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Google Professional-Agentic-Architect Exam Syllabus Topics:

Section	Weight	Objectives
Securing and governing agentic workflows	15%	- Configuring agent security and governance 1. Using Agent Gateway to monitor traffic and track agents 2. Configuring Principal Access Boundary policies using Agent Identity 3. Implementing authentication and secure tool execution using OAuth 2.0 4. Implementing agent governance and policy enforcement using Agent Registry and Model Armor - 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
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
Developing custom agents	33%	- 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) - Designing and building agentic workflows in code 1. Building custom agents using open-source libraries such as Agent Development Kit (ADK) 2. Configuring agent sessions, memory, and managed state 3. Configuring agent skills, plugins, and execution modes using Agents CLI 4. Selecting and configuring LLMs and SLMs based on cost, security, and architecture requirements - 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
Evaluating and deploying agentic workflows	22%	- 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 - Evaluating agents in development and production 1. Building continuous evaluation pipelines for agent tool execution 2. Creating test datasets, golden datasets, prompts, and edge cases 3. Using ADK evaluation tools, Gen AI evaluation services, and custom autoraters 4. Evaluating agent responses and retrieval quality against reference datasets
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. Creating system instructions and prompt templates to guide agent behavior 2. Configuring state-based workflows, transitions, and event handlers using Gemini Enterprise Workflow Builder and Customer Experience Agent Studio

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Google Cloud Certified - Professional Agentic Architect Sample Questions (Q31-Q36):

NEW QUESTION # 31
You are evaluating a policy question-answering agent with an Agent Development Kit (ADK) eval set. Each case stores one reference answer, and the ADK eval suite scores responses using the ADK response_match_score criterion. Reviewers have been asked to evaluate the agent's responses and identify acceptable responses that cite the policy section and provide any exception applicable to the prompt.
However, reviewers cannot agree on which answer to store because several substantially different answers are acceptable.
You want ADK evaluations to assign a pass verdict to every response that meets both conditions and a fail verdict to the rest.
What should you do?

A. Score the cases using the ADK final_response_match_v2 criterion against the stored reference answer.
B. Store several reference answers in each ADK eval case.
C. Define evaluation rubrics, and score the responses using the ADK rubric_based_final_response_quality_v1 criterion.
D. Lower the response_match_score threshold that the ADK eval set requires to pass.

Answer: C

Explanation:
Traditional reference-based evaluation metrics (such as response_match_score or final_response_match_v2) rely on semantic similarity to a single ground-truth reference answer. In domain-specific policy QA scenarios where multiple phrasing styles and structural variants are equally valid, forcing comparisons against a static reference string leads to false negatives. Lowering threshold scores only introduces false positives without verifying required content. To evaluate multi-criteria compliance objectively, the ADK evaluation framework supports rubric-based evaluations using the rubric_based_final_response_quality_v1 criterion. Architects define explicit, structured rubrics specifying the required qualitative rules: (1) mandatory citation of the relevant policy section, and (2) clear inclusion of all applicable exceptions. An LLM-as-a-judge autorater evaluates candidate responses directly against these rubrics, assigning a pass verdict only when both conditions are satisfied, irrespective of textual variation from a reference answer.
Reference: Evaluating and deploying agentic workflows - ADK evaluation criteria, rubric_based_final_response_quality_v1, and multi-criteria LLM judging.

NEW QUESTION # 32
You are developing an automated evaluation benchmark suite for a multiturn enterprise support agent that interacts with dynamic backend tools. In your current CI/CD pipeline, static golden datasets frequently produce failure signals because live backend dependencies return state that changes over time, while the current evaluation prompts do not assess tool-trajectory correctness, ambiguous user inputs, or API fault conditions. You need to ensure evaluation fidelity, ambiguity handling, and environment resilience.
What should you do?

A. Replace the trigger-based CI/CD evaluation pipeline with an online LLM-as-a-judge mechanism that evaluates the live agent's trajectory and outputs in production using continuous monitoring.
B. Run trigger-based CI/CD pipelines, and trigger Cloud Monitoring alerts when LLM-as-a-judge response evaluation metrics fall below baseline thresholds during live tool calls.
C. Use static simulated tool responses to decouple evaluation from dynamic backend state changes, and construct test sets in the gen AI evaluation service.
D. Deploy automated human-in-the-loop review tasks using Agent Designer to evaluate expected outputs when backend API dependencies return variable tool states.

Answer: C

Explanation:
Reliable benchmark suites require deterministic testing environments combined with comprehensive edge- case coverage. When evaluation pipelines execute against live backend APIs, fluctuating external state generates spurious failure signals that obscure actual code or prompt regressions. To establish environment resilience, the evaluation architecture must utilize static simulated tool responses (mocked responses or tool stubs), decoupling the evaluation run from dynamic backend environments. In parallel, teams construct structured test sets within the Gen AI evaluation service designed to assess edge cases: trajectory correctness, ambiguous user requests, and tool error handling (e.g., simulating HTTP 500 or timeout responses). This allows the evaluation framework to verify that the agent requests clarification on ambiguity, handles tool exceptions gracefully, and follows expected reasoning trajectories before production deployment. Live online LLM monitoring evaluates production post-facto without preventing breaking builds, and human-in-the-loop reviews introduce unscalable bottlenecks.
Reference: Evaluating and deploying agentic workflows - Benchmark suite design, Gen AI evaluation service test sets, simulated tool responses, and resilience testing.

NEW QUESTION # 33
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?

A. Create an MCP server, and connect Antigravity to the registry using the MCP.
B. Define a custom Antigravity subagent whose role is endpoint lookup.
C. Add an Antigravity PostToolUse hook that checks generated calls against the registry.
D. Add the endpoint list to an Antigravity workspace rule.

Answer: A

NEW QUESTION # 34
You are preparing to deploy an enterprise technical troubleshooting agent built with Agent Development Kit (ADK). Your company's QA team has compiled 500 representative customer support test cases paired with authoritative reference documents and expected answer trajectories. You need to establish a continuous integration testing process to measure document retrieval precision and answer faithfulness before releasing software updates.
How should you configure the evaluation pipelines in ADK using the agents-cli eval run command?

A. Assess model response trajectories using semantic BLEU and ROUGE n-gram overlap scoring against reference answers.
B. Measure task trajectory completion rates and exact-match tool call argument accuracy against baseline test logs.
C. Measure context precision and recall for documents against the ground truth.
D. Implement a scoring script that computes raw cosine similarity between embedding vectors of agent responses and reference answers.

Answer: C

Explanation:
Establishing a robust CI/CD evaluation pipeline for retrieval-augmented agents requires measuring the quality of the underlying information retrieval pipeline alongside answer fidelity. When evaluating an ADK agent using agents-cli eval run, the pipeline benchmarks candidate builds against a curated test dataset containing ground-truth source documents. Measuring context precision evaluates whether retrieved document chunks are relevant to the query and ranked appropriately near the top of the context payload. Measuring context recall assesses whether all necessary reference documents required to answer the query were successfully retrieved by the agent's search tools. Together, context precision and recall establish the baseline retrieval accuracy required for answer faithfulness. Cosine similarity, BLEU, and ROUGE metrics measure superficial token overlap or embedding closeness rather than evaluating retrieval precision or context coverage.
Reference: Evaluating and deploying agentic workflows - ADK evaluation framework, agents-cli eval run, context precision, and context recall metrics.

NEW QUESTION # 35
You are setting up Claude Code on Google Cloud to help developers refactor microservices. You have created a custom tool service using an MCP server that runs locally through standard input/output (stdio). You need to configure Claude Code so that the coding agent automatically discovers and executes the tools exposed by the MCP server during interactive sessions.
What should you do?

A. Export the tool function signatures as a JSON-encoded string in the CLAUDE_CODE_TOOLS environment variable before launching the CLI.
B. Define the custom tool definitions in a SKILLS.md file in the project's root directory.
C. Add the MCP server command field with executable code and arguments under the mcpServers object in the .claude.json file.
D. Register the MCP server to Agent Registry using Claude Code console.

Answer: C

Explanation:
Claude Code integrates local and remote tools via the Model Context Protocol (MCP). To register a local MCP server utilizing standard input/output (stdio) transport, the configuration must be declared in the mcpServers object within the ~/.claude.json configuration file. The definition requires the executable path in the command field and its corresponding parameters within the args array. When Claude Code initializes an interactive session, it launches the declared server process, discovers exposed tool declarations over the stdio pipe, and binds them to the model's function-calling registry, enabling the agent to execute refactoring tools securely.
Reference: Using coding agents for application development - Claude Code MCP integration, stdio transport configuration, and tool discovery schemas.

NEW QUESTION # 36
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