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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. Implementing agent governance and policy enforcement using Agent Registry and Model Armor 2. Implementing authentication and secure tool execution using OAuth 2.0 3. Using Agent Gateway to monitor traffic and track agents 4. Configuring Principal Access Boundary policies using Agent Identity - 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
Evaluating and deploying agentic workflows	22%	- Deploying and scaling production workloads 1. Selecting deployment environments including Agent Runtime, Cloud Run, and GKE 2. Monitoring and optimizing agent performance, reliability, latency, and cost 3. Troubleshooting agent drift, tool invocation latency, reasoning loops, and system failures - Evaluating agents in development and production 1. Building continuous evaluation pipelines for agent tool execution 2. Evaluating agent responses and retrieval quality against reference datasets 3. Using ADK evaluation tools, Gen AI evaluation services, and custom autoraters 4. Creating test datasets, golden datasets, prompts, and edge cases
Developing custom agents	33%	- 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. Selecting and configuring LLMs and SLMs based on cost, security, and architecture requirements 4. Configuring agent skills, plugins, and execution modes using Agents CLI - 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. 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. 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
Building agents using low-code tools	13%	- 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. 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

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

NEW QUESTION # 18
You are developing an autonomous agent flow deployed on Gemini Enterprise Agent Platform that executes function calling tools against backend cloud APIs. Changes to the API schemas have caused intermittent tool execution failures, leading the agent to generate invalid parameter payloads. You need to improve your diagnostics, identify regressions, and establish quality metrics to benchmark full user interactions.
What should you do?

A. Create custom Cloud Logging log metrics to extract function calling HTTP 400 error codes from production agent trace logs, route the metrics to BigQuery, and trigger Cloud Monitoring alert policies when tool execution failure rates cross established thresholds.
B. Deploy a Model Armor security template on the agent runtime to validate tool schema compliance, inspect request payloads for malformed JSON parameters, and sanitize invalid function arguments before API invocation.
C. Build an automated evaluation workflow using the gen AI evaluation service to evaluate the agent's final text responses using pointwise LLM-as-a-judge and ROUGE-L similarity metrics against ground- truth response datasets.
D. Configure a continuous evaluation pipeline using the gen AI evaluation service to compare tool call histories against reference trajectories using trajectory metrics, and log the results in Experiments on Agent Platform.

Answer: D

Explanation:
API schema drift causes agents to generate malformed JSON structures, invalid parameter keys, or out-of- order tool calls. Benchmarking full user interactions and establishing regression baselines requires structured evaluation tracking. The Gen AI evaluation service provides continuous evaluation pipelines that compare the agent's full tool-calling history against reference trajectories using formal trajectory metrics. These metrics assess tool selection precision, argument validity, and parameter schema compliance across multi-turn interactions. Logging evaluation runs to Experiments on the Agent Platform provides visual tracking, allowing engineering teams to benchmark versioned prompts and schemas, detect quality regressions, and analyze diagnostic traces across model iterations. Cloud Logging and Monitoring alert on runtime errors after failures occur in production but do not provide benchmark metrics for pre-deployment regression testing.
Model Armor scans content safety rather than resolving API schema compliance.
Reference: Evaluating and deploying agentic workflows - Gen AI evaluation service, Experiments tracking, trajectory metrics, and regression diagnostics.

NEW QUESTION # 19
You are designing a multiagent system on Google Cloud in which multiple autonomous agents deployed in Agent Runtime collaborate through a graph workflow and pass context along sequential task routes. You want to ensure that each agent operates with least-privilege access and that its specific actions can be traced independently.
What should you do?

A. Configure a dedicated service account for each agent in Agent Runtime, grant least-privilege IAM roles to each service account for target resources, and use standard Cloud Audit Logs for auditability.
B. Store API keys in Secret Manager, and enable agents to retrieve keys to authorize communications.
C. Create a unique agent identity for each agent in Agent Runtime, register the agents in the Agent Registry, and grant the agent identity principal the necessary IAM roles for specific endpoints.
D. Use Agent Development Kit (ADK) local session state using the ToolContext parameter to dynamically store and propagate user authorization credentials in memory.

Answer: C

Explanation:
In graph-based multiagent systems where tasks transition sequentially across distinct agents, sharing a single service account or passing monolithic credentials violates least privilege and obscures attribution. Agent Runtime provides native support for Agent Identity, enabling each deployed agent instance to receive a unique cryptographic identity principal. By registering each agent within Agent Registry and assigning distinct IAM role bindings to each agent's identity, architects enforce granular least privilege. Each agent possesses permissions only for its specific backend endpoints and tools. Furthermore, using dedicated Agent Identities ensures that all inter-agent calls and tool invocations generate distinct audit traces in Cloud Audit Logs, attributing actions directly to the specific executing subagent. Propagating shared user credentials via local session state introduces security risks and privilege escalation, while API keys lack identity-based access control and granular auditable attribution.
Reference: Securing and governing agentic workflows - Agent Identity, Agent Runtime least privilege, and Agent Registry capability registration.

NEW QUESTION # 20
You are troubleshooting a hierarchical multiagent architecture built with Agent Development Kit (ADK), in which a primary orchestrator delegates complex search tasks to specialized subagents. Execution traces show that subagents append their full tool execution histories to the orchestrator's context window across multiturn exchanges, causing rapid context window bloat, agent drift, and severe tool invocation latency. You need to isolate subagent execution history and prevent context pollution in the orchestrator.
What should you do?

A. Configure a Pub/Sub topic to decouple subagent events from the orchestrator, and configure the orchestrator agent in Agent Runtime to prevent transferring control back to a parent agent.
B. Increase the maximum iterations limit on the orchestrator's looping agent definition, and configure Cloud Trace sampling to automatically drop duplicate tool payload traces.
C. Wrap subagents using agent tool encapsulation, configure the subagent LLM settings to exclude previous conversation contents, and persist subagent findings to the session state.
D. Deploy subagents inside a sequential pipeline, and enable summarization compaction on the orchestrator's Cloud Storage log bucket in Agent Designer.

Answer: C

Explanation:
Hierarchical multiagent systems frequently suffer from context pollution when subagents append raw, voluminous tool execution histories and intermediate scratchpad traces directly into the parent orchestrator's context window. Over multiple turns, this leads to rapid token bloat, increased inference latency, and cognitive drift in the orchestrator. To solve this in Agent Development Kit (ADK), specialized subagents should be wrapped using agent tool encapsulation (AgentTool), which executes the subagent within an isolated, nested sub-session. By configuring the subagent's language model settings with include_contents='none', the runtime prevents prior conversational history and internal tool execution events from being reflected into subsequent reasoning loops. Essential intermediate findings, extracted parameters, or final task outputs are selectively written to the shared session state rather than accumulating unpruned messages in the active prompt context. Compacting logs via Cloud Storage or adjusting tracing sampling rates only affects telemetry, failing to reduce the orchestrator's in-memory prompt size.
Reference: Developing custom agents - Context boundary management, AgentTool encapsulation, and session state persistence.

NEW QUESTION # 21
You are configuring Agent Platform Memory Bank for a wealth management advisor agent deployed on Gemini Enterprise Agent Platform.
During the initial evaluation, you observe that Memory Bank creates duplicate and conflicting memory entries when users update their investment risk tolerance. You need to configure Memory Bank to properly consolidate updated user preferences over time, resolve conflicting information, and restrict memory extraction strictly to relevant personal information and user preferences.
What should you do?
Choose two

A. Disable identity scoping on the Memory Bank instance to do a similarity search for user information during memory consolidation.
B. Set an in-memory FIFO eviction policy on the managed session service to automatically purge older session event logs when user profile updates occur.
C. Configure memory topic customization in Memory Bank to enable the USER_PERSONAL_INFO and USER-PREFERENCES managed topics.
D. Specify a consistent user identity scope during memory generation and retrieval.
E. Add user risk tolerance values into the system instruction prompt as immutable string constants during session creation.

Answer: C,D

NEW QUESTION # 22
You are building a social media monitoring agent pipeline in Google Cloud that analyzes five million incoming customer feedback posts per day. The pipeline must classify each post into one of three sentiment categories: positive, negative, or neutral. It must also generate a one-sentence summary for each post. You need to configure the language model to minimize inference cost and latency while maintaining high classification accuracy.
What should you do?

A. Route incoming posts in real time to an LLM using a provisioned throughput endpoint.
B. Deploy a generalized pre-trained small language model (SLM) on an autoscaling online prediction endpoint with GPU acceleration.
C. Deploy a fine-tuned small language model (SLM) on Gemini Enterprise optimized for high-throughput batch classification.
D. Route the classification task to a high-capacity reasoning LLM, and offload the summarization task to a local regex pipeline.

Answer: C

Explanation:
Processing high-volume workloads of five million records daily requires an architecture optimized for compute efficiency, cost reduction, and batch throughput. Utilizing large, general-purpose reasoning models for narrow classification and concise summarization introduces prohibitive infrastructure costs and unneeded latency. The optimal pattern involves parameter-efficient fine-tuning (PEFT) of a small language model (SLM, such as Gemma) on task-specific sentiment datasets. Fine-tuning aligns the SLM's internal weights to achieve domain accuracy comparable to frontier models on structured classification and concise generation.
Deploying the fine-tuned SLM using Vertex AI batch prediction / Gemini Enterprise batch processing pipelines enables high-throughput processing at a fraction of online serving costs. Batch prediction pools compute resources, eliminates real-time provisioned throughput overhead, and manages massive token volumes cost-effectively. Regex pipelines cannot synthesize generative summaries, and hosting online GPU endpoints incurs substantial idle and operational expenses.
Reference: Evaluating and deploying agentic workflows - Small Language Model (SLM) optimization, parameter fine-tuning, and batch prediction cost efficiency.

NEW QUESTION # 23
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