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| Section | Objectives |
|---|---|
| Topic 1: Oracle Cloud Infrastructure | - Security and Access - Cloud Infrastructure Configuration - OCI Concepts Relevant to Fusion Data Intelligence |
| Topic 2: Functional Consultancy | - Implementation Decisions - Business Requirements Analysis - Troubleshooting and Verification - Configuration and Validation |
| Topic 3: Oracle Fusion Data Intelligence | - Security and Access Management - Administration and Configuration - Data Pipelines and Data Loading - Bundles, Backup and Restoration - Analytics and Reporting - Data Augmentation - Validation and Troubleshooting - Semantic Model Extensions and Customization - Instance Provisioning and Configuration - Solution Overview and Architecture |
| Topic 4: Oracle Fusion Cloud Applications | - Application Data and Business Processes - Integration with Fusion Data Intelligence - Fusion Cloud Applications Overview |
| Topic 5: Requirements Management | - Implementation and Change Considerations - Requirements Analysis and Prioritization - Requirements Gathering |
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NEW QUESTION # 10
You are defining custom database objects in the Autonomous AI Lakehouse associated with your Oracle Fusion Data Intelligence instance to support a custom dataset data augmentation.
What is the standard suffix required for a Data Augmentation Dataset object to adhere to Oracle's database naming standards?
Answer: C
Explanation:
B). _DS: According to Oracle Fusion Data Intelligence database naming standards for the Autonomous Data Warehouse / AI Lakehouse, custom database objects and datasets must follow strict suffix guidelines.
Specifically, _DS is the required standard suffix designated for Data Augmentation Dataset objects (whereas
_EXT is used for Data Augmentation Extensions, _MD for Mini Dimensions, and _F for Facts).
Why the Incorrect Answers are Wrong
A). _EXT: This suffix designates a Data Augmentation Extension rather than a dataset object.
C). _MD: This suffix is reserved for Mini Dimensions in custom star schemas.
D). _F: This suffix is standard for Fact tables rather than data augmentation datasets.
NEW QUESTION # 11
Your organization is preparing to apply a new FDI Application Release and wants to understand what happens if no manual action is taken before the predefined auto-update date, while also confirming business users won't lose access to reporting during the process.
Which two statements are correct about this behavior?
Answer: A,B
Explanation:
Explanation of the Correct Answers
Oracle automatically updates your data model and content on the predefined date if you haven't updated manually (B): If an administrator does not manually trigger or reschedule an Application Release before its scheduled deadline, Oracle automatically applies the update on the predefined auto-update target date to ensure environments remain current.
All reports and dashboards remain available during the upgrade process (D): FDI is architected with high availability in mind; during standard application updates and data model patching, end users do not lose access to their existing dashboards and reports, ensuring uninterrupted business operations.
Explanation of the Incorrect Answers
Application Releases only apply to Platform-level security patches, not the data model (A): This is incorrect because Application Releases specifically deliver data model enhancements, new semantic structures, and updated analytical content, whereas Platform Releases handle underlying service infrastructure and security patches.
Business users lose access to all dashboards for 24 hours during any Application Release (C): This is false; updates do not cause extended 24-hour downtime for reporting environments.
You cannot change the auto-update target date once it's been set by Oracle (E): Administrators are given flexibility within the Console's Release Updates area to reschedule or adjust auto-update target dates within the allowed maintenance window before the automated application occurs.
NEW QUESTION # 12
Which security feature would you use if you want security-assignment changes maintained in an external cost- center hierarchy to propagate into FDI automatically, without relying on manual Upload Center file uploads?
Answer: D
Explanation:
Auto Sync Security (D): This feature enables the automated propagation of user-to-security assignments into FDI without requiring manual file uploads via the Upload Center. By leveraging custom synchronization interfaces or tracking tables (such as automated mapping sync routines), it ensures that changes maintained in external cost-center hierarchies flow into the platform dynamically.
Explanation of the Incorrect Answers
Security Exclusion Rules (A): These rules are used to explicitly exclude certain data values or records from being visible to specific roles or users, rather than handling the automated synchronization of user-to- hierarchy assignments.
Configurable Security Context (B): While this framework defines the structure and rules for data access controls (such as mapping cost and balancing segments), it requires assignment mechanisms like the Upload Center or automated sync processes to populate user mappings.
Data Security Policy Template (C): This is used to govern baseline functional or data permissions within applications rather than serving as the automated synchronization mechanism for external hierarchy changes.
NEW QUESTION # 13
Which FDI release type is applied automatically to every customer on the same day, requires no administrator scheduling, and never changes the data model or content?
Answer: B
Explanation:
Platform Release (D): Platform releases contain service updates, backend infrastructure updates, security patches, and updates to the administrative Console. Oracle applies these patches automatically across all customer environments on the same day without requiring administrator scheduling or action. Because they focus entirely on infrastructure and service layers, they never modify the underlying data model or analytical content.
Explanation of the Incorrect Answers
Application Release (A): These releases contain data model updates and new analytical content. They feature auto-update target dates that administrators can customize or manually trigger ahead of time via the Console.
Major Quarterly Content Release (B): This is a descriptive term often used broadly for major application or feature cadences, but it involves content and data model changes governed by scheduled application update targets rather than immediate, automated platform-only deployment.
Emergency Data Model Patch (C): Emergency or hotfix patches are deployed to address specific urgent bugs or critical issues, which may target data models or functional logic rather than serving as the standard automated platform-wide maintenance routine.
NEW QUESTION # 14
Which underlying mechanism does the Oracle Fusion Data Intelligence pipeline use to extract operational data from Oracle Fusion Cloud Applications?
Answer: A
Explanation:
C). Business Intelligence Cloud Connector
Explanation for the Correct Answer
The Oracle Fusion Data Intelligence (FDI) pipeline leverages the Business Intelligence Cloud Connector (BICC) as the core underlying mechanism to extract bulk transactional data and metadata views (known as Pivot View Objects or PVOs) from Oracle Fusion Cloud Applications. BICC is specifically optimized for high-volume SaaS data extraction, natively supporting both full initial loads and scheduled incremental (delta) updates.
Why the Other Options Are Incorrect
A). Oracle GoldenGate Change Data Capture: GoldenGate provides real-time, log-based database replication, but it is not the built-in application-level extraction mechanism utilized by standard Oracle Fusion Data Intelligence pipelines to extract structured data from Oracle Fusion SaaS applications.
B). OCI Data Integration REST Connectors: While OCI Data Integration handles general-purpose cloud ETL and uses various connectors for custom integrations, it is not the native underlying framework built into the automated Oracle Fusion Data Intelligence pipeline.
D). Oracle Enterprise Data Quality Engine: This engine is built for data profiling, cleansing, validation, and matching, rather than serving as the high-volume data extraction framework for pulling operational tables out of cloud applications.
NEW QUESTION # 15
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