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Google Professional-Agentic-Architect Exam Syllabus Topics:

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

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Google Cloud Certified - Professional Agentic Architect Sample Questions (Q12-Q17):

NEW QUESTION # 12
You are designing an agentic workflow in Gemini Enterprise for a media organization. The agent will enable natural language search across thousands of hours of archived video training content. End users must be able to search the video library with text queries and receive precise video segment matches. Your solution must process video files stored in Cloud Storage, map visual content and user text queries into a shared vector space, and perform real-time similarity retrieval at scale.
What should you do?
Choose two

A. Store video files with embeddings in BigQuery, create a vector index, and execute pattern matching queries against the index.
B. Generate vector representations for video segments using the Multimodal embeddings API in Agent Platform.
C. Convert video files into Base64-encoded strings, and store them in the vector store. Send query requests to the Gemini API.
D. Use the Text embeddings API with the text-embedding-005 model to generate vectors from Cloud Storage video object path strings and file names.
E. Index the generated embedding vectors in Vector Search to perform approximate nearest neighbor (ANN) retrieval for user query embeddings.

Answer: B,E

Explanation:
Building an agentic video search system across extensive training archives requires shared semantic representation and scalable vector indexing. The Multimodal Embeddings API on Agent Platform generates dense vector representations across both text and video inputs, projecting visual features, spoken dialogue, and textual search queries into a unified, shared embedding space. By segmenting archived video content and generating segment-level multimodal embeddings with preserved timestamp metadata, the pipeline enables granular temporal retrieval. To support real-time querying across thousands of hours of video, these generated vectors are indexed in Vertex AI Vector Search. Vector Search provides low-latency Approximate Nearest Neighbor (ANN) search at massive enterprise scale, matching user query embeddings against indexed video segments in milliseconds. Text-only embedding models cannot extract visual feature semantics, and storing multi-gigabyte Base64 strings or raw video arrays directly inside vector databases is technically invalid and architecturally prohibitive.
Reference: Developing custom agents - Multimodal Embeddings API, shared vector spaces, and Vertex AI Vector Search indexing.

NEW QUESTION # 13
You are setting up an automated unit testing pipeline in which Antigravity generates and executes test scripts for third-party Python packages. Your organization's security requirements state that dynamic test suites generated by the LLM must never access local host credentials, private developer files, or unapproved internal network services. You need to configure a secure execution runtime for the coding agent.
What should you do?

A. Assign a Google Cloud service account with restricted, least-privilege IAM roles to the pipeline execution host.
B. Configure a .antigravityrules markdown file in the repository instructing the agent to never generate code that reads host files or connects to external domains.
C. Configure a VPC Service Controls perimeter around the pipeline's execution subnet.
D. Execute agent-generated test scripts inside isolated, containerized execution sandboxes.

Answer: D

Explanation:
LLM-generated code running within automated pipelines constitutes untrusted code execution. Generative models are vulnerable to indirect prompt injection embedded within third-party packages, making soft prompt instructions (such as .antigravityrules) insufficient for enterprise security. Host-level IAM permissions limit cloud resource interaction but fail to isolate local developer credential files, SSH keys, or link-local endpoints.
The Google-recommended security pattern is executing agent-generated test scripts inside isolated, containerized sandboxes (such as gVisor-based sandboxes or ephemeral containers). Sandboxes isolate system calls from the host kernel, restrict filesystem mounts to necessary ephemeral workspaces, and enforce strict network egress boundaries, deterministically blocking access to host credentials and internal enterprise networks.
Reference: Securing and governing agentic workflows - Secure execution environments, agent sandboxing, and untrusted code isolation.

NEW QUESTION # 14
You are designing an enterprise agent system to process sensitive financial records. The traffic consists of high-volume, high-token classification tasks. You need to design the system to minimize operational costs and limit the number of requests sent outside the bank network.
What should you do?

A. Provision dedicated Google Kubernetes Engine (GKE) GPU nodes to host an open-source LLM that processes all agent subtasks within the VPC.
B. Deploy an open-weight small language model (SLM) on Cloud Run functions with public HTTPS endpoints. Forward unparsed financial payloads to Gemini using LLM Gateway.
C. Deploy an open-weight small language model (SLM) on Google Kubernetes Engine (GKE) in a private Google Cloud perimeter for routine subtasks. Dynamically route complex reasoning steps to a managed enterprise LLM API.
D. Configure Agent Platform Provisioned Throughput on a managed Gemini model to handle both routine high-volume subtasks and complex agent reasoning under a fixed monthly quota.

Answer: C

Explanation:
Processing high-volume, high-token financial records requires balancing data residency, network isolation, and inference cost optimization. Routing all high-volume classification tasks to external public LLM endpoints incurs substantial API token expenses and transfers sensitive payloads across public networks.
Conversely, hosting large foundation models entirely on self-managed infrastructure for every complex task requires heavy GPU capital expenditures. The Google-recommended architecture implements a hybrid tiering model. High-volume, routine classification tasks are served by a fine-tuned, open-weight Small Language Model (SLM, such as Gemma) hosted on a Google Kubernetes Engine (GKE) cluster enclosed within a private VPC Service Controls perimeter. This keeps sensitive financial records within the bank's private network perimeter and reduces per-token compute costs. Only non-routine subtasks requiring deep multi-step reasoning are dynamically routed to managed Gemini Enterprise endpoints over private interconnects.
Reference: Securing and governing agentic workflows - Data residency perimeters, VPC Service Controls, and hybrid SLM/frontier LLM tiered routing.

NEW QUESTION # 15
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. 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.
B. Execute the evaluation of the golden dataset against the agent using ADK eval set tooling, and configure LLM-based autorater metrics.
C. Deploy the agent to Agent Runtime, run the agent against the golden dataset, and review the metrics using Google Cloud Observability.
D. Run a batch evaluation job against the agent using the golden dataset to calculate BLEU and ROUGE scores for the generated responses.

Answer: B

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 # 16
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. Set an in-memory FIFO eviction policy on the managed session service to automatically purge older session event logs when user profile updates occur.
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. Disable identity scoping on the Memory Bank instance to do a similarity search for user information during memory consolidation.
E. Specify a consistent user identity scope during memory generation and retrieval.

Answer: B,E

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 # 17
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