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Google Professional-Agentic-Architect Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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 coding agents effectively 1. Refactoring source code, optimizing execution runtimes, and patching application-layer vulnerabilities 2. Using coding agents in secure sandboxes 3. Configuring coding agents with Model Context Protocol servers, custom skills, and tools
Topic 2: Securing and governing agentic workflows	15%	- Implementing secure agent behavior and execution 1. Configuring secure data access and identity propagation using Agent Gateway and Agent Registry 2. Designing safety frameworks and guardrails using Agent Gateway, Model Armor, and human-in-the-loop controls - Configuring agent security and governance 1. Configuring Principal Access Boundary policies using Agent Identity 2. Configuring Agent Gateway to monitor traffic and track agents 3. Implementing authentication and secure tool execution using OAuth 2.0 4. Designing agent governance and policy enforcement using Agent Registry and Model Armor
Topic 3: Developing custom agents	33%	- Integrating enterprise domain knowledge 1. Using embedding models, similarity scoring, reranking, Vector Search, and Agent Retrieval 2. Integrating managed databases, APIs, third-party SaaS tools, and remote servers using Google Cloud MCP servers 3. Configuring agent permissions using Agent Identity 4. Designing and managing retrieval-augmented generation pipelines and vector retrieval systems - Orchestrating and coordinating agentic workflows 1. Orchestrating agents using MCP and Agent2Agent protocols 2. Using Agent Identity, Agent Registry, Agent Runtime, and agent policies for workflow coordination 3. Coordinating multi-agent handoffs and workflows including parallel, sequential, and graph workflows - Designing and building agentic workflows in code 1. Configuring sessions and memory using Agent Platform Memory Bank and managed sessions 2. Configuring agent skills using Agents CLI, plugins, and agent or human modes 3. Building custom agents using open-source libraries such as Agent Development Kit 4. Selecting and configuring language models based on cost, security, and architecture requirements
Topic 4: Evaluating and deploying agentic workflows	22%	- Evaluating agents in development and production 1. Evaluating agent responses and retrieval quality against golden datasets 2. Creating agent evaluation test sets using golden data, prompts, and edge cases 3. Selecting evaluation frameworks and tools including ADK evaluation tooling, Gen AI evaluation service, and custom autoraters 4. Creating continuous evaluation pipelines for agent tool execution - Deploying and scaling production workloads 1. Identifying logic errors, latency bottlenecks, and hallucinations 2. Troubleshooting agent drift, tool invocation latency, reasoning loops, and system failures 3. Selecting deployment runtimes including Agent Runtime, Cloud Run, and GKE 4. Monitoring and optimizing agent performance, reliability, and cost
Topic 5: Building agents using low-code tools	13%	- Connecting enterprise data to Gemini Enterprise 1. Configuring secure connections to enterprise proprietary data sources 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 using Gemini Enterprise Workflow Builder and Customer Experience Agent Studio 2. Creating system instructions and in-console prompt templates to guide agent behavior

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Google Cloud Certified - Professional Agentic Architect Sample Questions (Q73-Q78):

NEW QUESTION # 73
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 fine-tuned small language model (SLM) on Gemini Enterprise optimized for high-throughput batch classification.
C. Route the classification task to a high-capacity reasoning LLM, and offload the summarization task to a local regex pipeline.
D. Deploy a generalized pre-trained small language model (SLM) on an autoscaling online prediction endpoint with GPU acceleration.

Answer: B

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 # 74
You are setting up security and monitoring controls on Agent Gateway for an enterprise financial agent. The agent calls external third-party partner APIs through Agent Gateway.
You need to configure Agent Gateway to enforce these policies:
Outbound traffic passing through Agent Gateway must be continuously inspected to detect and block unintentional exfiltration of sensitive data in tool payloads.
Egress traffic metrics, including per-agent token usage, request latency, and HTTP response codes, must be aggregated in real time for centralized security monitoring and cost allocation.
What should you do?
Choose two

A. Configure a VPC Service Controls perimeter around external third-party API private IP addresses to perform deep packet inspection and sanitization on egress HTTPS traffic.
B. Enable telemetry exporters on Agent Gateway routes to stream structured request logs, latency metrics, traces, and usage metadata to Cloud Logging and BigQuery.
C. Deploy an in-container eviction hook in the third-party partner API server to push telemetry metrics back to Agent Gateway.
D. Configure the Agent Gateway ingress route to disable TLS decryption.
E. Attach a Sensitive Data Protection inspection filter to Agent Gateway egress routes to scan and sanitize request payload contents before forwarding them to third-party endpoints.

Answer: B,E

Explanation:
Securing external tool interactions through Agent Gateway requires active egress inspection and centralized telemetry collection. External third-party partner APIs represent a potential vector for data exfiltration if an agent forwards sensitive financial data within tool payloads. Attaching a Sensitive Data Protection inspection filter directly to the Agent Gateway egress routes inspects outgoing payloads in real time. If sensitive patterns (e.g., credit card numbers, tax identifiers, or account balances) are detected, the filter blocks or sanitizes the payload before the request leaves the enterprise perimeter. Concurrently, enabling telemetry exporters on the gateway routes streams structured request metadata, latency figures, HTTP response codes, and token usage into Cloud Logging and BigQuery. This provides real-time auditability and cost allocation without modifying agent code. Disabling TLS decryption prevents deep packet inspection, and deploying code to third-party partner servers is outside enterprise operational control.
Reference: Securing and governing agentic workflows - Agent Gateway egress security, Sensitive Data Protection payload inspection, and telemetry exporters.

NEW QUESTION # 75
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. Specify a consistent user identity scope during memory generation and retrieval.
B. Configure memory topic customization in Memory Bank to enable the USER_PERSONAL_INFO and USER-PREFERENCES managed topics.
C. Add user risk tolerance values into the system instruction prompt as immutable string constants during session creation.
D. Set an in-memory FIFO eviction policy on the managed session service to automatically purge older session event logs when user profile updates occur.
E. Disable identity scoping on the Memory Bank instance to do a similarity search for user information during memory consolidation.

Answer: A,B

Explanation:
Agent Platform Memory Bank manages long-term memory extraction and consolidation across disparate conversational turns. When memory entries conflict or duplicate upon user updates, the system requires proper topical boundaries and identity scoping. By default, Memory Bank can extract broad conversational details; configuring memory topic customization to enable only managed topics such as USER_PERSONAL_INFO and USER-PREFERENCES ensures that extraction is restricted strictly to relevant user attributes and risk tolerances, ignoring extraneous conversational noise. Furthermore, specifying a consistent user identity scope during memory generation and retrieval operations allows Memory Bank's consolidation engine to resolve conflicting information over time. When a user's risk tolerance changes, the engine compares the new preference against historical memories tied to that unique user identity, updating or superseding prior entries rather than generating disconnected duplicate records. Hardcoding values in system prompts destroys dynamic adaptability.
Reference: Developing custom agents - Memory Bank topic customization, identity scoping, and preference consolidation.

NEW QUESTION # 76
You are using CodeMender on Gemini Enterprise Agent Platform to remediate a critical SQL injection vulnerability in an enterprise Python web app. You need the coding agent to generate a production-ready patch that fixes the underlying vulnerability while preserving the app's existing functionality.
What should you do?

A. Grant CodeMender's execution identity the roles/iam.securityUser IAM role to run commands.
B. Generate a patch using CodeMender's automated remediation workflow, and use its built-in LLM-as-a- judge framework to validate program equivalence against the generated code diff.
C. Configure CodeMender's patch generator to execute dynamic database schema migrations to sanitize incoming string inputs before query execution.
D. Instruct CodeMender to wrap dynamic SQL queries in try-except error handling blocks and retry failed queries using the default fallback parameters.

Answer: B

Explanation:
CodeMender operates within Gemini Enterprise Agent Platform to provide automated code remediation through static analysis and generative reasoning. When addressing critical security vulnerabilities such as SQL injection, the automated remediation workflow analyzes the Abstract Syntax Tree (AST), identifies the insecure query construction, and produces an updated patch. To guarantee that the patch remediates the vulnerability without altering the functional semantics of the application, CodeMender incorporates an LLM- as-a-judge framework that systematically verifies program equivalence between the original codebase and the generated code diff. This evaluation verifies that unit tests pass, syntactic structure conforms to enterprise standards, and unintended side effects are absent before the diff is presented to human reviewers. Wrapping vulnerable queries in try-except blocks fails to address the root vulnerability, while executing dynamic database migrations alters data definitions unnecessarily. Granting IAM security user roles pertains to Google Cloud identity access rather than code patch generation. Using the built-in remediation workflow coupled with program equivalence validation ensures production-grade security and operational stability.
Reference: Using coding agents for application development - CodeMender automated remediation, program equivalence verification, and vulnerability patching.

NEW QUESTION # 77
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. Store API keys in Secret Manager, and enable agents to retrieve keys to authorize communications.
B. Use Agent Development Kit (ADK) local session state using the ToolContext parameter to dynamically store and propagate user authorization credentials in memory.
C. 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.
D. 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.

Answer: D

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 # 78
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