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| Section | Weight | Objectives |
|---|---|---|
| Domain 3: Developing Custom Agents | 33% | - Configure sessions, memory, and state management - Implement Agent Identity and permissions - Integrate enterprise data sources and APIs - Design agent architectures and model selection - Build agents with the Agent Development Kit (ADK) - Implement RAG pipelines and vector retrieval - Orchestrate multi-agent workflows using MCP and A2A |
| Domain 4: Evaluating and Deploying Agentic Workflows | 22% | - Design evaluation datasets and methodologies - Monitor, troubleshoot, and optimize agents in production - Select deployment targets: Agent Runtime, Cloud Run, GKE - Implement continuous evaluation pipelines - Deploy and scale agentic workflows |
| Domain 1: Building Agents Using Low-Code Tools | 13% | - Integrate data sources and knowledge bases - Test and iterate low-code agents - Design and configure agents using low-code platforms - Configure agent triggers, workflows, and responses |
| Domain 5: Securing and Governing Agentic Workflows | 15% | - Use Model Armor, Agent Gateway, and Agent Registry - Apply governance policies and human-in-the-loop controls - Protect against prompt injection and data leakage - Implement authentication and authorization for agents - Monitor audit logs and compliance |
| Domain 2: Using Coding Agents for Application Development | 17% | - Integrate coding agents with development workflows - Use coding agents in secure sandboxed environments - Select and configure coding agents - Manage MCP servers and custom skills |
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NEW QUESTION # 33
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: A
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 # 34
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?
Answer: D
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 # 35
You are developing a custom retail assistant agent using Agent Development Kit (ADK). The agent checks real-time stock levels by querying a proprietary internal inventory database. You have written a secure Python function that performs this query. You need to integrate the function so that the underlying Gemini model can autonomously decide when to invoke it and automatically format the required parameters based on user input.
What should you do?
Answer: A
Explanation:
The Agent Development Kit (ADK) integrates custom business logic and internal databases through its function tool abstraction. When converting a native Python function into an agent tool, the framework automatically extracts the underlying tool schema, parameter types, and semantic intent directly from the function's signature. By applying explicit Python type annotations (such as str, int, or Pydantic models) and comprehensive docstrings that detail the function's purpose, parameter definitions, and return types, ADK generates a compliant OpenAPI tool schema for the Gemini model. During reasoning loops, the foundation model analyzes user prompts, references the tool's docstring to evaluate applicability, and autonomously generates properly structured function arguments that adhere to the annotated parameter constraints. Injecting raw Python code with eval() introduces severe security vulnerabilities, while client-side regex matching circumvents the agent's autonomous reasoning loop entirely.
Reference: Developing custom agents - Tool definition patterns, FunctionTool wrapping, Python type hints, and parameter docstring schemas.
NEW QUESTION # 36
You are configuring a custom AI agent deployed in Gemini Enterprise and the Conversational Analytics API.
The agent calls downstream enterprise tool APIs running in a separate Google Cloud project to retrieve restricted business analytics on behalf of the logged-in user. Currently, custom tool logic handles unmanaged raw OAuth 2.0 access tokens from execution context state, causing authorization failures during cross-project API calls when tokens expire and exposing raw long-lived credentials in custom tool code.
You need to establish a secure, managed identity propagation architecture between Gemini Enterprise and the downstream tool APIs.
What action should you take?
Answer: C
Explanation:
Establishing cross-project identity propagation for Google Cloud APIs-such as the Conversational Analytics API-requires industry-standard Google OAuth 2.0 access tokens passed within standard HTTP headers.
Custom agent tools must not embed long-lived service account JSON keys or expose refresh tokens in query parameters, as this violates least-privilege security policies and risks credential leakage. When invoking the Conversational Analytics API on behalf of an enterprise user, tool requests must supply a standard Google OAuth 2.0 bearer access token in the Authorization: Bearer < token > HTTP header. In the downstream project hosting the target data analytics agent, the calling principal (or propagated user identity) must be granted the roles/geminidataanalytics.dataAgentUser IAM role. This role grants permissions to interact with and query data agents without granting administrative configuration privileges (dataAgentCreator). Identity tokens in IAP headers are designed for Cloud Run/App Engine perimeter identity validation rather than direct Conversational Analytics API authentication.
Reference: Securing and governing agentic workflows - Conversational Analytics API IAM roles, cross- project identity propagation, and OAuth 2.0 bearer authentication.
NEW QUESTION # 37
You are deploying an enterprise conversational agent in Google Cloud that connects to sensitive human resources (HR) databases. Your organization's compliance policy requires all user queries and model completions to be filtered in real time to prevent prompt injection attacks, block harmful content, and sanitize personally identifiable information (PII) before responses reach the user interface. You need to implement a managed security enforcement layer that meets these requirements with minimal latency.
What should you do?
Answer: C
Explanation:
Securing enterprise conversational agents that handle sensitive HR records demands inline, multi-layered threat mitigation and data privacy controls. Model Armor provides a managed, low-latency guardrail solution designed to sanitize model inputs and outputs against jailbreaks, adversarial prompt injections, and policy violations. By integrating Model Armor directly with Cloud Sensitive Data Protection (DLP), organizations link automated de-identification templates to redact, mask, or tokenize PII (such as employee IDs, social security numbers, and contact details). Enforcing these Model Armor filter templates centrally at the Agent Gateway applies uniform security across all incoming queries and outgoing responses before data reaches the client or touches downstream databases. This centralized perimeter enforcement eliminates the operational complexity of attaching policies to individual agent runtimes and prevents inconsistent policy enforcement across diverse endpoints. Hosting self-managed guardrail models on Model Garden adds significant compute latency and requires manual scaling, whereas managed Model Armor templates at Agent Gateway deliver sub- millisecond policy execution.
Reference: Securing and governing agentic workflows - Model Armor templates, Sensitive Data Protection integration, and centralized Agent Gateway enforcement.
NEW QUESTION # 38
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