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| Section | Objectives |
|---|---|
| Oracle Cloud Infrastructure | - Security and Access - Cloud Infrastructure Configuration - OCI Concepts Relevant to Fusion Data Intelligence |
| Oracle Fusion Data Intelligence | - Security and Access Management - Data Augmentation - Bundles, Backup and Restoration - Solution Overview and Architecture - Data Pipelines and Data Loading - Administration and Configuration - Analytics and Reporting - Instance Provisioning and Configuration - Validation and Troubleshooting - Semantic Model Extensions and Customization |
| Functional Consultancy | - Implementation Decisions - Configuration and Validation - Business Requirements Analysis - Troubleshooting and Verification |
| Requirements Management | - Requirements Analysis and Prioritization - Requirements Gathering - Implementation and Change Considerations |
| Oracle Fusion Cloud Applications | - Fusion Cloud Applications Overview - Application Data and Business Processes - Integration with Fusion Data Intelligence |
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NEW QUESTION # 128
Your organization has recently completed an incremental data pipeline load for ERP Analytics in Oracle Fusion Data Intelligence. To ensure long-term data integrity, the lead implementer needs to establish a verification routine that checks row-level transactional records and automatically re-verifies these metrics after future data refreshes. Which TWO actions should the implementer take within the FDI Console to satisfy these requirements?
Answer: D,E
Explanation:
Statement A: Detailed Data Validation is specifically designed for executing row-level transactional record comparisons between Oracle Fusion Cloud Applications (via OTBI) and the FDI data warehouse, satisfying the requirement to check individual transactional records.
Statement B: The Schedule tab on the Data Validation page allows administrators to establish automated, recurring execution cadences (such as weekly or post-pipeline refresh intervals), satisfying the requirement to automatically re-verify metrics after future data refreshes.
Why the Incorrect Statements are Wrong
Statement C: Re-applying security configurations under Semantic Model Extensions governs semantic layer permissions and model attributes, not data validation routines or record comparisons.
Statement D: Exporting an application artifact bundle from the Bundles tile is used for moving customizations and extensions between environments, not for setting up data validation or scheduling runs.
Statement E: Resetting source credentials before every run is unnecessary and incorrect, as valid source credentials only need to be configured once on the Source Credentials tab.
NEW QUESTION # 129
A financial organization needs to refresh General Ledger and Purchase Order data every 4 hours during quarter-end closing to ensure analysts have intraday transactional visibility. The administrator is setting up the Frequent Data Refresh schedule in the Oracle Fusion Data Intelligence Console.
Which TWO conditions or prerequisites must be satisfied to successfully configure and run this intraday frequent data refresh?
Answer: B,C
Explanation:
Statement B (The Frequent Refresh feature must be enabled...): Before high-frequency intraday schedules can be set up in Oracle Fusion Data Intelligence (FDI), the administrator must explicitly navigate to the Enable Features page under Generally Available Features and enable the Frequent Refresh feature flag.
Statement E (The incremental pipeline schedule... must be configured for a daily frequency): Frequent data refreshes operate as intraday runs that build upon the baseline incremental pipeline. Therefore, the base incremental pipeline schedule for the applicable functional areas (like GL and PO) must be set to run at a daily frequency before frequent 4-hour intraday intervals can be layered on top of it.
Why the Incorrect Answers are Wrong
Statement A: Private endpoints are used for secure VCN connectivity, but they do not bypass or alter the Autonomous Data Warehouse maintenance window.
Statement C: Semantic model update jobs are intentionally bypassed during frequent data refresh runs to maximize pipeline speed, and administrators certainly do not need to manually push a model update every 4 hours.
Statement D: The virus scanner utility is strictly for inspecting uploaded files for security risks and has no role in scanning intraday staging tables.
NEW QUESTION # 130
Which button becomes enabled once your FDI entitlements appear after subscription activation?
Answer: A
Explanation:
Explanation of the Correct Answer Create Instance (C): Once your Oracle Fusion Data Intelligence (FDI) subscription entitlements are successfully processed and appear in your cloud account, the Create Instance button becomes active and enabled, allowing you to proceed with provisioning your first FDI analytics instance.
Explanation of the Incorrect Answers
Order Service (A): This option is typically used to purchase or request new cloud services or add-on modules rather than serving as the activation milestone button unlocked by tenancy entitlements.
Enable Features (B): This is a configuration setting used after an instance has already been created to toggle specific preview or generally available features.
Bundle Repository (D): This relates to managing migration snapshots, customizations, or content packages within the analytics environment rather than the initial subscription provisioning workflow.
NEW QUESTION # 131
An organization completed the initial pipeline extraction for ERP Analytics six months ago using an Initial Extract Date set to January 1, 2020.
The implementer now attempts to edit the Initial Extract Date global parameter to January 1, 2015, to pull five additional years of historical data. What should the implementer expect?
Answer: C
Explanation:
Why the Correct Answer is Right
D). Locked Parameter and Reset Requirement: In Oracle Fusion Data Intelligence (FDI), once the initial data pipeline execution has run successfully, critical baseline settings like the Initial Extract Date become locked to prevent data corruption, orphan records, and timeline discrepancies in the Autonomous Data Warehouse. To expand the historical extraction boundary further back (e.g., from 2020 to 2015), the implementer must perform a data pipeline reset or re-initialization rather than simply editing the active parameter.
Why the Incorrect Answers are Wrong
A). The global parameter can be updated directly and will automatically trigger an incremental extract for the newly requested date range: Incremental extracts only capture changes made forward in time from the current high-water mark; they cannot automatically pull back historical gaps for years prior to the established initial boundary without a full reset.
B). The global parameter updates immediately but only applies to newly activated functional areas without affecting ERP Analytics: Global parameters govern the core extraction logic across the environment, and historical boundaries cannot be bypassed or treated as functional-area-specific subsets in this manner.
C). The global parameter is automatically overwritten by the latest transaction timestamp in Oracle Fusion Cloud Applications: The system does not automatically overwrite administrative configuration parameters based on source system timestamps; these boundaries are explicitly controlled by administrator inputs.
NEW QUESTION # 132
An admin user wants to use Bulk Data Augmentation to load several external, non-Fusion datasets that share the same connector, minimizing the number of extraction jobs needed.
Which two statements accurately describe Bulk Data Augmentation?
Answer: A,B
Explanation:
A and E
Detailed Explanations of the Correct Options
A). It submits a single extraction job covering multiple data stores to the Data Pipeline component: Bulk Data Augmentation allows administrators to group multiple data stores sharing the same connector into a single bundle, submitting them as one consolidated extraction job to the Data Pipeline framework to minimize management overhead.
E). It is especially useful for loading multiple external datasets that are functionally related, such as ledger or party entities: The feature is engineered specifically to streamline the loading and management of groups of related external non-Fusion datasets, such as associated ledger or party entities.
Detailed Explanations of the Incorrect Options
B). It requires each data store to use a different, unrelated connector: This statement is false because Bulk Data Augmentation specifically requires all included data stores to share the same connector or data source.
C). It supports Oracle Fusion Applications sources as well as external sources: This statement is incorrect because Bulk Data Augmentation is restricted exclusively to non-Fusion external data sources and does not support native Oracle Fusion Applications sources.
D). It replaces the need for the Data Pipeline component entirely: This statement is wrong because Bulk Data Augmentation utilizes the Data Pipeline component to process and execute the bundled job rather than replacing it.
NEW QUESTION # 133
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