C_BW4H_2404日本語試験正確な問題集、学習ノートと理論 [2025年06月]
100%高得点合格保証C_BW4H_2404日本語無制限82解答
質問 # 39
分析権限はどこで割り当てることができますか? 注: この質問には 2 つの正解があります。
- A. トランザクションPFCGで、権限オブジェクトS_RS_AUTHを使用してロールに
- B. トランザクションPFCGで、権限オブジェクトS_RS_AOを使用してロールに
- C. トランザクション RSECADMIN でユーザーに直接
- D. トランザクションSU01でユーザーに直接
正解：A、C
解説：
A. RSECADMIN:
* This transaction allows direct assignment of analysis authorizations to individual users for controlling access to data in SAP BW/4HANA.
D. PFCG with S_RS_AUTH:
* Analysis authorizations can be assigned to roles via transaction PFCG using the authorization object S_RS_AUTH to integrate security roles effectively.
References:SAP BW/4HANA Analysis Authorization Management (SAP Help Portal).
質問 # 40
Stard DataStore オブジェクトのスナップショット サポート機能の使用を検討しています。この機能を使用すると、使用しない場合に比べてどのデータ管理プロセスが遅くなる可能性がありますか?
- A. 受信テーブルへの入力
- B. 受信テーブルからリクエストを削除します
- C. 選択的なデータ削除
- D. データの有効化
正解：D
解説：
* Snapshot Support in a DataStore object (advanced) retains historical data snapshots. While this feature enables detailed historical analysis, the data activation process is slower because it requires additional management of snapshots during activation.
References:SAP BW/4HANA DataStore Object Advanced Guide (SAP Help Portal).
質問 # 41
上位レベルの CompositeProvider は、結合演算に基づいて現在の値と履歴値を比較します。
現在の値は、毎日更新される DataStore オブジェクト (高度) によって提供されます。履歴値は、DataSources からのさまざまなオープン ODS ビューを組み合わせた下位レベルの CompositeProvider によって提供されます。
上位レベルの CompositeProvider を使用する BW クエリのパフォーマンスを向上させるにはどうすればよいでしょうか?
注: この質問には 2 つの正解があります。
- A. 上位レベルの CompositeProvider で、Union ノードの代わりに join ノードを使用します。
- B. 現在のデータの DataStore オブジェクト (詳細) を、ソース システムから現在のデータに直接アクセスする Open ODS ビューに置き換えます。
- C. 下位レベルの CompositeProvider を新しい DataStore オブジェクトに置き換え (高度)、同じ履歴データの組み合わせでそれを埋めます。
- D. Open ODS ビューの「データフロー生成」機能を使用して、履歴データを新しく生成された DataStore オブジェクトに読み込みます (高度)。
正解：C、D
解説：
Improving the performance of BW queries that use a CompositeProvider involves optimizing the underlying data sources and their integration. Let's analyze each option to determine why A and D are correct:
* Explanation: CompositeProviders are powerful tools for combining data from multiple sources, but they can introduce performance overhead due to the complexity of union operations. Replacing the lower- level CompositeProvider with a DataStore object (advanced) simplifies the data model and improves query performance. The DataStore object can be preloaded with the combined historic data, eliminating the need for real-time union operations during query execution.
質問 # 42
SAP HANA HDI 計算ビューを作成します。
データ カテゴリ ディメンションではなく、データ カテゴリ スター結合付きキューブを選択する理由は何ですか? 注: この質問には 3 つの正解があります。
- A. メジャーを合計として集計できます。
- B. デフォルトの時間特性を指定できます。
- C. マスターデータとトランザクションデータを組み合わせることができます。
- D. トランザクション データを永続化できます。
- E. 制限された列を作成できます。
正解：A、C、D
解説：
* Cube with Star Join Data Category:
* Used for combining and analyzing transactional and master data with enhanced reporting capabilities.
* Key Advantages:
* Combine Data (Answer A):Combines master and transactional data in a star schema.
* Persist Data (Answer B):Supports the persistence of transactional data.
* Aggregate Measures (Answer E):Enables aggregations like summing measures for analytical reporting.
* Default Time Characteristics (Option C):Not specifically tied to the Cube with Star Join.
* Restricted Columns (Option D):Restricted columns can be created in other data categories as well.
Incorrect Options:References:
* SAP HANA HDI Calculation View Documentation
質問 # 43
SAP S/4HANA 組み込み分析を使用するにはどのような基盤が必要ですか?
- A. ABAP CDS ビューベースの仮想データモデル
- B. SAP HANA に最適化されたビジネスコンテンツ
- C. 生成された外部SAP HANA計算ビュー
- D. SAP アジャイルデータ準備
正解：A
解説：
SAP S/4HANA Embedded Analytics relies on theABAP CDS (Core Data Services)view-based Virtual Data Model (VDM). This foundation provides a unified layer for data consumption directly from transactional data in the S/4HANA system.
* ABAP CDS Views as Foundation:
* CDS views define the semantic model for data and integrate seamlessly with SAP S/4HANA.
* These views allow users to build advanced reporting and analytics without requiring external data movement.
* Virtual Data Model (VDM):
* VDM provides a structured framework of CDS views optimized for analytics and reporting.
* It includes analytical, transactional, and consumption views tailored for SAP Analytics tools.
References:
* SAP Help Portal - S/4HANA Embedded Analytics Overview
* SAP Learning Hub - ABAP CDS View Basics
質問 # 44
セルを含む BW クエリでは、セルの初期定義を上書きする必要があります。どのセル タイプを使用できますか?
注: この質問には 2 つの正解があります。
- A. ヘルプセル
- B. 数式セル
- C. 参照セル
- D. 選択セル
正解：B、D
解説：
* Formula Cell (Answer B):
* Used to define custom calculations or formulas for specific cells in a BW query.
* Allows overwriting initial definitions with more dynamic calculations.
* Selection Cell (Answer C):
* Represents a defined selection within the BW query, allowing specific data filtering.
* Can overwrite default definitions to refine the cell's content.
* Reference Cell (Option A):Reference cells are not designed for overwriting initial definitions.
* Help Cell (Option D):There is no concept of help cells in BW queries.
Incorrect Options:References:
* SAP BW Query Design - Advanced Cell Definitions
* SAP Learning Hub - BW Query with Cells
質問 # 45
SAP S/4HANA ABAP CDSビューをSAP BWへの抽出に使用するための前提条件は何ですか？
/4HANA を ODP コンテキストで使用することはできますか? 注: この質問には 2 つの正解があります。
- A. ABAP CDS ビューは、適切なデータ抽出注釈を使用して定義する必要があります。
- B. コンテキスト ODP_CDS を持つ ODP ソース システムを SAP BW/4HANA に作成する必要があります。
- C. BW 抽出のために、ABAP CDS ビューをプログラム RODPS_OS_EXPOSE を通じてリリースする必要があります。
- D. オペレーショナル データ プロビジョニング フレームワークは、SAP BW/4HANA で設定する必要があります。
正解：B、C
解説：
Extracting data from SAP S/4HANA ABAP CDS (Core Data Services) views into SAP BW/4HANA using the Operational Data Provisioning (ODP) framework requires specific prerequisites. These ensure that the CDS views are properly exposed and accessible for extraction. Below is a detailed explanation of why the verified answers are correct.
* ABAP CDS Views:ABAP CDS views are reusable data models defined in SAP S/4HANA. They provide a semantic layer for querying data and can be used for reporting and analytics.
* Operational Data Provisioning (ODP):ODP is a framework in SAP BW/4HANA that enables real-time or near-real-time data extraction from various source systems, including SAP S/4HANA.
* ODP Contexts:ODP contexts define the type of source system and data extraction method. For CDS views, the contextODP_CDSis used.
* Data Extraction Annotations:Annotations in CDS views specify metadata for extraction purposes, such as field properties and extraction behavior.
Key Concepts:
* Option A: The ABAP CDS views must be released through the program RODPS_OS_EXPOSE for BW extraction.
* Why Correct?To make an ABAP CDS view available for extraction via ODP, it must be explicitly released using the programRODPS_OS_EXPOSE. This step registers the view in the ODP framework and makes it accessible to SAP BW/4HANA.
* Option B: The Operational Data Provisioning Framework must be configured in SAP BW/4HANA.
* Why Incorrect?While configuring the ODP framework is a general prerequisite for any ODP- basedextraction, it is not specific to extracting ABAP CDS views. This option is too broad to be considered a direct prerequisite.
* Option C: An ODP source system with context ODP_CDS must be created in SAP BW/4HANA.
* Why Correct?To extract data from ABAP CDS views, you must create an ODP source system in SAP BW/4HANA with the contextODP_CDS. This context specifies that the source system provides data from CDS views.
* Option D: The ABAP CDS views must be defined with the appropriate data extraction annotations.
* Why Incorrect?While annotations are important for defining metadata in CDS views, they are not mandatory for ODP-based extraction. The primary requirement is releasing the view using RODPS_OS_EXPOSE.
Verified Answer Explanation:
* SAP BW/4HANA Extraction Guide:The guide outlines the steps for extracting data from ABAP CDS views using the ODP framework, including the use ofRODPS_OS_EXPOSEand the creation of an ODP source system.
* SAP Note 2700850:This note provides detailed instructions on releasing CDS views for BW extraction and configuring the ODP framework.
* SAP Best Practices for ODP Extraction:SAP recommends using theODP_CDScontext for extracting data from ABAP CDS views and emphasizes the importance of releasing views using RODPS_OS_EXPOSE.
SAP Documentation and References:
質問 # 46
SAP ERP で汎用データソースを作成するために使用できるソース タイプはどれですか? 注: この質問には 3 つの正解があります。
- A. ABAP クラスメソッド
- B. SAPクエリ
- C. ABAP ファンクションモジュール
- D. ABAP管理データベースプロシージャ
- E. データベースビュー
正解：A、B、C
解説：
InSAP ERP, aGeneric DataSourceis used to extract data from various source types and make it available for consumption in SAP BW/4HANA or other systems. The source type defines the origin of the data and how it is extracted. Below is an explanation of the correct answers and why they are valid.
* A. ABAP class method
* AnABAP class methodcan be used as a source type for a Generic DataSource. This approach allows developers to encapsulate complex logic within an ABAP class and expose the data extraction logic through a specific method.
* The method is called during the data extraction process, and its output is used as the data source.
This is particularly useful for scenarios where custom logic or calculations are required to prepare the data.
質問 # 47
BW クエリのキー日付によって影響を受ける可能性のあるオブジェクトの値はどれですか? 注: この質問には 3 つの正解があります。
- A. 基本的なキー数値
- B. 時間特性
- C. 階層
- D. 属性を表示する
- E. ナビゲーション属性
正解：C、D、E
解説：
A. Display Attributes:
* Key date determines which version of a display attribute is visible in reports.
D. Hierarchies:
* Time-dependent hierarchies reflect structures based on the key date in a query.
E. Navigation Attributes:
* Time-dependent navigation attributes adapt dynamically to the key date.
References:SAP BW Query Key Date Features (SAP Help Portal).
質問 # 48
SAP HANA HDI 計算ビューを作成します。
データ カテゴリ ディメンションではなく、データ カテゴリ スター結合付きキューブを選択する理由は何ですか? 注: この質問には 3 つの正解があります。
- A. メジャーを合計として集計できます。
- B. デフォルトの時間特性を指定できます。
- C. マスターデータとトランザクションデータを組み合わせることができます。
- D. 制限された列を作成できます。
- E. トランザクション データを永続化できます。
正解：A、B、C
解説：
When creating an SAP HANA HDI Calculation View, choosing thedata category Cube with Star JoinoverDimensiondepends on the specific requirements of your data model. Below is a detailed explanation of why the verified answers are correct.
* Data Category Dimension:
* Used for modeling master data or reference data.
* Does not support measures or aggregations.
* Typically used for descriptive attributes (e.g., customer names, product descriptions).
* Data Category Cube with Star Join:
* Used for modeling transactional data with measures and dimensions.
* Supports star schema designs, combining fact tables (measures) and dimension tables (attributes).
* Enables advanced features like aggregations, time characteristics, and joins between master and transactional data.
* Star Join:
* A star join connects a fact table (containing measures) with dimension tables (containing attributes) in a star schema.
* It is optimized for performance and scalability in analytical queries.
Key Concepts:
* Option A: You can combine master data transactional data.
* Why Correct?The Cube with Star Join data category is specifically designed to combine transactional data (fact tables) with master data (dimension tables). This enables comprehensive reporting and analysis.
* Option B: You can persist transactional data.
* Why Incorrect?Persisting transactional data is not a feature of the Cube with Star Join data category. Persistence is typically handled at the database or application layer.
* Option C: You can provide default time characteristics.
* Why Correct?The Cube with Star Join data category supports default time characteristics (e.g., fiscal year, calendar year), which are essential for time-based reporting and analysis.
* Option D: You can create restricted columns.
* Why Incorrect?Restricted columns are a feature of calculation views but are not specific to the Cube with Star Join data category. They can also be created in Dimension views.
* Option E: You can aggregate measures as a sum.
* Why Correct?The Cube with Star Join data category supports aggregations, such as summing measures. This is a key feature for analyzing transactional data.
Verified Answer Explanation:
* SAP HANA Modeling Guide:The guide explains the differences between data categories like Dimension and Cube with Star Join, highlighting their respective use cases.
* SAP Note 2700850:This note provides examples of scenarios where Cube with Star Join is preferred over Dimension, emphasizing its ability to handle transactional data and aggregations.
* SAP Best Practices for HANA Modeling:SAP recommends using Cube with Star Join for analytical models that require combining master and transactional data, providing default time characteristics, and performing aggregations.
質問 # 49
売上収益に基づいて 100 人の顧客のうち上位 10 人を検索する条件を BW クエリで定義しました。
BW クエリでキー数値プロパティを使用すると、結果の表示に関するどの 2 つのシナリオを実現できますか? 注: この質問には 2 つの正解があります。
- A. 上位10社の顧客の売上合計を含む1行の結果
- B. 上位 10 社の顧客の売上合計を含む 1 行目と、その他の 90 社の顧客の売上合計を含む 2 行目
- C. 100 人の顧客全員の売上合計を含む 1 つの結果行
- D. 上位 10 社の顧客の売上合計を含む 1 行目と、全 100 社の顧客の売上合計を含む 2 行目
正解：A、B
解説：
* Key Figure Properties in Query:
* Key figure properties enable flexible aggregation and presentation of data in BW queries.
* They allow splitting or consolidating result rows based on specific conditions or properties.
* Scenario Explanation:
* Answer C:A single result row showing the aggregated sales revenue for the top 10 customers.
* Answer D:Two separate result rows: one for the top 10 customers' revenue and another for the remaining 90 customers.
References:
* SAP BW Query Design Documentation
* SAP Learning Hub - Conditional Reporting Techniques in BW Queries
質問 # 50
DataMart DataStore オブジェクトでは、どのリクエストベースの削除が可能ですか?
- A. 受信テーブル内の最新の非アクティブ化リクエストのみ
- B. アクティブデータテーブル内の最新のリクエストのみ
- C. 受信テーブル内のアクティブ化されていないリクエスト
- D. アクティブデータテーブル内の任意のリクエスト
正解：B
解説：
In SAP BW/4HANA, aDataMart DataStore Object (DSO)is used to store detailed data for reporting and analysis. Request-based deletion allows you to remove specific data requests from the DSO. However, there are restrictions on which requests can be deleted, depending on whether they are in the inbound table or the active data table. Below is an explanation of the correct answer:
A: Only the most recent request in the active data tableIn a DataMart DSO, request-based deletion is possible only for themost recent requestin theactive data table. Once a request is activated, it moves from the inbound table to the active data table. To maintain data consistency, SAP BW/4HANA enforces the rule that only the most recent request in the active data table can be deleted. Deleting older requests would disrupt the integrity of the data.
* Steps to Delete a Request:
* Navigate to the DataStore Object in the SAP BW/4HANA environment.
* Identify the most recent request in the active data table.
* Use the request deletion functionality to remove the request.
質問 # 51
開始プロセスがプロセス チェーン内の特別なタイプのプロセスである理由は何ですか? 注: この質問には 2 つの正解があります。
- A. 別のプロセスの後継になることができます。
- B. 各プロセス チェーンには 1 つの開始プロセスのみが許可されます。
- C. 先行プロセスなしでスケジュールできる唯一のプロセスです。
- D. メタチェーンに埋め込むことができます。
正解：B、C
解説：
Thestart processin an SAP BW/4HANA process chain is a unique and essential component. It serves as the entry point for executing the chain and has specific characteristics that distinguish it from other processes.
Below is a detailed explanation of why the verified answers are correct.
* Process Chain Overview:A process chain in SAP BW/4HANA is a sequence of processes (e.g., data loads, transformations, reporting) that are executed in a predefined order. The start process initiates the execution of the chain.
* Start Process Characteristics:
* The start process is mandatory for every process chain.
* It determines when and how the process chain begins execution.
* It does not require a predecessor process to trigger its execution.
* Meta Chains:A meta chain is a higher-level process chain that controls the execution of multiple sub- process chains. While the start process can be part of a meta chain, this is not its defining characteristic.
Key Concepts:
* Option A: Only one start process is allowed for each process chain.
* Why Correct?Every process chain must have exactly one start process. This ensures that there is a single, unambiguous entry point for the chain. Multiple start processes would create ambiguity about where the chain begins.
* Option B: It can be embedded in a Meta chain.
* Why Incorrect?While the start process can technically be part of a meta chain, this is not a unique feature of the start process. Other processes in a chain can also be embedded in a meta chain, so this is not a distinguishing reason.
* Option C: It can be a successor to another process.
* Why Incorrect?The start process cannot have a predecessor because it is the first process in the chain. By definition, it initiates the chain and cannot depend on another process to trigger it.
* Option D: It is the only process that can be scheduled without a predecessor.
* Why Correct?The start process is unique in that it can be scheduled independently without requiring a predecessor. This allows the process chain to begin execution based on a schedule or manual trigger.
Verified Answer Explanation:
* SAP BW/4HANA Process Chain Guide:The guide explains the role of the start process in initiating a process chain and emphasizes that only one start process is allowed per chain.
* SAP Note 2700850:This note highlights the scheduling capabilities of the start process and clarifies that it does not require a predecessor.
* SAP Best Practices for Process Chains:SAP recommends using the start process as the sole entry point for process chains to ensure clarity and consistency in execution.
SAP Documentation and References:
質問 # 52
インフォオブジェクト「CITY」は、インフォオブジェクト「CUSTOMER」の表示属性として定義されています。 インフォオブジェクト「COUNTRY」は、インフォオブジェクト「CITY」の表示属性として定義されています。 マスターデータレポートでは、
「顧客」の「国」。
このシナリオを実現するにはどのようなオプションがありますか? 注: この質問には 3 つの正解があります。
- A. InfoObject 定義の「CUSTOMER」の推移属性として「COUNTRY」を追加します。
- B. 「CUSTOMER」「CITY」「COUNTRY」の外部ビューを生成し、別の計算ビューに結合します。
- C. 一連の関連付けを使用して、Open ODS ビューで「CUSTOMER」「CITY」「COUNTRY」を結合します。
- D. 左外部結合演算子のシーケンスを使用して、複合プロバイダーで「CUSTOMER」「CITY」「COUNTRY」を結合します。
- E. 「CUSTOMER」の BW クエリの行に「CUSTOMER」を含めると、ユニバーサル表示階層設定がアクティブになります。
正解：B、C、D
解説：
B. Generate external views:
* External views allow joining master data and transactional data in SAP HANA Calculation Views for flexible reporting.
C. Composite Provider with left outer joins:
* Composite Providers allow combining data using joins, providing a flexible virtual data mart structure.
E. Open ODS View with associations:
* Open ODS Views enable flexible modeling using associations between different master data entities.
References:SAP BW/4HANA modeling and reporting documentation (SAP Help Portal).
質問 # 53
Facts タイプの Open ODS ビューを作成しました。
特性フォルダー内のフィールドをどのオブジェクト タイプに関連付けることができますか? 注: この質問には 2 つの正解があります。
- A. Facts タイプの ODS ビューを開く
- B. HDI 計算データ カテゴリ ディメンションのビュー
- C. 特性タイプのインフォオブジェクト
- D. マスターデータタイプのODSビューを開く
正解：C、D
解説：
A. Open ODS view of type Master Data:
* Fields in the Characteristics folder of an Open ODS view can be associated with an Open ODS view representing master data.
B. InfoObject of type Characteristic:
* Characteristics in BW can be directly linked to fields in the Open ODS view, enabling standardized master data modeling.
References:SAP BW/4HANA Open ODS View documentation (SAP Help Portal).
質問 # 54
SAP BW ブリッジではどのソース システムがサポートされていますか? 注: この質問には 3 つの正解があります。
- A. SAP S/4HANA オンプレミス
- B. SAP アリバ
- C. SAP 成功要因
- D. SAP S/4HANAクラウド
- E. SAP ECC
正解：A、D、E
解説：
SAP BW bridge is designed to integrate data from various source systems into SAP BW/4HANA or SAP Datasphere. Let's analyze each option:
* Option A: SAP AribaSAP Ariba is a cloud-based procurement solution and is not directly supported as a source system in SAP BW bridge. While SAP Ariba data can be integrated into SAP systems, it typically requires intermediate tools like SAP Integration Suite or APIs for data extraction.
* Option B: SAP ECCSAP ECC (ERP Central Component) is fully supported as a source system in SAP BW bridge. SAP BW bridge provides connectors and extractors to extract data from SAP ECC systems, enabling seamless integration into SAP BW/4HANA or SAP Datasphere.
* Option C: SAP SuccessFactorsSAP SuccessFactors is a cloud-based human capital management (HCM) solution. It is not natively supported as a source system in SAP BW bridge. Similar to SAP Ariba, integrating data from SAP SuccessFactors typically involves using APIs or middleware solutions.
* Option D: SAP S/4HANA on-premiseSAP S/4HANA on-premise is fully supported as a source system in SAP BW bridge. The bridge provides robust connectivity and extraction capabilities to integrate data from on-premise S/4HANA systems into SAP BW/4HANA or SAP Datasphere.
* Option E: SAP S/4HANA CloudSAP S/4HANA Cloud is also supported as a source system in SAP BW bridge. The bridge leverages APIs and OData services to extract data from S/4HANA Cloud, ensuring compatibility with cloud-based deployments.
* SAP BW Bridge Documentation: Lists the supported source systems and their integration capabilities.
* SAP Help Portal: Provides detailed information on connecting SAP BW bridge to various source systems.
* SAP Integration Guides: Highlight best practices for integrating data from SAP ECC and S/4HANA systems.
References:In summary, the supported source systems in SAP BW bridge areSAP ECC,SAP S/4HANA on- premise, andSAP S/4HANA Cloud.
質問 # 55
SAP Datasphere のデータフロー機能を使用する場合の有効なオプションは何ですか? 注: この質問には 3 つの正解があります。
- A. データは、Union または Join 演算子を使用して結合できます。
- B. リモート テーブルをターゲット オブジェクトとして使用できます。
- C. 複雑な変換には Python 言語を使用できます。
- D. ターゲット モードは、追加、切り捨て、または削除です。
- E. NumPy Pas は自動的に SQL スクリプトに変換されます。
正解：A、C、D
解説：
B. Python for complex transformation:
* SAP Datasphere supports the use of Python for advanced and custom transformations during data processing.
C. Data combination with Union/Join operators:
* Data Flow allows combining multiple data sources through Union or Join operators for seamless integration.
E. Target modes (Append/Truncate/Delete):
* These target modes provide flexibility in managing data in the target table during the data flow execution.
References:SAP Datasphere Data Flow documentation (SAP Help Portal)
質問 # 56
DataMart DataStore オブジェクトでは、どのリクエストベースの削除が可能ですか?
- A. 受信テーブル内の最新の非アクティブ化リクエストのみ
- B. アクティブデータテーブル内の最新のリクエストのみ
- C. 受信テーブル内のアクティブ化されていないリクエスト
- D. アクティブデータテーブル内の任意のリクエスト
正解：A
解説：
* Deletion is restricted to the most recent non-activated request in the inbound table to maintain data integrity and avoid accidental deletion of active data.
References:SAP BW/4HANA DataStore Object Advanced documentation (SAP Help Portal - DataStore Objects).
質問 # 57
BW クエリでは、特性 0CALMONTH の入力可能な BW 変数のデフォルト値として、現在の四半期の最初の月を設定する必要があります。
どの処理タイプを使用しますか?
- A. 置換パス
- B. オフセット値による手動入力
- C. 顧客終了
- D. デフォルト値による手動入力
正解：C
解説：
The processing type "Customer Exit" allows dynamic determination of default values for input-ready BW variables based on custom logic.
In this case, you can use a customer exit to calculate the first month of the current quarter dynamically by implementing ABAP logic in the enhancement spotRSROA_VARIABLES_EXIT.
This ensures that the variable adapts automatically to the current quarter during runtime.
References:SAP BW/4HANA documentation on Customer Exit Variables (SAP Help Portal - Customer Exits).
質問 # 58
主要数値マトリックスからアーキテクチャ概要モデルを導出する必要があります。最初に実行する必要がある手順は何ですか?
- A. ストレージ要件を分析します。
- B. 変換を識別します。
- C. ソースを特定します。
- D. データ マートを定義します。
正解：C
解説：
Deriving anarchitecture overview modelfrom a key figure matrix is a critical step in designing an SAP BW
/4HANA solution. The first step in this process is toidentify the sourcesof the data that will populate the key figures. Understanding the data sources ensures that the architecture is built on a solid foundation and can meet the reporting and analytical requirements.
* Identify sources (Option B):Before designing the architecture, it is essential to determine where the data for the key figures originates. This includes identifying:
* Source systems:ERP systems, external databases, flat files, etc.
* Data types:Transactional data, master data, metadata, etc.
* Data quality:Ensuring the sources provide accurate and consistent data.
* Identifying sources helps define the data extraction, transformation, and loading (ETL) processes required to populate the key figures in the architecture.
* Identify transformations (Option A):Transformations are applied to the data after it has been extracted from the sources. While transformations are an important part of the architecture, they cannot be defined until the sources are identified.
* Analyze storage requirements (Option C):Storage requirements depend on the volume and type of data being processed. However, these requirements can only be determined after the sources and data flows are understood.
* Define data marts (Option D):Data marts are designed to serve specific reporting or analytical purposes.
Defining data marts is a later step in the architecture design process and requires a clear understanding of the sources and transformations.
* Identify sources:Determine the origin of the data.
* Map data flows:Define how data moves from the sources to the target system.
* Apply transformations:Specify the logic for cleansing, enriching, and aggregating the data.
* Design storage layers:Decide how the data will be stored (e.g., ADSOs, InfoCubes).
* Define data marts:Create specialized structures for reporting and analytics.
* Source Identification:Identifying sources is the foundation of any data architecture. Without knowing where the data comes from, it is impossible to design an effective ETL process or storage model.
* Key Figure Matrix:A key figure matrix provides a high-level view of the metrics and dimensions required for reporting. It serves as a starting point for designing the architecture.
* SAP BW/4HANA Modeling Guide:This guide explains the steps involved in designing an architecture, including source identification and data flow mapping.
* Link:SAP BW/4HANA Documentation
* SAP Note 2700980 - Best Practices for Architecture Design in SAP BW/4HANA:This note provides recommendations for designing scalable and efficient architectures in SAP BW/4HANA.
Why Other Options Are Incorrect:Steps to Derive an Architecture Overview Model:Key Points About Architecture Design:References to SAP Data Engineer - Data Fabric:By starting withsource identification, you ensure that the architecture overview model is grounded in the actual data landscape, enabling a robust and effective solution design.
質問 # 59
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