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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Domain 5: Securing and Governing Agentic Workflows | 15% | - Implement authentication and authorization for agents - Use Model Armor, Agent Gateway, and Agent Registry - Apply governance policies and human-in-the-loop controls - Protect against prompt injection and data leakage - Monitor audit logs and compliance |
| Topic 2: Domain 2: Using Coding Agents for Application Development | 17% | - Select and configure coding agents - Use coding agents in secure sandboxed environments - Integrate coding agents with development workflows - Manage MCP servers and custom skills |
| Topic 3: Domain 3: Developing Custom Agents | 33% | - Orchestrate multi-agent workflows using MCP and A2A - Design agent architectures and model selection - Implement Agent Identity and permissions - Configure sessions, memory, and state management - Implement RAG pipelines and vector retrieval - Build agents with the Agent Development Kit (ADK) - Integrate enterprise data sources and APIs |
| Topic 4: Domain 1: Building Agents Using Low-Code Tools | 13% | - Configure agent triggers, workflows, and responses - Design and configure agents using low-code platforms - Integrate data sources and knowledge bases - Test and iterate low-code agents |
| Topic 5: Domain 4: Evaluating and Deploying Agentic Workflows | 22% | - Implement continuous evaluation pipelines - Monitor, troubleshoot, and optimize agents in production - Deploy and scale agentic workflows - Design evaluation datasets and methodologies - Select deployment targets: Agent Runtime, Cloud Run, GKE |
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NEW QUESTION # 66
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?
Answer: A
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 # 67
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?
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 # 68
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?
Answer: B
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 # 69
You are designing research AI infrastructure for a university computing facility that supports two distinct academic workloads:
A high-volume, 24/7 data extraction pipeline handling sensitive student records that are subject to strict data residency rules.
An intermittent, complex research workload requiring advanced multimodal reasoning over large context windows, with unpredictable usage spikes.
You need to minimize total operational expenditure and meet data governance requirements while satisfying the latency and capability needs of both workloads.
What should you do?
Answer: C
Explanation:
Designing dual-workload AI infrastructure requires aligning infrastructure characteristics with workload patterns and regulatory compliance. The continuous 24/7 data extraction pipeline processes sensitive student data with strict data residency constraints. Deploying an open-weight Gemma model on Google Kubernetes Engine (GKE) within a private VPC guarantees continuous resource availability, satisfies local data residency rules, and maximizes hardware utilization, lowering unit processing costs. Conversely, the academic research workload is intermittent, features unpredictable spikes, and requires long-context multimodal reasoning that small open-weight models cannot support natively. Maintaining dedicated GPU clusters for intermittent research results in expensive idle capacity. Utilizing the managed Gemini API on Agent Platform provides serverless access to frontier multimodal reasoning with large context windows on a pay-as-you-go basis, eliminating infrastructure management while meeting performance demands.
Reference: Evaluating and deploying agentic workflows - Hybrid compute architectures, GKE workload hosting, and serverless managed API utilization.
NEW QUESTION # 70
You are deploying a custom AI assistant written in Python with Agent Development Kit (ADK). You want to maximize developer velocity by deploying the agent code with minimal infrastructure plumbing, while ensuring that the agent has built-in, out-of-the-box access to manage conversational session history and memory persistence.
How should you deploy the agent?
Answer: C
Explanation:
Agent Runtime is Google Cloud's fully managed, serverless execution platform built specifically for deploying ADK-based agentic workloads. Deploying an ADK application directly to Agent Runtime maximizes developer velocity by eliminating the operational overhead of provisioning virtual machines, managing container clusters, or configuring custom load balancers. Agent Runtime features native, out-of-the- box integration with Agent Platform Sessions and Agent Platform Memory Bank. The runtime automatically manages conversation lifecycles, persists session state across multi-turn dialogues, and connects to Memory Bank to handle long-term memory extraction and retrieval without requiring custom database persistence layers. Deploying to Cloud Run or Google Kubernetes Engine (GKE) with external BigQuery or Bigtable databases requires designing custom state synchronization middleware, schema migrations, and connection pooling, introducing unnecessary infrastructure plumbing and operational complexity.
Reference: Evaluating and deploying agentic workflows - Agent Runtime managed deployment, Agent Platform Sessions, and Memory Bank integration.
NEW QUESTION # 71
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