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| Section | Objectives |
|---|---|
| Topic 1: Requirements Management | - Requirements Analysis and Prioritization - Requirements Gathering - Implementation and Change Considerations |
| Topic 2: Oracle Fusion Data Intelligence | - Administration and Configuration - Data Augmentation - Instance Provisioning and Configuration - Validation and Troubleshooting - Solution Overview and Architecture - Bundles, Backup and Restoration - Semantic Model Extensions and Customization - Security and Access Management - Data Pipelines and Data Loading - Analytics and Reporting |
| Topic 3: Functional Consultancy | - Implementation Decisions - Troubleshooting and Verification - Business Requirements Analysis - Configuration and Validation |
| Topic 4: Oracle Fusion Cloud Applications | - Application Data and Business Processes - Integration with Fusion Data Intelligence - Fusion Cloud Applications Overview |
| Topic 5: Oracle Cloud Infrastructure | - OCI Concepts Relevant to Fusion Data Intelligence - Cloud Infrastructure Configuration - Security and Access |
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NEW QUESTION # 91
Which Fusion Applications duty role must be granted to the extraction user account to authorize data extractions via Business Intelligence Cloud Connector for Oracle Fusion Data Intelligence?
Answer: C
Explanation:
C). BICC_EXTRACT_TRANSFORM_LOAD_TECHNICAL_PRIVATE.
Explanation:
To perform and authorize data extractions using Business Intelligence Cloud Connector (BICC) for Oracle Fusion Data Intelligence (FDI / FAW) or external ETL pipelines, the extraction user account must be granted the duty role BICC_EXTRACT_TRANSFORM_LOAD_TECHNICAL_PRIVATE (ORA_BICC_EXTRACT_TRANSFORM_LOAD_TECHNICAL_PRIVATE).
This role grants the necessary privileges to execute extraction jobs, query BICC REST APIs, and access the extracted data files from the storage location
NEW QUESTION # 92
An enterprise architect is designing an external application merge to bring custom semantic objects from Oracle Analytics Server into an FDI non-production environment.
To which target database connection can the merged external semantic model connect within the Oracle Fusion Data Intelligence environment?
Answer: D
Explanation:
B). Only to the Oracle Autonomous Data Warehouse associated with the Oracle Fusion Data Intelligence instance: According to Oracle Fusion Data Intelligence (FDI) architecture and external application merge guidelines, any external semantic model (from Oracle Analytics Server or standalone OAC) must be modified so that its physical layer connection pools point exclusively to the custom schema hosted within the Oracle Autonomous Data Warehouse (ADW) / AI Lakehouse instance associated with the specific FDI environment.
Why the Incorrect Answers are Wrong
A). To any remote on-premises database via the Remote Data Gateway agent: Merged external semantic models within FDI cannot maintain live connection pools back to remote on-premises databases via gateways; data must be migrated and queried from the associated ADW instance.
C). Directly to the Oracle Fusion Cloud Applications transactional database (OLTP): Direct querying of the operational OLTP database by analytics semantic models is strictly blocked to prevent resource contention on source applications.
D). To an external third-party cloud database via a direct OCI Service Gateway bypass: FDI framework restrictions and security models do not permit merged semantic layers to bypass the native data architecture and query external third-party cloud databases directly.
NEW QUESTION # 93
A security administrator is setting up data-level security for global financial analysts who need row-level access restricted by Ledger and Business Unit across multiple functional areas. The organization wants to leverage automated capabilities for ingesting user access rights from Fusion Applications while establishing custom logical security rules in the semantic model.
Which TWO statements correctly describe how data security functions in Oracle Fusion Data Intelligence?
Answer: C,D
Explanation:
C). Data security context assignments can be synchronized automatically from Oracle Fusion Cloud Applications into Oracle Fusion Data Intelligence.
D). Data security context filters configured across different functional groups in Configure Data Security are combined using the AND logical operator.
Detailed Explanations
Why the Correct Statements are Right
Statement C: Oracle Fusion Data Intelligence supports automated data security synchronization pipelines.
This allows organizations to automatically pull user data access assignments (such as Business Units and Ledgers) directly from source Oracle Fusion Cloud Applications without relying exclusively on manual CSV file uploads.
Statement D: When configuring multiple filters within Configure Data Security, the system evaluates different functional groups using logical conjunction. Specifically, all sets of filters established across different functional groups are combined using the AND operator to ensure strict multi-criteria compliance.
Why the Incorrect Statements are Wrong
Statement A: Data security rules configured via Configure Data Security rely on data-type application roles, whereas metadata object permissions rely on duty-type application roles.
Statement B: Filters defined within the same functional group are actually combined using the OR operator (not the AND operator), allowing users to match any of the criteria included in that group.
Statement E: Managing user data access and security assignment files via the Uploads center updates user context mappings; it does not overwrite, delete, or impact the custom semantic model design steps created under Configure Data Security
NEW QUESTION # 94
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 # 95
A service administrator wants to schedule an automated monthly full data reload for the General Ledger functional area while maintaining standard daily incremental extractions.
Which feature must be enabled under Pipeline Features in Generally Available Features to expose this capability on the Pipeline Settings page?
Answer: D
Explanation:
D). Scheduled Full Data Reload: According to Oracle Fusion Data Intelligence documentation, the Scheduled Full Data Reload feature must be enabled under Pipeline Features in Generally Available Features to expose the warehouse full reload scheduling capabilities on the Pipeline Settings page. This enables administrators to schedule periodic full reloads for select functional areas (such as General Ledger) on a weekly, monthly, or quarterly basis while maintaining standard daily incremental extractions.
Why the Incorrect Answers are Wrong
A). Frequent Refresh: This feature is used to enable high-frequency data refresh intervals (such as every 4 hours or more frequently) rather than full data reloads.
B). Reset and Reload the Data Source: This feature enables a complete, one-off reset and reload of the entire data source (often used after a Production-to-Test refresh) rather than automated periodic full reloads of specific functional areas.
C). Override Data Pipeline Schedules: This feature is used to modify or override default functional area execution schedules rather than enabling full reload capabilities.
NEW QUESTION # 96
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