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| Section | Weight | Objectives |
|---|---|---|
| Domain 3: Developing Custom Agents | 33% | - Implement Agent Identity and permissions - Orchestrate multi-agent workflows using MCP and A2A - Implement RAG pipelines and vector retrieval - Integrate enterprise data sources and APIs - Configure sessions, memory, and state management - Build agents with the Agent Development Kit (ADK) - Design agent architectures and model selection |
| Domain 5: Securing and Governing Agentic Workflows | 15% | - Implement authentication and authorization for agents - Apply governance policies and human-in-the-loop controls - Protect against prompt injection and data leakage - Monitor audit logs and compliance - Use Model Armor, Agent Gateway, and Agent Registry |
| Domain 2: Using Coding Agents for Application Development | 17% | - Use coding agents in secure sandboxed environments - Integrate coding agents with development workflows - Select and configure coding agents - Manage MCP servers and custom skills |
| Domain 4: Evaluating and Deploying Agentic Workflows | 22% | - Deploy and scale agentic workflows - Design evaluation datasets and methodologies - Implement continuous evaluation pipelines - Monitor, troubleshoot, and optimize agents in production - Select deployment targets: Agent Runtime, Cloud Run, GKE |
| Domain 1: Building Agents Using Low-Code Tools | 13% | - Design and configure agents using low-code platforms - Configure agent triggers, workflows, and responses - Test and iterate low-code agents - Integrate data sources and knowledge bases |
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NEW QUESTION # 36
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: A
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 # 37
You are developing a customer support agent in Gemini Enterprise to analyze incoming customer incident reports. These reports include recorded audio calls in MP3 and WAV formats and long video-screen captures lasting up to 45 minutes. When you call the Gemini API with a raw Base64 payload, the request fails with a
413 error: size too large. You need to enable the agent workflow to process, analyze, and summarize these large audio and video files natively using Gemini models, without hitting payload restrictions or requiring preconversion steps.
What should you do?
Answer: D
Explanation:
When interacting with the Gemini API, submitting large binary payloads directly as inline Base64-encoded strings causes requests to exceed HTTP body payload limitations (typically resulting in HTTP 413: Payload Too Large errors). Gemini models natively support direct multimodal processing of extensive audio formats (MP3, WAV) and long-duration video recordings without requiring local transcoding, chunking, or feature conversion. The Google-recommended architecture requires uploading the raw media files to a Cloud Storage bucket and referencing their Cloud Storage URIs (gs://bucket-name/object-name) alongside their respective MIME types in the Gemini API request payload. The Gemini platform directly ingests and streams the multimedia content from Cloud Storage, allowing the model to perform complete end-to-end transcription, visual analysis, and long-context summarization while circumventing client-side network transmission bottlenecks and payload limitations. Converting audio to raw PCM chunks or streaming Base64 blobs through Pub/Sub introduces unnecessary serialization latency and operational complexity.
Reference: Developing custom agents - Large multimodal payload handling, Cloud Storage URI referencing, and native media processing.
NEW QUESTION # 38
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?
Answer: D
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 # 39
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?
Answer: A
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 # 40
You are establishing team-wide engineering guidelines in an Antigravity workspace for a cloud-native Go project. You want the agent to automatically enforce a set of enterprise architecture conventions on every interaction, while providing a database migration workflow that developers can invoke on demand. You need to structure these customizations using native Antigravity configuration files.
What should you do?
Answer: B
Explanation:
Antigravity organizes workspace guidance into persistent context rules and modular, on-demand capabilities.
Persistent architectural standards, coding conventions, and style constraints are declared within a GEMINI.md file (or inside .gemini/rules/) at the workspace root. The Antigravity engine continuously ingests these markdown rules into the agent's working context, ensuring every code generation and refactoring turn complies with enterprise standards. Conversely, procedural workflows required only intermittently-such as database schema migrations-must not be loaded persistently, as this causes context window congestion.
Instead, they are packaged as custom skills within a dedicated skills directory containing a SKILL.md specification. Developers invoke the skill on demand, allowing the agent to load the migration procedure only when requested.
Reference: Using coding agents for application development - Antigravity workspace configuration, GEMINI.md rules, and modular SKILL.md definition.
NEW QUESTION # 41
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