Steve Allen Steve Allen's Profile Page

Steve Allen Steve Allen

0 Course Enrolled • 0 Course Completed

Biography

1z0-1080-24最新問題 & 1z0-1080-24復習時間

Oracle 1z0-1080-24資格認定はバッジのような存在で、あなたの所有する専業技術と能力を上司に直ちに知られさせます。次のジョブプロモーション、プロジェクタとチャンスを申し込むとき、Oracle 1z0-1080-24資格認定はライバルに先立つのを助け、あなたの大業を成し遂げられます。

Oracle 1z0-1080-24 認定試験の出題範囲：

トピック
出題範囲

トピック 1

計画モジュールの概要: この試験のセクションでは、モジュールプランナーのスキルを測定し、計画モジュールを紹介します。モジュールの統合、モジュール計画のベストプラクティスの活用、財務、労働力、資本、プロジェクトの構成などが含まれます。

トピック 2

計画データに関するレポート: 試験のこのセクションでは、レポートアナリストのスキルを測定し、計画データに関するレポートの作成について説明します。これには、組織のパフォーマンスに関する洞察を提供するための EPM データに関するレポートが含まれます。

トピック 3

承認の管理: この試験のセクションでは、承認プロセスマネージャーのスキルを測定し、Planning での承認ワークフローの設定について説明します。組織内での意思決定を効率化するために承認プロセスを構成する方法について説明します。

トピック 4

計画の維持: 試験のこのセクションでは、システム管理者のスキルを測定し、計画アプリケーションの保守タスクの監視に焦点を当てます。ジョブのスケジュール設定、自動化タスク用の EPM Automate の管理、データのバックアップ、メタデータの移行、環境のクローン作成、システム更新の維持などが含まれます。

トピック 5

インテリジェントパフォーマンス管理の構成: 試験のこのセクションでは、機械学習スペシャリストのスキルを測定し、IPM コンポーネントの構成に重点を置きます。計画アプリケーション内での予測分析用の機械学習モデルの設定が含まれます。

トピック 6

フォーム、ダッシュボード、ナビゲーションフローの管理: この試験のセクションでは、ダッシュボードデザイナーのスキルを測定し、フォーム、ダッシュボード、検証ルールの設計について扱います。また、Planning アプリケーション内でのユーザーエクスペリエンスを合理化するためのナビゲーションフローの作成と管理も含まれます。

>> 1z0-1080-24最新問題 <<

便利なOracle 1z0-1080-24｜権威のある1z0-1080-24最新問題試験｜試験の準備方法Oracle Planning 2024 Implementation Professional復習時間

Fast2testは、最新の1z0-1080-24試験トレントが能力を強化し、1z0-1080-24試験に合格して認定を取得するのに非常に役立つと深く信じています。嫌がらせから抜け出すために、1z0-1080-24学習教材は高品質で高い合格率を備えています。ほとんどの時間インターネットにアクセスできない場合、どこかに行く必要がある場合はオフライン状態ですが、1z0-1080-24試験のために学習したい場合。当社のウェブサイトは、優れた1z0-1080-24試験問題の助けを借りて問題の解決に役立ちます。

Oracle Planning 2024 Implementation Professional 認定 1z0-1080-24 試験問題 (Q50-Q55):

質問 # 50
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, first run predictions in a test environment to ensure there is no impact on production data.
C. For future data, create a new insight by leveraging templates that include insight definitions.
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.

正解：A、B

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

質問 # 51
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?

A. Forecast Prediction
B. Multivariate Prediction
C. Dimension Prediction
D. Source Prediction

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.
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).

質問 # 52
You are preparing to design a report in Cloud EPM Planning using Report Designer. Which two statements are true about data sources in reports?

A. Control the way a report object is printed on the page relative to other report objects.
B. Insert a report object into the report if you are in Modify mode.
C. Include a chart in your report without displaying the underlying data grid.
D. Insert charts in the report header to provide a quick visual summary of data.

正解：A、C

解説：
When designing reports in Oracle Cloud EPM Planning using Report Designer, data sources and report objects (e.g., grids, charts) have specific capabilities. The two true statements about data sources in reports are:
* A. Insert charts in the report header to provide a quick visual summary of data: Incorrect. Report Designer does not support inserting charts directly into the report header; charts are added as objects within the report body, linked to data sources.
* B. Include a chart in your report without displaying the underlying data grid: Correct. You can create a chart object tied to a data source (e.g., a grid) and configure it to display only the chart, suppressing the grid for a cleaner visual presentation.
* C. Control the way a report object is printed on the page relative to other report objects: Correct. Report Designer allows you to adjust the layout and positioning of report objects (e.g., grids, charts) relative to each other, controlling how they appear when printed or exported.
* D. Insert a report object into the report if you are in Modify mode: Incorrect. While you modify reports in Report Designer, there's no specific "Modify mode" restriction-objects can be inserted during initial design or edits, but this is not a data source-specific feature.
The Oracle documentation verifies that B and C reflect Report Designer's flexibility with data sources and object management, making them the correct answers.
References:
* Oracle Planning 2024 Implementation Study Guide: "Designing Reports with Report Designer" (docs.
oracle.com, Published 2024-09-30).
* Oracle EPM Cloud Documentation: "Report Designer Data Sources" (docs.oracle.com, Published 2023-
12-05, updated for 2024).

質問 # 53
Which task must be completed before EPM administrators import a Machine Learning model into Planning?

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

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.
References:
* 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 # 54
Which four statements are true about the Optimize Dimension feature?

A. 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.
B. You can optimize the dimension order only for BSO cubes in Custom Planning applications.
C. The new optimized dimension order is maintained even if you enable additional features or other modules.
D. You refresh the database and then back up the application and download the snapshot before you optimize dimension order.
E. 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.

正解：B、C、D、E

解説：
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).

質問 # 55
......

当社Fast2testの1z0-1080-24認定ファイルは、代表的な傑作であり、品質、サービス、革新をリードしています。テスト1z0-1080-24認定に関する最も重要な情報を収集し、業界の上級専門家および認定講師および著者によって作成およびコンパイルされた新しい知識ポイントを補足します。クライアントが1z0-1080-24クイズ教材を効率的に学習し、1z0-1080-24試験に合格できるように、実際の試験を刺激する機能などの補助機能を提供します。

1z0-1080-24復習時間: https://jp.fast2test.com/1z0-1080-24-premium-file.html

Steve Allen Steve Allen

Biography

Propellers

Quick Links