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| Section | Weight | Objectives |
|---|---|---|
| Developing custom agents | 33% | - Orchestrating and coordinating agentic workflows
|
| Securing and governing agentic workflows | 15% | - Configuring agent security and governance
|
| Building agents using low-code tools | 13% | - Connecting enterprise data to Gemini Enterprise
|
| Using coding agents for application development | 17% | - Customizing coding agents for enterprise workflows
|
| Evaluating and deploying agentic workflows | 22% | - Deploying and scaling production workloads
|
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NEW QUESTION # 30
You are configuring a secure agent-to-tool API integration for a financial agent on Gemini Enterprise Agent Platform. The agent calls an internal payment processing microservice protected by OAuth 2.0.
To comply with strict enterprise security policy, you must ensure that:
OAuth client credentials used for service-to-service tool authentication are encrypted at rest and never hardcoded in tool definitions or repository source code.
Tool execution is strictly limited to the minimum required API permissions, so the agent cannot perform unauthorized administrative operations on the payment microservice.
What should you do?
Choose two
Answer: D,E
Explanation:
Enterprise security governance requires strict separation of credentials, encryption at rest, and least-privilege permission scoping for autonomous agent tool invocations. Hardcoding credentials or encoding secrets in Base64 within environment variable files violates compliance standards and exposes sensitive keys in repository histories. To satisfy credential protection requirements, client secrets and certificates must be stored in Secret Manager, with their resource URIs linked to the Agent Identity auth manager configuration.
Agent Identity auth manager resolves secrets securely at runtime to obtain OAuth tokens, preventing credentials from being exposed in application code. To enforce least privilege and prevent unauthorized operations on downstream payment APIs, the auth provider definition must declare explicit OAuth 2.0 scopes (e.g., payments.read and payments.create). Binding this auth provider directly to the specific payment tool definition in Agent Runtime restricts the token's authorization boundary strictly to necessary operations, blocking wildcard access and administrative abuse.
Reference: Securing and governing agentic workflows - Agent Identity auth manager, Secret Manager credential integration, and OAuth 2.0 scope bounding.
NEW QUESTION # 31
You are deploying an autonomous coding agent on Google Kubernetes Engine (GKE) to run user-requested Python scripts. To isolate untrusted code execution, you enabled GKE Agent Sandbox with gVisor to intercept container system calls in user space. During a security audit, you find that an untrusted Python script generated by a prompt injection attack successfully fetched IAM identity tokens by sending an HTTP request to the link-local instance metadata server at 169.254.169.254.
You need to prevent sandboxed code from exfiltrating metadata tokens while maintaining container kernel isolation.
What should you do?
Answer: B
Explanation:
GKE Agent Sandbox with gVisor provides strong application kernel isolation by executing container system calls in a user-space kernel (Sentry), preventing malicious workloads from compromising the underlying host operating system. However, user-space system call virtualization does not restrict network-level IP egress traffic by default. When untrusted, generated code runs inside a pod, it can issue TCP connections to the link- local metadata server (169.254.169.254), potentially exfiltrating node service account tokens. To deterministically block metadata server exfiltration, administrators configure a Kubernetes NetworkPolicy attached to the sandbox Pod. Defining an egress rule that blocks outbound traffic to 169.254.169.254/32 (or restricting egress strictly to authorized internal subnets) closes the metadata vulnerability. Blocking ingress traffic does not prevent outbound token acquisition requests initiated by the pod, and disabling execve breaks the execution runtime needed to launch child Python processes.
Reference: Securing and governing agentic workflows - GKE Agent Sandbox, gVisor container isolation, and Kubernetes NetworkPolicy metadata protection.
NEW QUESTION # 32
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: B
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 # 33
You built a document processing pipeline in which an extraction agent and a scoring agent exchange records through the Agent2Agent (A2A) protocol. The agents were written with Agent Development Kit (ADK) and deployed as two separate Agent Runtime agents, and neither agent is called from outside the pipeline.
You find that each record takes 400 milliseconds longer than expected to process. Your profiling tool attributes the extra time to serialization and network calls between the two agents. You want to minimize processing time while ensuring that each agent retains its current logic and continues to handle one record per invocation.
What should you do?
Answer: D
Explanation:
The Agent2Agent (A2A) protocol is engineered for cross-boundary, loosely coupled collaboration where agents reside in separate domains or administrative boundaries. However, deploying pipeline agents as independent Agent Runtime instances communicating over A2A introduces network latency, HTTP serialization/deserialization overhead, and inter-service routing delays-adding substantial processing time per record. When both extraction and scoring agents are dedicated to an internal sequential pipeline without external invocation requirements, deploying both agents into the same Agent Runtime instance eliminates inter-process network overhead. Configuring the scoring agent as a local subagent of the extraction agent allows execution to occur in-process. State and data records pass directly through shared memory structures rather than external protocol envelopes, drastically slashing processing latency while preserving the individual agent logic and single-record invocation design. Transport-level adjustments such as gRPC or co-locating regions still incur network serialization overhead.
Reference: Developing custom agents - Agent runtime co-location, subagent patterns, and A2A overhead optimization.
NEW QUESTION # 34
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: B
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 # 35
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