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Oracle Planning 2024 Implementation Professional Sample Questions (Q12-Q17):

NEW QUESTION # 12
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?

  • A. For historical data, there should be atleast twice the amount of historical data as the number of prediction periods.
  • B. For future data, create a new insight by leveraging templates that include insight definitions.
  • C. For future data, first run predictions in a test environment to ensure there is no impact on production data.
  • D. 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.

Answer: A,C

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


NEW QUESTION # 13
Which four statements are true about the Optimize Dimension feature?

  • A. You refresh the database and then back up the application and download the snapshot before you optimize dimension order.
  • B. You can optimize the dimension order only for Financials and Workforce, and only for the provided BSO cubes that are created when you enable and configure.
  • C. You can optimize the dimension order only for Financials and Projects, and only for the provided BSO cubes that are created when you enable and configure.
    DYou optimize dimension order first in your test environment before you optimize dimension order in the production environment.
  • D. You can optimize the dimension order only for BSO cubes in Custom Planning applications.
  • E. The new optimized dimension order is maintained even if you enable additional features or other modules.

Answer: A,B,D,E

Explanation:
In Oracle Planning 2024, the Optimize Dimension feature enhances performance by reordering dimensions in BSO (Block Storage Option) cubes. Let's evaluate the six statements to determine the four that are true:
* A. The new optimized dimension order is maintained even if you enable additional features or other modules: True. Once optimized, the dimension order remains intact even if new features or modules are enabled, unless explicitly re-optimized or manually altered. This ensures performance stability post- optimization.
* B. You can optimize the dimension order only for BSO cubes in Custom Planning applications: True.
The Optimize Dimension feature is exclusively available for BSO cubes in Custom Planning applications, not for ASO (Aggregate Storage Option) cubes or module-based applications (e.g., Financials, Workforce), due to their predefined structures.
* C. You can optimize the dimension order only for Financials and Projects, and only for the provided BSO cubes that are created when you enable and configure: False. This statement is incorrect because the feature applies to Custom Planning applications, not specifically to Financials and Projects, which use predefined BSO cubes not eligible for user-driven dimension optimization.
* D. You optimize dimension order first in your test environment before you optimize dimension order in the production environment: True. Oracle recommends testing dimension optimization in a test environment first as a best practice to assess performance impacts and avoid risks in production, making this a procedural truth.
* E. You refresh the database and then back up the application and download the snapshot before you optimize dimension order: True. Before optimizing, Oracle advises refreshing the database to ensure data consistency, then backing up the application and downloading a snapshot to preserve a recovery point in case optimization causes issues.
* F. You can optimize the dimension order only for Financials and Workforce, and only for the provided BSO cubes that are created when you enable and configure: False. Similar to C, this is incorrect; optimization is not restricted to Financials and Workforce module cubes-it's for Custom Planning BSO cubes, not predefined module-specific cubes.
From these, the four true statements are:
* A - Persistence of the optimized order after feature/module changes.
* B - Restriction to BSO cubes in Custom Planning applications.
* D - Testing in a test environment first as a best practice.
* E - Refreshing and backing up before optimization.
The false statements (C and F) incorrectly limit the feature to specific modules (Financials, Projects, Workforce), whereas it's designed for Custom Planning applications. The Oracle documentation supports A, B, D, and E as true, aligning with the feature's functionality and recommended practices.
References:
* Oracle Planning 2024 Implementation Study Guide: "Optimize Dimension Feature" (docs.oracle.com, Published 2024-09-25).
* Oracle EPM Cloud Documentation: "BSO Dimension Optimization" (docs.oracle.com, Published 2023-
12-20, updated for 2024).


NEW QUESTION # 14
What two levels of workforce detail granularity would you need to perform Merit-Based Planning?

  • A. Job
  • B. Employee and Job
  • C. Employee
  • D. Merit

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.
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).


NEW QUESTION # 15
You want to select a probability distribution for your Strategic Modeling simulation. The minimum and maximum are fixed, and you know the most likely values.
Which probability distribution is useful with limited data in situations such as sales estimates, inventory numbers, and marketing costs in Strategic Modeling simulations?

  • A. Triangular
  • B. Uniform
  • C. Beta PERT
  • D. Normal
  • E. Lognormal

Answer: A

Explanation:
In Oracle Planning 2024's Strategic Modeling simulations, selecting a probability distribution for scenarios with fixed minimum and maximum values and a known most likely value (e.g., sales estimates, inventory, marketing costs) points to:
A: Beta PERT: Incorrect. Beta PERT uses min, max, and most likely values but requires more data to shape the curve accurately, making it less ideal with limited data.
B: Uniform: Incorrect. Uniform assumes equal probability between min and max, ignoring the most likely value, which doesn't fit this scenario.
C: Triangular: Correct. The Triangular distribution uses minimum, maximum, and most likely values, making it simple and effective for limited data situations like sales or costs.
D: Normal: Incorrect. Normal requires mean and standard deviation, not just min, max, and most likely, and assumes more data availability.
E: Lognormal: Incorrect. Lognormal is skewed and suited for data with a positive range, requiring more statistical input than provided here.
C: The Oracle documentation recommends the Triangular distribution for its simplicity and suitability with limited data, making C the correct answer.
References:
Oracle Planning 2024 Implementation Study Guide: "Probability Distributions in Strategic Modeling" (docs.oracle.com, Published 2024-10-25).
Oracle EPM Cloud Documentation: "Simulation Distributions" (docs.oracle.com, Published 2023-11-
30, updated for 2024).


NEW QUESTION # 16
In which three ways can you create data maps that copy data using Smart Push?

  • A. Between two Planning instances
  • B. From Planning to Tax Reporting Cloud
  • C. From Tax Reporting Cloud to Planning
  • D. From Financial Consolidation and Close to Planning
  • E. From Planning to Financial Consolidation and Close

Answer: A,D,E

Explanation:
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).


NEW QUESTION # 17
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