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| Section | Objectives |
|---|---|
| Data Integration and Management | - Data loading methods
|
| Oracle Enterprise Performance Management (EPM) Cloud Overview | - EPM architecture and components
|
| AI and Predictive Planning Features | - Predictive forecasting
|
| Planning Application Configuration | - Application design
|
| Reporting and Analysis | - Financial reporting
|
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質問 # 116
With Machine Learning, which type of prediction would you define to predict revenue using several input variables such as price, promotions, industry market size, and climate?
正解：B
解説：
In Oracle Planning 2024's Machine Learning capabilities, particularly with the "Bring Your Own ML" feature, predictions can be defined based on the type of analysis required. To predict revenue using multiple input variables such as price, promotions, industry market size, and climate, the appropriate prediction type is:
A . Dimension Prediction: Incorrect. This type is not a standard term in Oracle's ML framework for Planning; it suggests predicting across dimensions, which isn't specific to multi-variable revenue prediction.
B . Source Prediction: Incorrect. This is not a defined prediction type in Oracle Planning's ML documentation; it might imply source data analysis, but it's not applicable here.
C . Forecast Prediction: Incorrect. While forecasting involves predicting future values, "Forecast Prediction" is not a specific ML type in Oracle, and it doesn't emphasize the use of multiple variables.
D . Multivariate Prediction: Correct. Multivariate prediction involves using multiple input variables (e.g., price, promotions, market size, climate) to predict an outcome (e.g., revenue). Oracle's ML integration supports importing PMML models that handle multivariate analysis, aligning with this scenario.
The Oracle documentation confirms that Multivariate Prediction is the type suited for complex predictions with several input variables, making D the correct answer.
Oracle Planning 2024 Implementation Study Guide: "Machine Learning Prediction Types" (docs.oracle.com, Published 2024-10-15).
Oracle EPM Cloud Documentation: "Bring Your Own ML: Multivariate Models" (docs.oracle.com, Published 2023-11-20, updated for 2024).
質問 # 117
Which client requirement aligns with using a custom BSO plan type along with or in place of the Workforce module?
正解：C
質問 # 118
Which configuration task is NOT mandatory in Workforce?
正解：C
解説：
In Oracle Planning 2024's Workforce module, certain configuration tasks are mandatory to enable basic functionality, while others are optional depending on the organization's needs. The task that is not mandatory is:
A . Employee Type: Incorrect. Defining Employee Type (e.g., full-time, part-time) is mandatory because it establishes the categories of employees to be planned, forming the foundation of workforce data.
B . Planning and Forecast Preparation: Incorrect. This task is mandatory as it sets up the planning periods, scenarios, and versions, which are essential for Workforce to function within the broader Planning application.
C . Benefits and Taxes: Correct. Configuring Benefits and Taxes is optional. While Workforce provides predefined options to calculate benefits (e.g., health insurance) and taxes (e.g., payroll taxes), organizations can choose not to configure these if they do not need detailed compensation planning beyond salaries.
D . Workforce Assumptions: Incorrect. Workforce Assumptions (e.g., hiring rates, salary increases) are mandatory to drive calculations and populate employee data over time.
The Oracle documentation specifies that while Benefits and Taxes enhance Workforce planning, they are not required for core functionality, making C the non-mandatory task.
Oracle Planning 2024 Implementation Study Guide: "Configuring Workforce Module" (docs.oracle.com, Published 2024-10-01).
Oracle EPM Cloud Documentation: "Workforce Configuration Tasks" (docs.oracle.com, Published 2023-11-20, updated for 2024).
質問 # 119
Which two types of Groovy Rules are supported by Oracle?
正解：A、C
解説：
In Oracle Planning 2024, Groovy Rules enhance business logic flexibility. Oracle supports two main types of Groovy Rules:
A . Rules that overwrite member formulas that combine operators and calculation functions, and perform calculations on members in Dimension Editor: Incorrect. Groovy Rules do not overwrite member formulas in the Dimension Editor; they operate at runtime and are defined in the Rules editor, not as static dimension overrides.
B . Rules that can dynamically generate calculation scripts at run time based on contexts such as runtime prompts, the POV, the current grid, and so on: Correct. Oracle supports Groovy Rules that generate dynamic calc scripts based on runtime contexts (e.g., POV, grid data, prompts), enabling adaptive calculations.
C . Pure Groovy rules that can perform data validations and cancel the operation if the data entered violates company policies: Correct. Pure Groovy Rules can validate data (e.g., checking ranges or policies) and cancel operations (e.g., via exceptions), a key feature for enforcing business rules.
D . Rules that dynamically calculate data and perform validation checks in tile charts and infolets: Incorrect. Groovy Rules operate on cubes and forms, not directly within tile charts or infolets, which are UI elements driven by underlying data, not rule execution points.
The Oracle documentation confirms B (dynamic script generation) and C (data validation) as supported Groovy Rule types, making them the correct answers.
Oracle Planning 2024 Implementation Study Guide: "Groovy Rules in Planning" (docs.oracle.com, Published 2024-10-15).
Oracle EPM Cloud Documentation: "Supported Groovy Rule Types" (docs.oracle.com, Published 2023-11-20, updated for 2024).
質問 # 120
Which two can be used to push data between cubes?
正解：B、D
解説：
In Oracle Planning 2024, pushing data between cubes (e.g., from one cube to another within the same application) can be accomplished using specific tools. The two methods that facilitate this are:
A . Data Integration: Incorrect. Data Integration is used to import data from external sources (e.g., files, other systems) into Planning, not to push data between cubes within the same application.
B . Data Maps: Correct. Data Maps allow you to define mappings and push data between cubes (or applications) using Smart Push or manual execution. This is a primary method for intra-application data movement.
C . Copy Data: Correct. The Copy Data feature enables administrators to copy data from one cube to another within the same Planning application, specifying dimensions and members to transfer.
D . Import Data: Incorrect. Import Data is designed to bring external data into a cube from a file, not to push data between existing cubes.
Both Data Maps (with Smart Push for real-time updates) and Copy Data (for batch-style transfers) are explicitly supported for moving data between cubes, as per Oracle's documentation, making B and C the correct answers.
Oracle Planning 2024 Implementation Study Guide: "Moving Data Between Cubes" (docs.oracle.com, Published 2024-09-05).
Oracle EPM Cloud Documentation: "Data Maps and Copy Data Features" (docs.oracle.com, Published 2023-12-25, updated for 2024).
質問 # 121
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