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| Section | Weight | Objectives |
|---|---|---|
| Calculation Rules and Logic | 10% | - Create and manage business rules and scripts - Implement Groovy rules and advanced calculations |
| Reporting, Approval, and Maintenance | 5% | - Create and distribute reports and analytics - Set up and manage approval workflows - Perform system maintenance and updates |
| Data Management and Integration | 15% | - Integrate with external sources and systems - Load, map, and validate data |
| Forms, Dashboards, and User Interface | 15% | - Manage navigation flows and user experience - Design and maintain forms and templates - Build and configure dashboards and infolets |
| Planning Application Setup and Configuration | 20% | - Set up and administer security and access permissions - Create and configure Planning applications - Manage dimensions, hierarchies, and metadata |
| Business Process Configuration | 25% | - Configure Workforce module - Configure Strategic Modeling - Configure Projects module - Configure Financials module - Configure Capital module |
| Intelligent Performance Management and AI Features | 10% | - Leverage AI-driven planning and forecasting capabilities - Configure IPM and predictive planning features |
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NEW QUESTION # 47
Which option describes Intelligent Performance Management (IPM) Insights?
Answer: C
NEW QUESTION # 48
Which task must be completed before EPM administrators import a Machine Learning model into Planning?
Answer: B
Explanation:
Before an EPM (Enterprise Performance Management) administrator can import a Machine Learning (ML) model into Oracle Planning, a prerequisite task must be completed by data scientists. According to Oracle's "Bring Your Own ML" feature in the Planning application, the process begins with data scientists gathering historical data related to a business problem, training an ML algorithm, and generating a Predictive Model Markup Language (PMML) file using a third-party data science tool or Oracle Data Science Cloud. This PMML file represents a fully trained ML model that can then be imported into the Planning application by an EPM administrator.
Option A is correct because it aligns with this prerequisite step: the ML model must be pretrained and saved as a PMML file before the import process can begin. Option B is incorrect because Groovy rules are not created by data scientists to evaluate historical data; instead, these rules are automatically generated by the Planning application during the import process to integrate the ML model with the application. Option C is also incorrect, as EPM administrators do not generate PMML files by creating data models and pushing data-instead, they import an existing PMML file. Finally, Option D is incorrect because while EPM administrators may create data maps and Groovy rules as part of the deployment process, this occurs after the PMML file is imported, not before.
The Oracle Planning 2024 Implementation documentation emphasizes that the "Bring Your Own ML" functionality relies on importing a prebuilt PMML file, making the data scientists' role in building and training the model a mandatory first step.
Oracle Planning 2024 Implementation Study Guide: "Bring Your Own ML: About Machine Learning Model Import" (docs.oracle.com, Published 2024-09-04).
Oracle EPM Cloud Documentation: "Importing ML Models" (docs.oracle.com, Published 2022-06-17, updated for 2024).
NEW QUESTION # 49
You want to analyze past data and predicted data to help you find patterns and insights into data that you might not have found on your own. To accomplish this, you configure Insights with Auto Predict.
Which two are Oracle EPM guidelines for implementing Insights and Auto Predict?
Answer: A,D
Explanation:
In Oracle Planning 2024, configuring Insights with Auto Predict allows users to analyze past and predicted data to uncover patterns and insights. Oracle provides specific guidelines to ensure effective implementation:
A . For future data, create a new insight by leveraging templates that include insight definitions: Incorrect. While templates can be used to set up Insights, this is not a specific Oracle guideline for implementing Auto Predict. Auto Predict relies on historical data and predictive algorithms, not predefined insight templates for future data.
B . For historical data, there should be at least twice the amount of historical data as the number of prediction periods: Correct. Oracle recommends having sufficient historical data-specifically, at least twice the number of periods you intend to predict-to ensure the accuracy of Auto Predict's machine learning algorithms. For example, predicting 12 months requires at least 24 months of historical data.
C . For historical data, create the Insights job using the lowest level of Period members possible so that the greatest amount of historical data can be used: Incorrect. While granularity matters, Oracle does not mandate using the lowest level of Period members (e.g., days instead of months) as a guideline. The focus is on the quantity of historical data, not necessarily the lowest level of aggregation.
D . For future data, first run predictions in a test environment to ensure there is no impact on production data: Correct. Oracle advises testing Auto Predict in a non-production environment to validate results and avoid unintended impacts on live data, aligning with best practices for predictive analytics deployment.
The two guidelines-B and D-are explicitly outlined in Oracle's documentation for Insights and Auto Predict to ensure reliable predictions and safe implementation.
Oracle Planning 2024 Implementation Study Guide: "Configuring Insights and Auto Predict" (docs.oracle.com, Published 2024-10-15).
NEW QUESTION # 50
What two levels of workforce detail granularity would you need to perform Merit-Based Planning?
Answer: B,C
Explanation:
In Oracle Planning 2024's Workforce module, Merit-Based Planning involves planning salary increases or adjustments based on employee performance (merit). To perform this, you need workforce data at a level of granularity that includes individual employee details. The two levels required are:
A . Merit: Incorrect. "Merit" is not a granularity level; it's a planning concept or assumption applied to employee data, not a structural level of detail.
B . Employee and Job: Correct. This level combines employee-specific data (e.g., individual identity) with job-specific data (e.g., role, grade), enabling merit-based adjustments tailored to both the person and their position.
C . Job: Incorrect. Job-level granularity (e.g., aggregated data for a role) lacks individual employee details, which are necessary for merit-based planning.
D . Employee: Correct. Employee-level granularity provides the individual data (e.g., current salary, performance rating) needed to calculate merit increases for specific employees.
Merit-Based Planning requires at least Employee-level detail, and often Employee and Job for more precise planning (e.g., tying merit to job roles or grades). The Oracle documentation confirms these as the key granularity levels for this functionality, making B and D the correct answers.
Oracle Planning 2024 Implementation Study Guide: "Merit-Based Planning in Workforce" (docs.oracle.com, Published 2024-10-10).
Oracle EPM Cloud Documentation: "Workforce Granularity Levels" (docs.oracle.com, Published 2023-11-15, updated for 2024).
NEW QUESTION # 51
You must assign a Planning user with a Cloud EPM predefined role that allows them to create and administer Planning or Planning Modules and service components. This role should also allow them to grant permissions to other users.
Which of the following predefined role must you assign this Planning user?
Answer: C
NEW QUESTION # 52
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