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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Domain 1: Building Agents Using Low-Code Tools | 13% | - Design and configure agents using low-code platforms - Configure agent triggers, workflows, and responses - Integrate data sources and knowledge bases - Test and iterate low-code agents |
| Topic 2: Domain 2: Using Coding Agents for Application Development | 17% | - Integrate coding agents with development workflows - Manage MCP servers and custom skills - Use coding agents in secure sandboxed environments - Select and configure coding agents |
| Topic 3: Domain 4: Evaluating and Deploying Agentic Workflows | 22% | - Select deployment targets: Agent Runtime, Cloud Run, GKE - Deploy and scale agentic workflows - Design evaluation datasets and methodologies - Monitor, troubleshoot, and optimize agents in production - Implement continuous evaluation pipelines |
| Topic 4: Domain 3: Developing Custom Agents | 33% | - Orchestrate multi-agent workflows using MCP and A2A - Design agent architectures and model selection - Implement RAG pipelines and vector retrieval - Configure sessions, memory, and state management - Implement Agent Identity and permissions - Build agents with the Agent Development Kit (ADK) - Integrate enterprise data sources and APIs |
| Topic 5: Domain 5: Securing and Governing Agentic Workflows | 15% | - Monitor audit logs and compliance - Use Model Armor, Agent Gateway, and Agent Registry - Implement authentication and authorization for agents - Apply governance policies and human-in-the-loop controls - Protect against prompt injection and data leakage |
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NEW QUESTION # 54
You are implementing a continuous evaluation pipeline for an agent built with Agent Development Kit (ADK) that invokes multiple external API tools. During evaluation runs, the agent often selects the wrong tools or executes them in the wrong order, causing errors in multistep workflows. You need to establish automated evaluation gates in your CI/CD pipeline to improve execution reliability before promoting model or prompt changes to production.
What should you do?
Answer: B
Explanation:
In complex multi-tool agentic workflows, functional reliability depends on the correct selection and execution order of intermediate tool calls. When prompt updates or model revisions cause an agent to trigger incorrect tools or scramble execution order, response-level text metrics (such as BLEU or final answer similarity) often fail to detect underlying workflow errors. The Google-recommended CI/CD gating pattern utilizes the Gen AI evaluation service integrated into automated build pipelines (e.g., Cloud Build). The evaluation service compares the agent's intermediate execution steps against a curated golden trajectory dataset using specialized trajectory metrics. Metrics such as Trajectory Exact Match, In-Order Tool Match, and Tool Call Accuracy evaluate whether the agent selected the expected tools and invoked them in the required operational sequence. Thresholds set on these metrics serve as programmatic quality gates, blocking deployment of breaking changes. Model Armor filters content rather than validating tool workflows.
Reference: Evaluating and deploying agentic workflows - Continuous evaluation pipelines, Gen AI evaluation trajectory metrics, and CI/CD deployment gating.
NEW QUESTION # 55
You are configuring custom coding agents with Antigravity for an enterprise engineering team. You find that the agent frequently attempts to run unauthorized terminal commands, despite instructions in the project's markdown context files that specify command boundaries. You need to enforce strict, deterministic guardrails that validate shell command arguments before tool execution.
What should you do?
Answer: C
Explanation:
Prompt instructions and markdown rules provide probabilistic boundaries that generative models can bypass during complex workflows. To enforce deterministic execution guardrails on tool execution, Antigravity supports lifecycle hooks defined in .agents/hooks.json. A PreToolUse hook intercepts tool calls-such as run_command-before arguments reach the host operating system. The hook passes proposed tool arguments via standard input (stdin) to a local validation script. The script deterministically validates the proposed commands and command-line flags against enterprise allowlists, immediately halting unauthorized executions. Relying on remote LLM evaluations reintroduces latency and non-deterministic behavior, whereas a local deterministic validation script provides definitive security enforcement.
Reference: Using coding agents for application development - Antigravity hook architecture, PreToolUse interception, and deterministic command guardrails.
NEW QUESTION # 56
You are developing a custom financial analysis agent in Python with Agent Development Kit (ADK). The agent calls an exchange rate function when users request cross-border portfolio valuations, but in multi-turn interactions, the model often passes invalid currency codes or fails to trigger the function. You need to ensure that the agent reliably invokes the function with valid parameter types.
What should you do?
Answer: C
Explanation:
In the Agent Development Kit (ADK), the Gemini foundation model relies on structured OpenAPI schemas generated from Python function signatures to understand how and when to invoke tools. When an agent fails to trigger a function or frequently generates malformed arguments (such as invalid currency codes), the root cause typically lies in missing, ambiguous, or poorly documented function parameters. Defining explicit Python type annotations (such as typed strings, literal enumerations, or Pydantic data models) ensures that ADK constructs a rigid parameter schema that the LLM's function-calling mechanism can parse.
Accompanying this with detailed parameter docstrings provides semantic context, guiding the model on valid value domains (e.g., ISO-4217 three-letter currency formats) and expected input structures. Unformatted string lists in system prompts lack schema binding, while custom retry loops treat runtime symptoms rather than fixing model guidance at the interface contract level.
Reference: Developing custom agents - ADK function tool contracts, type annotations, and docstring schema generation.
NEW QUESTION # 57
You are deploying a custom Antigravity CLI plugin to enforce enterprise data loss prevention compliance across developer workstations. You must configure an event hook that intercepts tool calls immediately before file execution, automatically redacts sensitive API keys from generated code diffs in transit, and stops tool execution if sanitization fails.
What should you do?
Answer: C
Explanation:
Antigravity CLI enforces developer guardrails via lifecycle hooks declared within a plugin's hooks.json file.
To prevent secrets or API keys from being written to disk, security validation must execute synchronously prior to file tool invocation. In the CLI hook lifecycle, a handler bound to the pre-tool execution event intercepts the incoming tool payload before execution. The handler inspects the generated code diff, identifies sensitive credentials against DLP rule sets, and rewrites the parameters in transit to redact detected keys. If redaction succeeds, the handler returns an explicit success status, permitting tool execution to proceed with sanitized parameters. If sanitization fails or encounters an uncorrectable violation, the handler halts execution deterministically, preventing sensitive tokens from being written to the workstation filesystem. PostToolUse hooks execute after the tool has completed, which would allow credentials to be committed before cleanup runs.
Reference: Using coding agents for application development - Antigravity lifecycle hooks, hooks.json configuration, PreToolUse interception, and in-transit sanitization.
NEW QUESTION # 58
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
Answer: A,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 # 59
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