Pass the Oracle Certified Associate 1z0-1145-1 Questions and answers with ExamsMirror

Comprehensive and Detailed Explanation:

Industry-specific compliance is achieved by directly incorporating relevant language into both the agent’s system prompt and its topics. Adding topics (Option A) ensures that each type of promotion is addressed in context with specific compliance details. Updating the system prompt (Option D) enables the agent to always include mandatory compliance language when generating summaries.

The official Oracle documentation states:

“Customizing system prompts and topics is essential when regulatory or compliance requirements vary by business unit or product line. Prompts can include fixed compliance disclaimers, and topics can be expanded to address promotion-specific rules.”

(Reference: Oracle Fusion AI Agent Studio Customization Guide, “Embedding Compliance in AI Agent Outputs,” 2024, p. 54)

Removing access to past data (B) or merely changing the language model (C) does not address compliance directly.

Practical takeaway:Embed compliance at both the conversation and prompt levels for legal assurance.

Comprehensive and Detailed Explanation:

The Metrics Reporting Dashboard is the centralized, real-time analytics hub within Oracle Fusion AI Agent Studio. It provides visualizations and reports on correctness, safety, token usage, and other key metrics, empowering analysts to track trends and quickly identify performance or compliance issues.

Oracle states:

“The Metrics Reporting Dashboard aggregates performance and safety metrics, updating continuously for comprehensive agent oversight.”

(Reference: Oracle Fusion AI Agent Studio Reporting Guide, 2024, p. 43)

Token usage and evaluation sets offer only partial or historical data.

Practical takeaway:Use the dashboard for consolidated, up-to-date agent quality insights.

Comprehensive and Detailed Explanation:

Oracle Fusion AI Agent Studio provides pre-built templates that serve as foundational blueprints for common business scenarios. Selecting and customizing a relevant template enables rapid configuration, extension, and integration—supporting both external (via External REST tool) and internal (via Business Object tool) data requirements.

Oracle documentation:

“Pre-built templates accelerate deployment and ensure best-practice workflows, allowing teams to extend functionality and integrate diverse data sources with minimal manual effort.”

(Reference: Oracle AI Agent Studio Templates Guide, 2024, p. 10)

Building from scratch is time-consuming; generative services do not deliver agent workflows; email is not core to the requirement.

Practical takeaway:Always start with a template for hybrid data and workflow needs.

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:

Security evaluation in AI agents focuses on protection against prompt injection attacks and maintaining content safety. Oracle AI Agent Studio tracks metrics related to both, ensuring agents are not manipulated into inappropriate responses or actions.

The official documentation specifies:

“Prompt injection metrics monitor attempts to manipulate agent instructions, while content safety metrics detect and block the generation of unsafe or non-compliant responses. These are core to maintaining trusted AI interactions and regulatory compliance.”

(Reference: Oracle Fusion AI Agent Studio Security Overview, 2024, p. 46)

User/token count and LLM requests/latency are operational, not security, metrics.

Practical takeaway:Prioritize prompt injection and content safety monitoring for robust agent security.

Comprehensive and Detailed Explanation:

The External REST tool allows agents to interact with third-party applications and data sources by consuming or sending data through secure APIs. This capability is essential for integrating Oracle Fusion AI Agent Studio agents into broader enterprise ecosystems, enabling automation and data exchange beyond the Oracle environment.

From Oracle documentation:

“External REST tools are used to connect agents with non-Fusion systems, supporting use cases such as data retrieval, workflow orchestration, and integration with external services via standard API protocols.”

(Reference: Oracle Fusion AI Agent Studio Tools Reference, 2024, p. 37)

Deep linking and UI template generation are handled by other specialized tools.

Practical takeaway:Use the External REST tool to extend agent capabilities beyond Oracle Fusion.

Comprehensive and Detailed Explanation:

Comparative analysisin Oracle Fusion AI Agent Studio allows you to objectively evaluate new agent versions against previous ones using identical input and reference sets. This identifies changes in logic quality, correctness, and efficiency, ensuring measurable improvements before broader rollout.

Oracle documentation:

“The comparative analysis tool is essential for continuous agent improvement, supporting controlled A/B testing and quantitative assessment of new agent logic.”

(Reference: Oracle Fusion AI Agent Studio Testing Guide, 2024, p. 23)

Semantic search and document tool are not comparison features for agent logic.

Practical takeaway:Use comparative analysis to validate and demonstrate agent enhancements.

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:

Structured agent testing requires both the ability to manage test cases (Option D) and compare outcomes over time (Option B). Oracle Fusion AI Agent Studio provides robust tools for managing evaluation datasets and for comparing the results of different test runs to assess improvements or regressions in agent behavior.

The documentation states:

“Evaluation test data management is fundamental for consistent agent assessment. Additionally, built-in comparison tools enable users to review performance across multiple versions or configurations, ensuring that any changes made to agents result in measurable improvements.”

(Reference: Oracle AI Agent Studio Evaluation Framework, 2024, p. 24)

Options A and C are not part of the standard testing toolkit.

Practical takeaway:Maintain organized test data and always compare results for continuous quality.

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.