Free and Premium Oracle 1z0-1080-24 Dumps Questions Answers

In Oracle Planning 2024 Implementation, the Benefits and Taxes Wizard is used to configure employee-related financial components, such as additional earnings, benefits, and taxes, within the Workforce module. To configure Additional Earnings specifically, members must be imported into theComponentdimension. This dimension is designed to categorize and manage various types of earnings, benefits, and taxes that apply to employees.

TheComponentdimension acts as a foundational structure in the Workforce module, allowing administrators to define and import members (e.g., "Bonus," "Overtime," or other additional earnings types) that can then be associated with employees via the wizard. The wizard uses these members to calculate and allocate costs accurately across the workforce plan.

B. Account: While the Account dimension is critical for financial reporting and calculations, it is not the dimension where Additional Earnings members are imported in the Benefits and Taxes Wizard. Accounts are typically used to map earnings to financial statements, not to define the earnings types themselves.

C. Property: The Property dimension is used for employee or job attributes (e.g., location, department), not for configuring earnings types in the wizard.

D. Pay Type: Although Pay Type is related to salary and wage classifications, it is not the dimension used for importing Additional Earnings members in the Benefits and Taxes Wizard. Pay Type is more about categorizing base pay structures rather than additional earnings components.

References

Oracle Enterprise Performance Management Cloud Documentation: "Administering Workforce – Benefits and Taxes Wizard" (docs.oracle.com, updated 2024). Specifies that "members for additional earnings must be imported into the Component dimension" for configuration in the wizard.

Oracle Planning 2024 Implementation Study Guide: Highlights the Component dimension as the target for importing earnings-related members in Workforce configuration.

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).

In Oracle Planning 2024, importing data from a file and saving the operation as a job involves specific steps and options. The two true statements about Import Data jobs are:

A. Reference a data file stored locally when creating the Import Data job: Incorrect. Oracle EPM Cloud does not allow referencing files stored locally on a user’s machine for Import Data jobs. Files must be uploaded to the cloud environment (e.g., Inbox) for processing.

B. Select the option to clear data before import: Correct. When configuring an Import Data job, you can choose to clear existing data in the target before importing new data. This option ensures a clean slate for the import, avoiding data duplication or overlap, and is a standard feature in the job setup.

C. Upload your data file to the Inbox before running the Import Data job: Correct. Oracle requires that data files be uploaded to the Inbox (or another cloud storage location like the Outbox) before scheduling or running an Import Data job. The job then references this uploaded file for execution.

D. Include the path for the data file stored on the server: Incorrect. While you specify a file name in the job definition, you do not manually include a server path. The system automatically manages file locations within the cloud environment (e.g., Inbox), and users select files from there, not via explicit server paths.

The documentation confirms that uploading the file to the Inbox and optionally clearing data are key aspects of setting up an Import Data job, making B and C the true statements.

References:

Oracle Planning 2024 Implementation Study Guide: "Importing Data and Creating Jobs" (docs.oracle.com, Published 2024-10-20).

Oracle EPM Cloud Documentation: "Managing Data Import Jobs" (docs.oracle.com, Published 2023-11-05, updated for 2024).

To include asset-related expenses such as depreciation, amortization, and insurance in Financials reporting within Oracle Planning 2024 Implementation, integration between the Capital and Financials modules is required. The correct setup involves enabling integration from the Capital module to share expense data with Financials.

A. In Capital, for Expense, enable Integration with Financials: This option activates the integration feature in the Capital module under the Expense section, allowing asset-related expenses (e.g., depreciation, amortization, insurance) to be automatically pushed to Financials. Once enabled, Capital maps these expenses to the appropriate Financials accounts, ensuring seamless reporting. This is the standard method outlined in Oracle documentation for sharing Capital data with Financials.

B. In Financials, on the Enable page, in Map/Rename Dimensions, add a custom dimension called Assets: Adding a custom dimension in Financials is not the correct approach for integrating Capital expenses. Custom dimensions are for extending dimensionality, not for enabling data sharing between modules.

C. In Capital, on the Configure page, select Map Capital Accounts and map capital accounts to the corresponding account in Financials: While account mapping is part of the integration process, it is a subsequent step that occurs after enabling integration. The primary action is enabling the integration itself, not just mapping accounts.

D. In Financials, in the Asset Expenses Wizard, map each component to a Financials account: There is no "Asset Expenses Wizard" in Financials for this purpose. The integration is driven from Capital, not through a wizard in Financials.

References

Oracle Enterprise Performance Management Cloud Documentation: "Administering Capital – Integration with Financials" (docs.oracle.com, updated 2024). Specifies that "enabling Integration with Financials under Expenses in Capital" is required to share depreciation, amortization, and insurance expenses.

Oracle Planning 2024 Implementation Study Guide: Notes that enabling integration from Capital’s Expense section is the key step for including asset-related expenses in Financials reporting.

In Oracle Planning 2024’s Strategic Modeling module, balancing a model with a deficit involves adjusting funding options to ensure cash flow or balance sheet equilibrium. When there’s a deficit (e.g., insufficient cash), you can either increase inflows or decrease outflows. The two valid statements are:

A. You can decrease Preferred to balance the model: Incorrect. "Preferred" typically refers to preferred stock (an equity component), but decreasing it (e.g., reducing preferred equity) would not directly increase available funds to cover a deficit—it might even worsen it by reducing capital.

B. You can increase Debt or Equity to balance the model: Correct. Increasing Debt (e.g., issuing loans) or Equity (e.g., issuing stock) provides additional funds to cover a deficit, a common strategy in Strategic Modeling to balance cash needs.

C. You can decrease Dividends or Assets to balance the model: Correct. Decreasing Dividends reduces cash outflows, retaining more funds, while decreasing Assets (e.g., selling assets) generates cash inflows, both helping to balance the model.

D. You can increase Contra-Equity to balance the model: Incorrect. Contra-Equity (e.g., treasury stock) reduces total equity when increased (e.g., buying back shares), which decreases available funds, not helping to balance a deficit.

The Oracle documentation highlights that increasing Debt/Equity or decreasing Dividends/Assets are standard funding options in Strategic Modeling to address deficits, making B and C the correct statements.

References:

Oracle Planning 2024 Implementation Study Guide: "Balancing Models in Strategic Modeling" (docs.oracle.com, Published 2024-09-15).

Oracle EPM Cloud Documentation: "Funding Options in Strategic Scenarios" (docs.oracle.com, Published 2023-12-10, updated for 2024).

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.

References:

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).

In Oracle Planning 2024, cell-level security restricts access to specific data intersections using anchor and nonanchor dimensions. The two true statements are:

A. Anchor dimensions are always required in the cube that is used in the cell-level security definition: Correct. Anchor dimensions (e.g., Entity, Scenario) are mandatory in the security definition to specify the primary scope of access control within the cube.

B. Anchor dimensions are never required in the cube that is used in the cell-level security definition: Incorrect. Anchor dimensions are always required, contradicting this statement.

C. By default, nonanchor dimensions are not required: Correct. Nonanchor dimensions (e.g., Account, Period) are optional by default in cell-level security definitions, allowing flexibility in granularity unless explicitly included.

D. By default, nonanchor dimensions are required. You can change this setting later: Incorrect. Nonanchor dimensions are not required by default, and there’s no setting to make them mandatory—it’s an optional inclusion.

The Oracle documentation specifies that A (anchor necessity) and C (nonanchor optional) align with cell-level security behavior, making them the correct answers.

References:

Oracle Planning 2024 Implementation Study Guide: "Cell-Level Security Configuration" (docs.oracle.com, Published 2024-09-30).

Oracle EPM Cloud Documentation: "Anchor and Nonanchor Dimensions" (docs.oracle.com, Published 2023-12-05, updated for 2024).

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.

References:

Oracle Planning 2024 Implementation Study Guide: "Configuring Insights and Auto Predict" (docs.oracle.com, Published 2024-10-15).

In Oracle Planning 2024, scheduling jobs such as a weekly data import is managed through the Jobs interface, which provides options for automation, monitoring, and notifications. Let’s evaluate the provided statements to identify the two that are true:

A. You can check the execution status of a job only if it completed: This is false. The Jobs console in Oracle EPM allows users to check the status of a job (e.g., Running, Completed, Failed) at any time, not just after completion. Real-time monitoring is a key feature.

B. You can set the daily maintenance time when scheduling cloning environment jobs: This is false. Daily maintenance time is a system-wide setting controlled by administrators via Application Settings, not something adjustable when scheduling specific jobs like cloning or data imports.

C. You can set to receive notifications when the job has completed: This is true. When scheduling a job (e.g., Import Data), users can enable email notifications to be alerted upon job completion, success, or failure, enhancing job management.

D. You can schedule an Import Data job to run later at intervals: This is true. The scheduling feature supports recurring jobs, such as weekly data imports, allowing users to define the start time and frequency (e.g., daily, weekly) for tasks like importing data from external sources.

E. You can delete that are currently processing: This is false. Jobs that are currently processing (i.e., in a "Running" state) cannot be deleted until they complete or fail, as per Oracle’s job management rules.

Thus, the two true statements are C and D, reflecting the flexibility of scheduling recurring Import Data jobs and receiving completion notifications, both of which are explicitly supported in Oracle Planning 2024.

References:

Oracle Planning 2024 Implementation Study Guide: "Managing Jobs and Scheduling" (docs.oracle.com, Published 2024-08-22).

Oracle EPM Cloud Documentation: "Scheduling Jobs in Planning" (docs.oracle.com, Published 2023-12-10, updated for 2024).

Oracle Planning Administration Guide: "Monitoring and Notifications" (docs.oracle.com, Published 2024-10-01).

In Oracle Planning 2024, the Dimensions page in a Custom Planning application allows administrators to manage dimensions. The three tasks you can perform are:

A. Set the order of precedence: Correct. This task adjusts the display order of dimensions in forms and reports, enhancing usability, and is manageable on the Dimensions page.

B. Delete dimensions: Correct. Administrators can delete custom dimensions from the application on the Dimensions page, provided they are not in use (e.g., no data or dependencies).

C. Clear dimension members: Incorrect. Clearing members (e.g., removing all members from a dimension) is not a task performed on the Dimensions page; it’s typically done via metadata import with the "Clear Members" option or manual member deletion.

D. Select the dimension density: Incorrect. Dimension density (sparse/dense) is set during cube creation or in Cube Designer, not adjustable on the Dimensions page post-creation.

E. Set the Evaluation Order: Correct. This task defines the order in which dimensions are evaluated for calculations (e.g., resolving member formulas), configurable on the Dimensions page for custom applications.

The Oracle documentation confirms that A, B, and E are tasks supported on the Dimensions page for Custom Planning applications, making them the correct answers.

References:

Oracle Planning 2024 Implementation Study Guide: "Managing Dimensions in Custom Applications" (docs.oracle.com, Published 2024-10-10).

Oracle EPM Cloud Documentation: "Dimensions Page Tasks" (docs.oracle.com, Published 2023-11-15, updated for 2024).

In Oracle Planning 2024, the approval process uses an approval unit hierarchy where parent and child relationships influence the review workflow. The two ways these relationships affect the process are:

A. When the status of all children changes to one status (for example, Signed Off), the parent status changes to the same status: Correct. In a bottom-up approval process, when all child approval units reach a uniform status (e.g., Signed Off, Approved), the parent’s status automatically updates to match, reflecting the completion of the children’s review.

B. When you approve a parent, its children are Signed Off: Incorrect. Approving a parent does not automatically sign off its children; the workflow typically moves bottom-up, requiring children to be approved first.

C. After all children are promoted to the same owner, the parent status is changed to Signed Off: Incorrect. Promotion to an owner changes ownership, not necessarily status (e.g., Signed Off). Status changes are driven by approval actions, not just ownership.

D. After all children are promoted to the same owner, the parent is promoted to the owner: Correct. In the approval hierarchy, once all child units are promoted to a new owner (e.g., for review), the parent unit is also promoted to that owner, ensuring the hierarchy progresses together.

The Oracle documentation confirms that A (status aggregation) and D (owner promotion) are key behaviors of parent/child relationships in the approval process, making them the correct answers.

References:

Oracle Planning 2024 Implementation Study Guide: "Approval Unit Hierarchies" (docs.oracle.com, Published 2024-09-25).

Oracle EPM Cloud Documentation: "Managing Approvals" (docs.oracle.com, Published 2023-12-20, updated for 2024).

In Oracle Planning 2024, Smart Push is a feature within data maps that enables efficient, real-time data movement between Oracle EPM Cloud applications or instances. It copies data dynamically when triggered (e.g., via forms or rules). The three supported ways to create data maps with Smart Push are:

A. From Planning to Tax Reporting Cloud: Incorrect. Smart Push does not support direct data movement from Planning to Tax Reporting Cloud, as these modules lack a predefined integration path for this feature.

B. From Tax Reporting Cloud to Planning: Incorrect. Similarly, Smart Push does not facilitate data movement from Tax Reporting Cloud to Planning.

C. From Financial Consolidation and Close to Planning: Correct. Smart Push supports moving consolidated data (e.g., actuals) from Financial Consolidation and Close (FCC) to Planning for planning purposes.

D. From Planning to Financial Consolidation and Close: Correct. Smart Push allows pushing planned data from Planning to FCC for consolidation or reporting.

E. Between two Planning instances: Correct. Smart Push can move data between two Planning instances (e.g., test and production environments) to synchronize data.

These three options—C, D, and E—are explicitly supported by Smart Push in Oracle EPM Cloud, as per the documentation, enabling seamless data integration across these applications.

References:

Oracle Planning 2024 Implementation Study Guide: "Using Smart Push in Data Maps" (docs.oracle.com, Published 2024-09-30).

Oracle EPM Cloud Documentation: "Data Integration with Smart Push" (docs.oracle.com, Published 2023-12-20, updated for 2024).

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).

As a Service Administrator in the context of Oracle Planning 2024 Implementation, application diagnostics is a critical tool used at design time to ensure that applications are free of design flaws before they are deployed into production. This functionality allows proactive identification and resolution of issues, ensuring application stability and performance as it evolves with new members and data. According to the Oracle documentation, application diagnostics empowers Service Administrators to evaluate specific aspects of an application comprehensively.

D. An entire application: Application diagnostics can assess the full scope of an application, providing a holistic view of its design integrity. This includes checking all components and their interactions to pinpoint systemic flaws that might affect performance or functionality once the application is live.

C. Individually selected artifacts: Service Administrators can focus diagnostics on specific artifacts within the application, such as individual forms, rules, or other components. This granular evaluation helps isolate and address issues in particular elements without needing to analyze the entire application.

E. Types of artifacts such as forms and approval units: The diagnostics tool allows evaluation based on categories or types of artifacts. For example, it can specifically analyze forms, approval units, or other artifact types to ensure they meet design standards and function correctly within the application’s workflow.

The optionsA. Error log filesandB. Migration snapshotsare not explicitly mentioned as evaluable components within the scope of application diagnostics at design time in the Oracle Planning 2024 Implementation documentation. Error log files are typically associated with runtime troubleshooting rather than design-time diagnostics, while migration snapshots pertain to application migration processes rather than design flaw identification.

References

Oracle Enterprise Performance Management Cloud Documentation: "About Application Diagnostics" (docs.oracle.com, published 2018-03-22, updated as of 2024). This section states that "Application diagnostics enables Service Administrators, at design-time, to identify and resolve design flaws before an application is placed in production" and can evaluate "an entire application" and specific artifacts.

Oracle Planning 2024 Implementation Study Guide: Application diagnostics section confirms the ability to assess "entire applications" and "individual or types of artifacts such as forms and approval units" to ensure design integrity.

These references align with the capabilities described for Service Administrators using application diagnostics in the Oracle Planning 2024 Implementation framework.