Oracle 1z0-1080-24 Exam With Confidence Using Practice Dumps

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.

References:

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).

In Oracle Planning 2024, configuring and post-configuring the Financials module involves tasks to establish its structure and functionality. The three valid configuration or post-configuration tasks are:

A. Adding custom expense driver categories: Incorrect. While Financials supports driver-based planning (e.g., revenue drivers), adding custom expense driver categories is not a standard configuration task—it’s more of a customization within forms or rules, not a core setup step.

B. Adding custom dimensions: Correct. Adding custom dimensions (e.g., Region, Product) is a key configuration task in Financials to tailor the module to business needs, often done during or shortly after initial setup.

C. Completing Planning and Forecast Preparation: Correct. This mandatory configuration task defines the planning periods, scenarios, and versions for Financials, setting the foundation for budgeting and forecasting.

D. Setting up exchange rates (for multicurrency applications): Correct. For multicurrency-enabled Financials applications, configuring exchange rates is a critical task during or post-configuration to support currency conversion in financial planning.

E. Removing custom dimensions: Incorrect. Removing dimensions is not a standard configuration or post-configuration task—it’s a rare adjustment that could disrupt existing data and is not recommended as part of setup.

The Oracle documentation identifies B, C, and D as essential tasks during or after Financials configuration, ensuring the module meets planning and reporting requirements.

References:

Oracle Planning 2024 Implementation Study Guide: "Financials Configuration Tasks" (docs.oracle.com, Published 2024-10-15).

Oracle EPM Cloud Documentation: "Setting Up Financials" (docs.oracle.com, Published 2023-12-15, updated for 2024).

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.

References:

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).