorater metrics, context relevance, and groundedness scoring.

NEW QUESTION # 12
You are configuring a secure agent-to-tool API integration for a financial agent on Gemini Enterprise Agent Platform. The agent calls an internal payment processing microservice protected by OAuth 2.0.
To comply with strict enterprise security policy, you must ensure that:
OAuth client credentials used for service-to-service tool authentication are encrypted at rest and never hardcoded in tool definitions or repository source code.
Tool execution is strictly limited to the minimum required API permissions, so the agent cannot perform unauthorized administrative operations on the payment microservice.
What should you do?
Choose two

A. Configure the tool definition to use OAUTH2_IMPLICIT_GRANT with wildcard scopes (*).
B. Store the OAuth client credentials in Secret Manager, and reference the Secret Manager resource URI within the Agent Identity auth manager provider configuration.
C. Encode the OAuth client secret in Base64, and pass the secret in the tools.py environment variable map.
D. Define explicit OAuth scopes such as payments.read and payments.create in the auth provider definition, and bind the provider to the specific payment tool definition in Agent Runtime.
E. Grant the roles/owner IAM role on the payment microservice project to the agent's identity.

Answer: B,D

Explanation:
Enterprise security governance requires strict separation of credentials, encryption at rest, and least-privilege permission scoping for autonomous agent tool invocations. Hardcoding credentials or encoding secrets in Base64 within environment variable files violates compliance standards and exposes sensitive keys in repository histories. To satisfy credential protection requirements, client secrets and certificates must be stored in Secret Manager, with their resource URIs linked to the Agent Identity auth manager configuration.
Agent Identity auth manager resolves secrets securely at runtime to obtain OAuth tokens, preventing credentials from being exposed in application code. To enforce least privilege and prevent unauthorized operations on downstream payment APIs, the auth provider definition must declare explicit OAuth 2.0 scopes (e.g., payments.read and payments.create). Binding this auth provider directly to the specific payment tool definition in Agent Runtime restricts the token's authorization boundary strictly to necessary operations, blocking wildcard access and administrative abuse.
Reference: Securing and governing agentic workflows - Agent Identity auth manager, Secret Manager credential integration, and OAuth 2.0 scope bounding.

NEW QUESTION # 13
You are evaluating a multitool customer support agent built with Agent Development Kit (ADK) against a curated golden dataset of complex, multiturn user queries. During initial testing, the agent often returns plausible final answers but skips intermediate knowledge retrieval steps or produces responses unsupported by retrieved context, leading to ungrounded claims in production. You need to systematically isolate retrieval quality failures from response generation errors while preventing regressions in tool sequence trajectory accuracy.
What should you do?

A. Deploy a Cloud Monitoring dashboard linked to Cloud Logging telemetry to alert on low vector similarity scores from Agent Search retrieval calls. Execute a gen AI evaluation service EvalTask.
B. Configure supervised fine-tuning using the golden dataset as training pairs to enforce deterministic tool call parameters and eliminate ungrounded response generation. Execute a gen AI evaluation service EvalTask.
C. Implement Agent Designer guardrails with mandatory retrieval filters to automatically drop agent responses that do not achieve high lexical similarity to reference documents. Add a human-in-the-loop approval workflow.
D. Execute a gen AI evaluation service EvalTask using the golden dataset, and specify Groundedness metrics alongside trajectory sequence match metrics to separately evaluate retrieval context fidelity and tool call execution.

Answer: D

Explanation:
Evaluating multiturn, tool-augmented agents requires decoupling retrieval fidelity from intermediate reasoning and tool execution trajectories. When an agent outputs plausible text while skipping retrieval or hallucinating facts, standard end-to-end response evaluation metrics fail to identify the breakdown point. The Gen AI evaluation service supports running an EvalTask that benchmarks the agent against a curated golden dataset using multi-dimensional metrics. Groundedness metrics evaluate whether the generated output is factually supported by the retrieved context, isolating generation hallucinations. Simultaneously, trajectory sequence match metrics (such as tool trajectory exact match and tool call precision) evaluate whether the agent invoked the required retrieval tools in the correct sequence before formulating an answer. Combining these autoraters distinguishes retrieval failures from generation errors and detects trajectory regressions. Fine- tuning models solely to enforce tool sequencing reduces reasoning adaptability.
Reference: Evaluating and deploying agentic workflows - Gen AI evaluation EvalTask, Groundedness autoraters, and trajectory sequence evaluation.

NEW QUESTION # 14
You are configuring an agentic workflow in Agent Studio for an enterprise customer analytics platform. The agent was initialized with default system instructions, prompt templates, and a Gemini Flash model family. In staging, you notice that the agent frequently hallucinates when synthesizing data across multiple knowledge documents and produces inconsistent output formats.
You need to ensure that the agent reliably uses verified source context and follows a standardized response format.
What should you do first?

A. Update the system instructions to route date parsing requests to a custom Cloud Run OpenAPI extension tool, and reconfigure prompt templates to increase chunk overlap in Agent Search.
B. Update the system instructions to reduce the temperature parameter and maximum top-p sampling to zero, and change the model to a Gemini Pro model family.
C. Update the system instructions to restrict answers strictly to retrieved source data, and reconfigure prompts with chain-of-thought reasoning steps and few-shot input-output examples.
D. Update the system instructions with explicit data redaction rules, and configure prompt templates to send all output through a Model Armor filter for schema enforcement.

Answer: C

Explanation:
Factual hallucinations and schema inconsistencies during multi-document synthesis are primarily addressed by bounding the model's operational scope. Updating system instructions to explicitly mandate that all claims derive strictly from retrieved context-and instructing the model to declare when context is insufficient- establishes a grounded factual boundary. Reconfiguring prompt templates with chain-of-thought (CoT) reasoning guides the model through intermediate analytical steps across diverse source snippets. Providing few-shot input-output demonstrations sets concrete structural precedents for response formatting, enabling smaller, low-latency models like Gemini Flash to adhere to output contracts. Adjusting temperature or top-p to zero limits sampling diversity but fails to enforce syntactic output constraints or prevent parametric hallucinations. Model Armor evaluates content safety and sensitive data rather than structural schema compliance. Therefore, structured grounding instructions and few-shot CoT exemplars represent the foundational first step.
Reference: Building agents using low-code tools - Grounding agent responses, system instruction constraints, and few-shot schema enforcement.

NEW QUESTION # 15
You are developing an enterprise customer support agent in Customer Experience Agent Studio (CX Agent Studio). The agent handles an order cancellation workflow that requires customer identity verification before invoking an external payment refund tool. During testing, when end users submit complex, multi-sentence requests, the agent frequently skips the intermediate identity verification step and calls the refund tool prematurely.
You need to configure the agent's behavior to enforce strict, sequential execution of every workflow step.
What should you do?

A. Attach a Model Armor security template to the endpoint to sanitize user conversational inputs.
B. Restructure in-console system instructions in CX Agent Studio into XML tags.
C. Configure a backward handoff rule on the agent node using a boolean variable check condition to handle transfer errors.
D. Define global instructions in CX Agent Studio's advanced application settings with broad behavioral constraints to apply to every conversational turn.

Answer: B

Explanation:
Generative agents in CX Agent Studio parse natural language system prompts to guide task execution. When user utterances contain multiple intents, models can skip intermediate operational steps if the workflow instructions lack clear syntactic structure. Restructuring system instructions using explicit XML tags (e.g., < workflow > , < step > , < prerequisites > , < action > ) establishes rigid boundaries that Gemini foundation models parse with high fidelity. Encapsulating the verification step within explicit prerequisite tags and specifying that tool execution is gated on verification success forces the model to respect the required sequential order. Unstructured global text instructions are easily bypassed in complex multi-intent dialogues.
Model Armor provides content safety filtering rather than execution-flow enforcement, and backward handoff rules handle transfer exceptions rather than intra-agent sequence constraints.
Reference: Building agents using low-code tools - CX Agent Studio instruction engineering, XML schema structuring, and deterministic workflow gating.

NEW QUESTION # 16
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