During the quality assurance and testing phase of a digital assistant (agent), latency and token metrics are crucial for evaluating non-functional requirements. Latency directly measures the response wait time from the user's perspective; ensuring this is within an acceptable range is critical for a positive user experience. Tokens are the units of text processed by the underlying Large Language Model (LLM). Since LLM services are typically priced per token, monitoring token usage is essential for understanding and managing the operational financial cost of the agent.

A. Time saved and user adoption are business-level outcomes measured post-deployment, not direct technical metrics like latency or tokens used in pre-deployment testing.

C. Accuracy relates to the correctness and relevance of the agent's response content, which is a separate functional quality metric, not directly measured by latency or token count.

1. Oracle Cloud Infrastructure Documentation

"Generative AI - Pricing": This official pricing guide explicitly states that the service is billed based on the number of input and output tokens. This directly links the "tokens" metric to the "financial cost of running the agent".

Source: Oracle Cloud Infrastructure Official Documentation.

Section: Generative AI > Pricing.

2. Oracle Digital Assistant Documentation

"The Insights User Interface - Conversation-Quality Metrics": This document details the built-in analytics for Oracle Digital Assistant

which includes "Average Skill Response Time (ms)". This metric is a direct measurement of latency

used to confirm that the "agent response wait time is acceptable".

Source: Oracle Help Center

Oracle Digital Assistant Documentation.

Section: Use > Monitor and Analyze Your Digital Assistant > The Insights User Interface > Conversation-Quality Metrics.

3. Oracle Digital Assistant Documentation

"Use the Invoke Large Language Model Component": This section explains how the digital assistant integrates with LLMs. The interaction described is the process that consumes tokens and incurs latency. Testing this component involves evaluating its performance (latency) and resource consumption (tokens)

which translates to cost.

Source: Oracle Help Center

Oracle Digital Assistant Documentation.

Section: Develop > Integrate with Large Language Models > Use the Invoke Large Language Model Component.

The most effective method within AI Agent Studio to securely manage sensitive data changes is by implementing a human-in-the-loop process. This feature allows the AI Agent to pause its automated workflow and escalate a proposed change, such as an update to an employee's salary or personal information, to a designated human user for review and explicit approval. This ensures that a responsible individual verifies the change before it is committed to the Fusion Applications database, providing a critical layer of security, oversight, and accountability for sensitive operations. This preventative control is superior to reactive alerts or simple time-based restrictions.

A. Sending alerts after an update is a detective control, not a preventative one. It doesn't stop an incorrect or unauthorized change from happening, it only reports it post-event.

C. Restricting actions to business hours is a weak temporal control. It does not prevent an unauthorized or erroneous change from being made during that time window.

1. Oracle Fusion Cloud Applications Documentation - Oracle Digital Assistant: The concept of escalating a task to a human is a core feature. The documentation states

"You can transfer a conversation from your skill to a human agent when the user's utterance indicates a need for human intervention." This principle is applied in AI Agent Studio for approval workflows.

Source: Oracle Help Center

Oracle Digital Assistant - Use Oracle Digital Assistant

Section: "Human Agent Handoff".

2. Oracle Fusion Cloud Applications Documentation - AI Apps for HCM: The design pattern for AI-driven processes involving sensitive data in HCM emphasizes control and oversight. The documentation on Digital Assistant and HCM integration highlights routing complex or sensitive transactions for manual processing.

Source: Oracle Help Center

Implementing Human Capital Management for AI Apps

Chapter: "Digital Assistant and HCM"

Section: "Route to a Live Agent".

The agent tracing feature, or its equivalent like the conversation review function, is a diagnostic tool. Its primary purpose is to allow developers and administrators to inspect individual interactions with the agent or system. By selecting a specific session or prompt, users can analyze the entire execution flow, including the initial user inputs, the system's processing steps, the final outputs, and associated performance metrics like latency. This detailed view is crucial for debugging issues, optimizing performance, and understanding the agent's behavior in specific scenarios.

A. To update agent security permissions based on detected access vulnerabilities

This is incorrect. Tracing is a read-only, analytical function for observing behavior, not a tool for modifying security configurations or permissions.

C. To modify an agent's security roles directly from the trace files

This is incorrect. Trace files are logs for review and analysis. They are not an interface for performing administrative tasks like modifying security roles.

1. Oracle Digital Assistant Documentation - Review Conversations on the Conversations Page: While the term "agent tracing" is general

its function is embodied in the "Conversations" page within Oracle Digital Assistant (a related Oracle conversational AI technology). The documentation states

"The Conversations page provides a 360-degree view of the conversations that users have with your digital assistant and skills. You can use it to troubleshoot problems... For each turn in the conversation

you can review the processing details..." This aligns directly with reviewing prompts to analyze inputs

outputs

and performance.

Source: Oracle Help Center

Use Oracle Digital Assistant

Chapter: "Monitor and Improve Your Assistant"

Section: "Review Conversations on the Conversations Page".

2. Oracle Intelligent Advisor Documentation - Debug an interview: The principle of tracing for analysis is also central to the Oracle Intelligent Advisor debugger. The documentation explains its use to "step through the interview one screen at a time"

"view and change data"

and "see explanations for decisions". This process of reviewing specific steps to analyze inputs (data) and outputs (decisions) is functionally identical to the purpose described in the correct answer.

Source: Oracle Help Center

Oracle Intelligent Advisor 24A Policy Modeling User Guide

Chapter: "Test a policy model"

Section: "Debug an interview".

AI Agent Studio streamlines narrative report generation primarily through two methods. First, using a prebuilt agent template specifically designed for narrative reporting provides a foundational structure and workflow, significantly reducing initial setup time and effort. This allows the finance team to start with a working model instead of building one from scratch. Second, customizing the system prompt is crucial for aligning the AI-generated content with specific company standards for tone, style, and terminology. This fine-tuning ensures the output is more accurate and requires less manual revision, directly accelerating the final report creation process.

A. Enabling multi-language support is a localization feature for reaching different audiences, not a method to streamline the core report generation workflow itself.

C. Changing the underlying data visualization library is a complex developer-level task, not a standard feature for a finance team to streamline report creation.

1. Oracle Cloud Infrastructure Documentation

Digital Assistant

"Agent Templates": This documentation describes how templates provide a pre-configured starting point for building agents

which accelerates development. It states

"You can create an agent from a template

which is a pre-populated agent that you can then customize." This supports the use of templates (Option B) to streamline processes.

Reference: Oracle Help Center

Develop Bots with Oracle Digital Assistant

Section: "Agent Templates".

2. Oracle Cloud Infrastructure Documentation

Generative AI

"Prompts": This source details the importance of prompt engineering to guide the output of large language models. It explains that a well-crafted system prompt can define the persona

tone

and constraints for the AI's response. This directly supports customizing the system prompt (Option D) to align output with company standards

thereby reducing manual edits.

Reference: Oracle Help Center

OCI Generative AI

Section: "Prompts".

AI Agent Studio is designed to accelerate the development of AI agents. It provides pre-built templates that serve as a starting point for common use cases, such as integrating with Oracle Fusion Cloud Applications. The most efficient method to meet the department's requirements is to select the "Fusion Applications Agent" template. This template comes pre-configured with the necessary tools and authentication setups for Fusion. The developer can then customize this template by adding new tools, such as a REST tool to connect to the third-party system's API and another tool to query the custom data objects within Fusion Applications.

A. Build the agent workflow from scratch: While possible, this is less efficient than using a template, which provides a pre-configured foundation for Fusion Applications integration, saving significant development time.

B. Use generative AI services instead of an agent: This is incorrect because an agent in AI Agent Studio is the mechanism that uses generative AI services (like a large language model) for reasoning and task execution.

D. Add an email tool to the agent workflow: An email tool is a specific component for a single task (sending emails). It does not address the core requirement of building the agent's data access capabilities.

1. Oracle Help Center

Oracle Digital Assistant Documentation

"Create an Agent": This section explicitly states that agents can be created from scratch or from a template. It highlights that templates

such as the Fusion Applications Agent template

provide a starting point for specific use cases. This supports using a template as the primary method for building an agent that interacts with Fusion Applications. (Refer to the "AI Agent Studio" > "Agents" > "Create an Agent" section in the official Oracle Digital Assistant documentation).

2. Oracle Help Center

Oracle Digital Assistant Documentation

"About Agent Templates": This documentation details the purpose of templates

explaining they are pre-built agents that can be customized. It mentions the existence of templates for specific enterprise applications like Fusion

which are designed to be extended with custom tools for unique business needs

such as connecting to third-party systems. (Refer to the "AI Agent Studio" > "Agents" > "Agent Templates" section).

An External REST tool is the correct choice because modern CRM systems expose their data and functionalities through REST APIs. This allows the AI Agent to make standardized HTTP requests (e.g., GET) to specific endpoints to retrieve structured customer data, such as contact details or case history, in a predictable format like JSON. This method is the industry standard for integrating disparate enterprise applications, providing a secure and scalable way for the AI Agent to interact with the CRM in real-time. The tool handles the connection, authentication, and data mapping required for the integration.

A. Document tool: Used for searching and retrieving information from unstructured documents (e.g., PDFs), not for querying structured data fields in a CRM system.

C. Email tool: Designed for sending and receiving electronic mail, not for direct data retrieval from an application's database or API.

D. Business Object tool: Typically manages internal data structures within an application, whereas an external tool is needed to communicate with the separate CRM system's API.

1. Oracle Intelligent Advisor Documentation

"Connect to a REST service": This section details how to create and configure connections to external REST services to invoke operations. It states

"You can create a connection to a REST service to invoke operations that are exposed by that service... For example

to get a list of contacts from a CRM system." This directly supports using a REST tool for CRM integration.

Source: Oracle Help Center

Oracle Intelligent Advisor Documentation

"Use Intelligent Advisor with other applications"

Chapter: "Connect to a REST service".

2. Oracle Intelligent Advisor Documentation

"Use data from other applications in a policy model": This document explains the concept of using external data within Intelligent Advisor. It describes how data connectors (including REST services) are used to "load data from

and save data to

an external data source" at the start and end of a session

which is precisely the requirement for retrieving CRM data.

Source: Oracle Help Center

Oracle Intelligent Advisor Documentation

"Model policies with Intelligent Advisor"

Chapter: "Use data from other applications in a policy model".

Oracle's AI Agent Studio allows for the rapid deployment of conversational agents using prebuilt templates. For an HR administrator, the "Benefits Agent" template serves as a starting point. To tailor it, the administrator can directly modify the template's configuration. Customizing the system prompt (B) allows the administrator to inject specific company policy details, rules, and the desired persona for the agent. Adjusting the agent's topics (C) enables the administrator to define or modify the conversational flows to match the company's unique benefits structure, such as adding a topic for a "Wellness Stipend" or removing one for a benefit the company doesn't offer. These two actions directly customize the agent's behavior and conversational scope.

A. Modify the agent's source code within the template editor

AI Agent Studio is a low-code platform; templates are customized through configuration and settings, not by modifying underlying source code, especially by a non-developer persona like an HR administrator.

D. Import policy data by uploading documents directly to the agent template

While you upload documents to provide knowledge, this data populates a separate knowledge source that the agent connects to. It is not an action performed "directly to the agent template" itself, which contains the logic and configuration.

---

1. Oracle Digital Assistant Documentation - Get Started with Generative AI: This document explains the role of the system prompt in guiding the Large Language Model. It states

"The system prompt is where you provide high-level instructions for the LLM... You can also use it to provide information that is key to the agent's purpose

such as the company name or key policies." This directly supports option B.

Source: Oracle Help Center

Oracle Digital Assistant Documentation

"Get Started with Generative AI in Oracle Digital Assistant"

Section: "System and User Prompts".

2. Oracle Digital Assistant Documentation - Topics: This documentation describes how Topics are used to structure an agent's conversation. "A topic is a collection of related conversational flows that help a user complete a specific task... You can create topics to handle different aspects of a use case." An administrator would adjust these to reflect their organization's specific benefit scenarios

supporting option C.

Source: Oracle Help Center

Oracle Digital Assistant Documentation

"Topics"

Section: "About Topics".

3. Oracle Digital Assistant Documentation - Prebuilt Skills and Templates: The documentation on using prebuilt components emphasizes customization. "When you pull a skill or template from the Skill Store

you get a full copy of the skill... You can then customize it as you see fit." This confirms that the intended use is to take a template and then modify its components

such as prompts and topics.

Source: Oracle Help Center

Oracle Digital Assistant Documentation

"Use Prebuilt Skills and Templates"

Section: "Pull from the Skill Store".

Oracle AI Agent Studio is a comprehensive platform designed for developers to build, manage, and deploy AI agents. Its core components are the foundational elements that facilitate this process. These include Tools, which are functions or APIs that agents can invoke to perform actions; Templates, which provide pre-built starting points for various agent types; and organizational constructs like Agent teams and Topics for managing the development lifecycle and capabilities of multiple agents within a collaborative environment. These components are specific to the agent-building purpose of the studio.

A. Workflow templates, custom scripts, and scheduling triggers: These are characteristic components of a general-purpose automation or orchestration service, such as Oracle Cloud Infrastructure (OCI) Process Automation, not the specific building blocks of the AI Agent Studio.

B. Process orchestrators, data connectors, and security profiles: These are generic cloud infrastructure components. Process orchestrators, data connectors (like in OCI Data Integration), and security profiles (managed by OCI IAM) are supporting services, not the primary, unique components of the AI Agent Studio itself.

1. Oracle Cloud Infrastructure (OCI) Documentation. Generative AI

Key Concepts for Agents (Preview). This document defines "Tools" as a key concept for agents

stating

"A tool is a function or API that an agent can call to get information that's not in the LLM." This directly supports the "Tools" component in the correct answer. (Available in the Oracle Cloud Infrastructure console under AI & Machine Learning > Generative AI > Agents).

2. Oracle AI Blog. (March 20

2024). Introducing Oracle AI Agent Studio. This official announcement describes the platform

stating

"AI Agent Studio is a comprehensive platform that provides developers with the necessary tools

templates

and frameworks to build and deploy AI agents that can understand context

make decisions

and take actions." This supports the "Tools" and "Templates" components.

In Oracle AI Agent Studio, the evaluation framework is designed to assess an agent's performance across multiple dimensions. The "Security" category of metrics is specifically focused on the agent's safety and resilience against malicious use. This category includes evaluations for prompt injection, which tests the agent's ability to resist attempts to bypass its core instructions, and content safety, which measures whether the agent's outputs adhere to safety guidelines and avoid generating harmful or inappropriate content. These two metrics are crucial for ensuring the agent behaves securely and responsibly.

A. User and Token count: These are categorized as usage and cost metrics. They measure operational aspects like user engagement and resource consumption, not the agent's security posture.

C. LLM requests and latency: These are performance and cost metrics. They evaluate the agent's responsiveness and the operational load on the underlying language model, not its security integrity.

1. Oracle Cloud Infrastructure Documentation - Generative AI Agents: In the section on evaluating an agent version

the documentation explicitly lists the categories of metrics provided in the evaluation results. It states

"The evaluation results provide metrics for the following categories: ... Security: Evaluates the agent's security against prompt injection and content safety." This directly confirms that prompt injection and content safety are the security evaluation metrics. The document also lists "Cost and latency" as a separate category

which includes metrics like token count and latency

confirming options A and C are incorrect categories for security.

Source: Oracle Cloud Infrastructure Documentation

"Evaluating an Agent Version

" Section: "Reviewing Evaluation Results." Document accessed October 2023. (Available at: https://docs.oracle.com/en-us/iaas/generative-ai/generative-ai-agents/using/agent-evaluate.htm)

The security architecture of Oracle Fusion Applications is fundamentally based on Role-Based Access Control (RBAC). The most effective and architecturally correct method to minimize data exposure is to ensure the agent operates under a security role with the least privilege necessary for its function. This involves configuring the agent's access rules to align with a user role that has been granted access only to the specific business objects and data attributes (fields) required for summarizing shift data. Oracle Fusion's data security policies, which are part of the RBAC framework, can restrict access down to the attribute (column/field) level, thereby preventing the agent from accessing unrelated or confidential information at the foundational security layer.

B. Set up the agent for public data sharing to promote transparency

This action directly contradicts the stated goal of minimizing exposure of confidential information and would introduce a severe security risk.

C. Limit the agent’s queries and responses to specific, relevant business object fields

While this is a good development practice, it is a secondary control. The primary and authoritative security mechanism in Oracle Fusion is the RBAC model. Proper security should be enforced by the platform's security policies (Option A), not rely solely on the agent's client-side code.

1. Oracle Fusion Cloud Applications - Securing Your Applications Guide: This guide details the security architecture

emphasizing Role-Based Access Control. It states

"Oracle Fusion Cloud Applications security is a function- and data-based security model that is organized by roles." This supports the principle of using roles (Option A) as the primary security mechanism.

Reference: Chapter 1

"Oracle Fusion Cloud Applications Security

" Introduction.

2. Oracle Fusion Cloud Applications - Implementing Security Guide: This document explains the implementation of data security

including at the attribute level. It describes how data security policies

which are assigned to roles

can secure attributes. "You can secure a logical entity's attributes by creating a policy for an attribute group." This confirms that field-level security is an integral part of the RBAC setup described in Option A.

Reference: Chapter 4

"Data Security

" Section: "Data Security Policies".

3. Oracle Fusion Cloud HCM - REST API for Common Features: The documentation for using REST APIs notes that access is controlled by privileges granted to roles. "Before you can use a REST API

you must grant a function security privilege for the required REST resource to a custom role." This reinforces that role configuration is the prerequisite and primary control for any data access.

Reference: Chapter 2

"Getting Started

" Section: "Set Up Authentication and Authorization".