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SAP C-BW4H-2505 認定試験の出題範囲：

トピック
出題範囲

トピック 1

• Data Acquisition into SAP BW
• 4HANA: This section tests how Data Engineers manage data integration into SAP BW
• 4HANA from multiple sources. It covers essential knowledge of tools and processes used for data extraction, transformation, and loading into the SAP environment.

トピック 2

• Fundamentals: This section of the exam measures the foundational understanding of SAP Consultants and covers essential terms and concepts related to SAP BW
• 4HANA and SAP Business Data Cloud. It focuses on the core framework and architecture necessary to navigate and work with these platforms.

トピック 3

• SAP BW
• 4HANA Modeling:This section targets the skills of Data Engineers in selecting appropriate modeling options and applying best practices like LSA++ within SAP BW
• 4HANA. It focuses on designing scalable, high-performing data models.

トピック 4

• SAP BW
• 4HANA Project and the Modeling Process:This section of the exam assesses how Data Engineers guide and contribute to SAP BW
• 4HANA projects. It includes knowledge of modeling workflows, project lifecycle stages, and collaboration strategies within project teams.

トピック 5

• SAP Analytics Tools and SAP Analytics Cloud: This section evaluates the skills of SAP Consultants in using tools like SAP Analytics Cloud, Lumira, and Analysis for Office to visualize and interpret data. It focuses on the consultant’s ability to apply business intelligence tools within the SAP ecosystem.

トピック 6

• SAP BW Query Design: This section of the exam assesses the ability of Data Engineers to create and run queries using SAP BW
• 4HANA. It evaluates how well candidates can work with query components to retrieve and structure data effectively for reporting and analysis.

トピック 7

• Data Acquisition into SAP HANA: This section evaluates the capacity of SAP Consultants to integrate various data sources into SAP HANA. It assesses their ability to understand different ingestion techniques and ensure data accessibility for processing.

 

SAP Certified Associate - Data Engineer - SAP BW/4HANA 認定 C-BW4H-2505 試験問題 (Q53-Q58):

質問 # 53
Which options do you have to combine data from SAP BW bridge a customer space in SAP Datasphere core?
Note: There are 2 correct answers to this question.

• A. *Import SAP BW bridge objects to the customer space.
  *Create additional views in the customer space to combine data.
• B. *Import SAP BW bridge objects to the SAP BW bridge space.
  *Create additional views in the customer space.
  *Share the created views with the SAP BW bridge space to combine data.
• C. *Import objects from the customer space to the SAP BW bridge space.
  *Create additional views in the SAP BW bridge space to combine data.
• D. *Import SAP BW bridge objects to the SAP BW bridge space.
  *Share the generated remote tables with the customer space.
  *Create additional views in the customer space to combine data.

正解：A、D

解説：
Combining data from SAP BW Bridge and the customer space in SAP Datasphere Core requires careful planning to ensure seamless integration and efficient data access. Let's analyze each option to determine why A and B are correct:
* Explanation:
* Step 1: Importing SAP BW Bridge objects into the SAP BW Bridge space ensures that the data remains organized and aligned with its source.
* Step 2: Sharing the generated remote tables with the customer space allows the customer space to access the data without duplicating it.
* Step 3: Creating additional views in the customer space enables users to combine the shared data with other datasets in the customer space.
* This approach leverages the concept of "remote tables" in SAP Datasphere, which provides a virtual link to the data in the SAP BW Bridge space. It avoids unnecessary data replication and ensures efficient data access.
2. Option B: Import SAP BW bridge objects to the customer space and create views to combine data Explanation:
Step 1: Importing SAP BW Bridge objects directly into the customer space simplifies the data model by consolidating all required data in one location.
Step 2: Creating additional views in the customer space allows users to combine the imported data with other datasets within the same space.
Reference: This approach is suitable when the customer space is the primary workspace for data modeling and analysis. It eliminates the need for cross-space sharing but may involve some data duplication.
3. Option C: Import SAP BW bridge objects to the SAP BW bridge space, create views in the customer space, and share views with the SAP BW bridge spaceExplanation: Sharing views created in the customer space back to the SAP BW Bridge space is not a standard practice. Views in SAP Datasphere are typically used within the space where they are created, and sharing them across spaces can lead to complexity and inefficiency.
Reference: SAP Datasphere emphasizes clear separation between spaces to maintain governance and performance. Cross-space sharing of views is not supported or recommended.
4. Option D: Import objects from the customer space to the SAP BW bridge space and create views to combine dataExplanation: Importing objects from the customer space into the SAP BW Bridge space reverses the typical data flow and introduces unnecessary complexity. The SAP BW Bridge space is designed to host data from SAP BW Bridge, while the customer space is intended for custom data modeling and integration.
Reference: SAP Datasphere follows a unidirectional flow where data from SAP BW Bridge is shared with the customer space, not the other way around.

 

質問 # 54
You created an Open ODS View on an SAP HANA database table to virtually consume the data in SAP BW
/4HANA. Real-time reporting requirements have now changed you are asked to persist the data in SAP BW
/4HANA.
Which objects are created when using the "Generate Data Flow" function in the Open ODS View editor?
Note: There are 3 correct answers to this question.

• A. CompositeProvider
• B. DataStore object (advanced)
• C. Data source
• D. Transformation
• E. SAP HANA calculation view

正解：B、C、D

解説：
* Open ODS View: An Open ODS View in SAP BW/4HANA allows virtual consumption of data from external sources (e.g., SAP HANA tables). It does not persist data but provides real-time access to the underlying source.
* Generate Data Flow Function: When using the "Generate Data Flow" function in the Open ODS View editor, SAP BW/4HANA creates objects to persist the data for reporting purposes. This involves transforming the virtual data into a persistent format within the BW system.
* Generated Objects:
* DataStore Object (Advanced): Used to persist the data extracted from the Open ODS View.
* Transformation: Defines how data is transformed and loaded into the DataStore Object (Advanced).
* Data Source: Represents the source of the data being persisted.
Key Concepts:Objects Created by "Generate Data Flow":When you use the "Generate Data Flow" function in the Open ODS View editor, the following objects are created:
* DataStore Object (Advanced): This is the primary object where the data is persisted. It serves as the storage layer for the data extracted from the Open ODS View.
* Transformation: A transformation is automatically generated to map the fields from the Open ODS View to the DataStore Object (Advanced). This ensures that the data is correctly structured and transformed during the loading process.
* Data Source: A data source is created to represent the Open ODS View as the source of the data. This allows the BW system to extract data from the virtual view and load it into the DataStore Object (Advanced).
* B. SAP HANA Calculation View: While Open ODS Views may be based on SAP HANA calculation views, the "Generate Data Flow" function does not create additional calculation views. It focuses on persisting data within the BW system.
* E. CompositeProvider: A CompositeProvider is used to combine data from multiple sources for reporting. It is not automatically created by the "Generate Data Flow" function.
References:SAP BW/4HANA Documentation on Open ODS Views: The official documentation explains the
"Generate Data Flow" function and its role in persisting data.
SAP Note on Open ODS Views: Notes such as 2608998 provide details on how Open ODS Views interact with persistent storage objects.
SAP BW/4HANA Best Practices for Data Modeling: These guidelines recommend using transformations and DataStore Objects (Advanced) for persisting data from virtual sources.
By using the "Generate Data Flow" function, you can seamlessly transition from virtual data consumption to persistent storage, ensuring compliance with real-time reporting requirements.

 

質問 # 55
What are the prerequisites for deleting business partner attribute master data in SAP BW/4HANA? Note:
There are 2 correct answers to this question.

• A. In SAP BW/4HANA there must be no analysis authorizations related to business partner values that should be deleted
• B. There must be no transaction data in a DataStore Object (advanced) referring to business partner values that should be deleted.
• C. In SAP BW/4HANA there must be no hierarchy data related to business partner values that should be deleted.
• D. There must be no BW query as InfoProvider in SAP BW/4HANA that uses business partner as a free characteristic.

正解：A、B

解説：
Deleting master data in SAP BW/4HANA requires careful consideration of dependencies to ensure data integrity and system stability. Below is a detailed explanation of the prerequisites for deleting business partner attribute master data:
* Explanation: While it is important to ensure that queries do not rely on specific master data values, this is not a strict prerequisite for deleting master data. Queries using business partner as a free characteristic will not prevent the deletion of master data, as long as there are no active dependencies such as transaction data or authorizations tied to those values.
* SAP BW/4HANA allows master data deletion even if queries reference the characteristic, provided there are no underlying dependencies like transaction data or authorizations.
Option B: In SAP BW/4HANA there must be no hierarchy data related to business partner values that should be deletedExplanation: While hierarchy data can be associated with master data, the presence of hierarchies does not directly prevent the deletion of master data. Hierarchies can be adjusted or removed independently of the master data deletion process. Therefore, this is not a prerequisite.
Reference: SAP documentation does not list hierarchy data as a blocking factor for master data deletion unless the hierarchy itself has active dependencies.
Option C: There must be no transaction data in a DataStore Object (advanced) referring to business partner values that should be deletedExplanation: Transaction data in a DataStore Object (advanced) creates a dependency on the master data. If transaction data references specific business partner values, those values cannot be deleted until the transaction data is either archived or removed. This ensures data consistency and prevents orphaned records.
Reference: SAP BW/4HANA enforces this rule to maintain referential integrity between master data and transactional data. Deleting master data without addressing transaction data would lead to inconsistencies.
Option D: In SAP BW/4HANA there must be no analysis authorizations related to business partner values that should be deletedExplanation: Analysis authorizations define access restrictions based on master data values. If analysis authorizations are configured to restrict access using specific business partner values, those values cannot be deleted until the authorizations are updated or removed. This ensures that security settings remain valid and consistent.
Reference: SAP BW/4HANA checks for dependencies in analysis authorizations before allowing master data deletion. Failing to address these dependencies can result in authorization errors.

 

質問 # 56
What are prerequisites for S-API Extractors to load data directly into SAP Datasphere core tenant using delta mode? Note: There are 2 correct answers to this question.

• A. Extractor must be based on a function module
• B. Operational Data Provisioning (ODP) must be enabled
• C. A primary key needs to exist.
• D. Real-time access needs to be enabled

正解：B、C

解説：
To load data directly into SAP Datasphere (formerly known as SAP Data Warehouse Cloud) core tenant using delta mode via S-API Extractors, certain prerequisites must be met. Let's evaluate each option:
* Option A: Real-time access needs to be enabled.Real-time access is not a prerequisite for delta mode loading. Delta mode focuses on incremental data extraction and loading, which does not necessarily require real-time capabilities. Real-time access is more relevant for scenarios where immediate data availability is critical.
* Option B: A primary key needs to exist.A primary key is essential for delta mode loading because it uniquely identifies records in the source system. Without a primary key, the system cannot determine which records have changed or been added since the last extraction, making delta processing impossible.
* Option C: Extractor must be based on a function module.While many S-API Extractors are based on function modules, this is not a strict requirement for delta mode loading. Extractors can also be based on other mechanisms, such as views or tables, as long as they support delta extraction.
* Option D: Operational Data Provisioning (ODP) must be enabled.ODP is a critical prerequisite for delta mode loading. It provides the infrastructure for managing and extracting data incrementally from SAP source systems. Without ODP, the system cannot track changes or deltas effectively, making delta mode loading infeasible.
References:SAP Datasphere Documentation: Outlines the prerequisites for integrating data from SAP source systems using delta mode.
SAP Help Portal: Provides detailed information on S-API Extractors and their requirements for delta processing.
SAP Best Practices for Data Integration: Highlights the importance of primary keys and ODP in enabling efficient delta extraction.
In conclusion, the two prerequisites for S-API Extractors to load data into SAP Datasphere core tenant using delta mode are the existence of aprimary keyand the enabling ofOperational Data Provisioning (ODP).

 

質問 # 57
You consider using the feature Snapshot Support for a Stard DataStore object. Which data management process may be slower with this feature than without it?

• A. Activating Data
• B. Delete request from the inbound table
• C. Selective Data Deletion
• D. Filling the Inbound Table

正解：C

解説：
The feature "Snapshot Support" in SAP BW/4HANA is designed to enable the retention of historical data snapshots within a Standard DataStore Object (DSO). When enabled, this feature allows the system to maintain multiple versions of records over time, which is useful for auditing, tracking changes, or performing historical analysis. However, this capability comes with trade-offs in terms of performance for certain data management processes.
Let's evaluate each option:
* Option A: Selective Data DeletionWith Snapshot Support enabled, selective data deletion becomes slower because the system must manage and track historical snapshots. Deleting specific records requires additional processing to ensure that the integrity of historical snapshots is maintained. This process involves checking dependencies between active and historical data, making it more resource- intensive compared to scenarios without Snapshot Support.
* Option B: Delete request from the inbound tableDeleting requests from the inbound table is generally unaffected by Snapshot Support. This operation focuses on removing raw data before it is activated or processed further. Since Snapshot Support primarily impacts activated data and historical snapshots, this process remains efficient regardless of whether the feature is enabled.
* Option C: Filling the Inbound TableFilling the inbound table involves loading raw data into the DSO.
This process is independent of Snapshot Support, as the feature only affects how data is managed after activation. Therefore, enabling Snapshot Support does not slow down the process of filling the inbound table.
* Option D: Activating DataWhile activating data may involve additional steps when Snapshot Support is enabled (e.g., creating historical snapshots), it is not typically as slow as selective data deletion.
Activation processes are optimized in SAP BW/4HANA, even with Snapshot Support, to handle the creation of new records and snapshots efficiently.
References:SAP BW/4HANA Administration Guide: Discusses the impact of Snapshot Support on data management processes, including selective data deletion.
SAP Help Portal: Provides insights into how Snapshot Support works and its implications for performance.
SAP Best Practices Documentation: Highlights scenarios where Snapshot Support is beneficial and outlines potential performance considerations.
In conclusion,Selective Data Deletionis the process most significantly impacted by enabling Snapshot Support in a Standard DataStore Object. This is due to the additional complexity of managing historical snapshots while ensuring data consistency during deletions.

 

質問 # 58
......

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