Oracle 1z0-1145-1 - Oracle Fusion AI Agent Studio Foundations Associate - Rel 1

Comprehensive and Detailed Explanation:

TheExternal REST toolis specifically used for integrations that require accessing data or services outside of Oracle Fusion Applications. This includes:

(B) Retrieving data from internal but non-Fusion databases (such as a compliance database exposed via REST API)

(D) Accessing third-party systems, like logistics platforms, through their public or private APIsOracle documentation states:

“The External REST tool enables seamless integration between Oracle Fusion AI Agents and any system accessible via RESTful API, supporting scenarios ranging from compliance data validation to real-time supply chain updates.”

(Reference: Oracle AI Agent Studio Integration Guide, 2024, p. 39)

Options A and C are unrelated to REST-based integrations.

Practical takeaway:Use External REST for any external or cross-system data requirements.

Comprehensive and Detailed Explanation:

Latency measures the time it takes for the agent to respond, which directly impacts user experience. Token count reflects how much data is processed and helps gauge the cost of running the agent, as Oracle’s AI services are often billed per token.

According to Oracle:

“Monitoring latency ensures users are not left waiting, maintaining satisfaction and engagement. Tracking token usage provides transparency regarding processing costs, which is vital for budgeting and operational efficiency. Together, these metrics help optimize both agent performance and cost management.”

(Reference: Oracle AI Agent Studio Metrics and Reporting, “Latency and Token Analytics,” 2024, p. 11)

Accuracy is tested separately; latency and tokens are about speed and cost.

Practical takeaway:Regularly monitor both latency and tokens to balance user satisfaction and operational expenses.

Comprehensive and Detailed Explanation:

To objectively measure and compare the output quality of two agent versions, Oracle Fusion AI Agent Studio provides evaluation tools that run both versions against the same test inputs and reference outputs. This process, called evaluation testing, reveals differences in correctness, reliability, and performance.

From Oracle’s documentation:

“By executing standardized evaluation sets across different agent versions, users can pinpoint improvements or regressions in agent logic, ensuring each update delivers quantifiable quality gains.”

(Reference: Oracle AI Agent Studio Evaluation Guide, 2024, p. 19)

Token counts reflect cost, not quality. Changing prompts mid-operation is not a valid assessment approach.

Practical takeaway:Always use evaluation sets for scientific, reproducible agent quality assessment.

Comprehensive and Detailed Explanation:

Correctnessmeasures the accuracy of agent responses, whileLatencymeasures the time taken to respond—together, these are the two most important metrics for optimizing both answer quality and customer experience.

Oracle documentation:

“To improve both efficiency and satisfaction, focus on correctness (response accuracy) and latency (response speed) metrics in the monitoring dashboard.”

(Reference: Oracle Fusion AI Agent Studio Performance Guide, 2024, p. 29)

Token counts and error rates provide operational, not customer-centric, insights.

Practical takeaway:Monitor correctness and latency to continually enhance support agent workflows.

Comprehensive and Detailed Explanation:

TheDocument toolallows agents to ingest, search, and extract answers from uploaded unstructured content such as manuals and guides. Semantic search ensures the agent’s answers are grounded in the most relevant and current documentation, supporting rapid and accurate user responses.

Oracle documentation says:

“Document tools empower agents to search and summarize information from uploaded or linked documents, making them indispensable for knowledge-intensive workflows and dynamic content delivery.”

(Reference: Oracle AI Agent Studio Document Management Guide, 2024, p. 21)

Accuracy tools are for measurement, not information retrieval; Business Object is for structured data; Deep Link is navigation only.

Practical takeaway:Use Document tool for AI-powered document search and answer generation.

Comprehensive and Detailed Explanation:

Thecorrectness scoreevaluates how accurately the agent’s responses match expected outcomes, whilemedian/P99 latencymeasures responsiveness and consistency in real-world conditions. Monitoring these metrics provides immediate feedback on agent quality and user experience.

Oracle documents:

“Dashboards display correctness, latency, and other operational metrics in real time, allowing continuous monitoring and rapid intervention if performance degrades.”

(Reference: Oracle Fusion AI Agent Studio Metrics Dashboard, 2024, p. 20)

UI color and meetings are irrelevant for runtime performance.

Practical takeaway:Monitor correctness and latency for ongoing agent optimization.

Comprehensive and Detailed Explanation:

The External REST tool allows agents to query third-party logistics platforms in real time using defined REST APIs. This tool is specifically designed for cross-application integration, supporting any external service with a standards-compliant API, and returning data directly into the agent workflow.

Oracle documentation details:

“External REST tools facilitate seamless interaction with external systems, enabling use cases like real-time order tracking, status updates, and supply chain event monitoring without leaving the Oracle Fusion ecosystem.”

(Reference: Oracle Fusion AI Agent Studio Integration Guide, 2024, p. 31)

User Session and Document tools do not enable live external integration; Business Object tool is only for internal Fusion data.

Practical takeaway:Use the External REST tool whenever agents must interact with outside systems.

Comprehensive and Detailed Explanation:

To maintain data privacy and security, Oracle Fusion AI Agent Studio strongly recommends aligning agent permissions with established user permissions. This ensures the agent can only access data appropriate for its role, following the organization’s access controls.

Oracle states:

“By inheriting user permission sets, agents are prevented from accessing data outside their scope. This is the most effective way to enforce confidentiality and regulatory requirements for sensitive business information.”

(Reference: Oracle AI Agent Studio Security Configuration Guide, 2024, p. 21)

Public sharing (B) increases risk, and while specific field limitation (C) is useful, access alignment is foundational.

Practical takeaway:Always configure agent permissions to match user access profiles for strong data protection.

Comprehensive and Detailed Explanation:

Evaluation test data in Oracle Fusion AI Agent Studio is primarily used to assess the quality and reliability of agent responses. Users upload test inputs and reference outputs to compare actual agent performance against expected results during offline testing phases.

From the Oracle documentation:

“Evaluation sets allow developers to simulate real-world usage by supplying a battery of inputs and expected outputs. This enables objective measurement of agent performance before deployment and helps identify gaps or errors in agent logic.”

(Reference: Oracle AI Agent Studio Testing Guide, “Offline Evaluation with Test Sets,” 2024, p. 18)

Option A is incorrect—evaluation sets do not fine-tune the LLM, and Option C misrepresents their function.

Practical takeaway:Always use uploaded evaluation sets for objective, reference-based agent testing.