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Oracle 1Z0-1080-26 Exam Syllabus Topics:

Section	Objectives
Topic 1: Reporting and Analysis	- Financial reporting 1. Dashboard configuration 2. Report creation and customization - Analytics 1. Performance monitoring 2. Variance analysis
Topic 2: Oracle Enterprise Performance Management (EPM) Cloud Overview	- Security and user management 1. Application security configuration 2. Roles and access control - EPM architecture and components 1. Planning Cloud services overview 2. Data flow and integration concepts
Topic 3: Data Integration and Management	- Data validation and transformation 1. Mapping and transformation rules 2. Data reconciliation techniques - Data loading methods 1. Source system integration 2. Data imports and exports
Topic 4: Planning Application Configuration	- Business rules and calculations 1. Rule creation and validation 2. Calculation logic for planning scenarios - Application design 1. Planning models setup 2. Metadata structure and dimensions
Topic 5: AI and Predictive Planning Features	- Predictive forecasting 1. Trend-based planning models 2. Forecast generation and adjustment - AI-assisted planning insights 1. Anomaly detection and recommendations 2. Driver-based planning concepts

>> 1Z0-1080-26復習攻略問題 <<

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Oracle Planning + AI 2026 Implementation Professional 認定 1Z0-1080-26 試験問題 (Q62-Q67):

質問 # 62
Which task must be completed before EPM administrators import a Machine Learning model into Planning?

A. EPM Administrators create a data model and push data to it to generate a PMML file.
B. EPM Administrators create data maps and Groovy rules to move and process data.
C. Data Scientists build and train the ML model in a data science tool and save it as a PMML file.
D. Data Scientists create Groovy rules designed to evaluate historical data and identify patterns.

正解：C

解説：
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).

質問 # 63
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 future data, create a new insight by leveraging templates that include insight definitions.
B. For historical data, there should be at least twice the amount of historical data as the number of prediction periods.
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.
D. For future data, first run predictions in a test environment to ensure there is no impact on production data.

正解：B、D

解説：
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).

質問 # 64
After loading data from external systems, you can quickly perform task that can add or update existing employee and job data using Mass Update forms. Which two actions can you take on the Mass Update forms in Workforce?

A. Delete the loaded salary information and then run the data mal for Compensation Data to calculate the updated benefits.
B. If an existing employee was not loaded from the load file, you won't be able to add their compensation before first adding them to the source system and reloading the file.
C. Add a new record for an existing employee who was not in the load file, and apply configured defaults.
D. Change existing properties and then override default assignments and rates.

正解：C、D

質問 # 65
A Department Manager requires access to all accounts in their own department, but only a certain account in all other departments. With the usual metadata security, the Manager would have access to all accounts across all departments.

A. Access control
B. Cell-level security
C. Member security
D. Artifact security

正解：B

質問 # 66
Which Planning feature or component enables you to create, validate, deploy, and launch business rules?

A. Data Integration
B. Validation Rules Editor
C. Member Formulas
D. Calculation Manager

正解：D

質問 # 67
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