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[General] Pass Guaranteed 2026 SAP Valid C_BW4H_2505: SAP Certified Associate - Data Engin

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【General】 Pass Guaranteed 2026 SAP Valid C_BW4H_2505: SAP Certified Associate - Data Engin

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SAP C_BW4H_2505 Exam Syllabus Topics:
TopicDetails
Topic 1
  • Native SAP HANA Modeling:This section evaluates the ability of SAP Consultants to describe and apply native modeling options in SAP HANA. It emphasizes understanding how to build optimized data structures directly within the HANA platform.
Topic 2
  • 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.
Topic 3
  • 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.
Topic 4
  • 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.
Topic 5
  • 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.
Topic 6
  • 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.
Topic 7
  • 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.

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SAP Certified Associate - Data Engineer - SAP BW/4HANA Sample Questions (Q64-Q69):NEW QUESTION # 64
Which join types can you use in a Composite Provider? Note: There are 3 correct answers to this question.
  • A. Full Outer join
  • B. Inner join
  • C. Temporal hierarchy join
  • D. Text join
  • E. Referential join
Answer: B,D,E
Explanation:
In SAP Data Engineer - Data Fabric, specifically within the context of Composite Providers in SAP BW
/4HANA, there are specific types of joins that can be utilized to combine data from different sources effectively. Let's break down each join type mentioned in the question:
* Text Join (A):A text join is used when you need to include descriptive texts (like descriptions for codes) in your query results. This join type connects a primary table with a text table based on language- specific attributes. It ensures that textual information is appropriately linked and displayed alongside the main data. This is particularly useful in scenarios where reports or queries require human-readable descriptions.
* Temporal Hierarchy Join (B):Temporal hierarchy joins are not supported in Composite Providers.
These types of joins are typically used in other contexts within SAP systems, such as when dealing with time-dependent hierarchies in Advanced DataStore Objects (ADSOs) or other temporal data models.
However, they do not apply to Composite Providers.
* Full Outer Join (C):Full outer joins are not available in Composite Providers. Composite Providers primarily support inner joins, referential joins, and text joins. The full outer join, which includes all records when there is a match in either left or right table, is not part of the join options within this specific context.
* Referential Join (D):Referential joins are optimized joins that assume referential integrity between the tables involved. This means that the system expects all relevant entries in one table to have corresponding entries in the other. If this condition is met, referential joins can significantly improve query performance by reducing the amount of data processed. They are commonly used in Composite Providers to efficiently combine data while maintaining performance.
* Inner Join (E):Inner joins are fundamental join types used in Composite Providers. They return only the records that have matching values in both tables being joined. This is one of the most frequently used join types due to its straightforward nature and effectiveness in combining related datasets.
References:SAP BW/4HANA Documentation: The official documentation outlines the capabilities and limitations of Composite Providers, including the types of joins supported.
SAP Help Portal: Provides detailed explanations and examples of how different join types function within SAP BW/4HANA environments.
SAP Community Blogs & Forums: Discussions and expert insights often highlight practical use cases and best practices for implementing various join types in Composite Providers.
By understanding these join types and their applications, data engineers can design efficient and effective data models within the SAP Data Engineer - Data Fabric framework, ensuring optimal performance and accurate data representation.

NEW QUESTION # 65
How does SAP position SAP Datasphere in supporting business users?Note: There are 3 correctanswers to this question.
  • A. Business users can create restricted and calculated columns based on existing models.
  • B. Business users can upload their own CSV files.
  • C. Business users can allocate system resources without IT involvement.
  • D. Business users can create agile models from different sources.
  • E. Business users can leverage embedded analytic Fiori apps for data analysis.
Answer: A,B,D

NEW QUESTION # 66
For InfoObject "ADDRESS" the High Cardinality flag has been set. However "ADDRESS" has an attribute
"CITY" without the High Cardinality flag. What is the effect on SID values in this scenario?
  • A. SID values are generated when InfoObject "ADDRESS" is activated.
  • B. SID values are not stored for InfoObject "ADDRESS".
  • C. SID values are generated when data for InfoObject "ADDRESS" is loaded.
  • D. SID values are generated when InfoObject "CITY" is activated.
Answer: C
Explanation:
In SAP BW (Business Warehouse), the concept ofHigh Cardinalityplays a crucial role in determining how data is stored and managed for InfoObjects. Let's break down the scenario described in the question and analyze the effects on SID (Surrogate ID) values:
* InfoObject: An InfoObject is a basic building block in SAP BW, representing a business entity like
"ADDRESS" or "CITY".
* High Cardinality Flag: When this flag is set for an InfoObject, it indicates that the InfoObject has a very large number of distinct values (high cardinality). This affects how SIDs are generated and managed.
* SID (Surrogate ID): A unique identifier assigned to each distinct value of an InfoObject. SIDs are used to optimize query performance and reduce storage requirements.
* InfoObject "ADDRESS": The High Cardinality flag is set for this InfoObject. This means that the system expects a large number of distinct values for "ADDRESS". As a result, SID generation for
"ADDRESS" is deferred until actual data is loaded into the system. This approach avoids unnecessary overhead during activation and ensures efficient storage.
* Attribute "CITY": This attribute does not have the High Cardinality flag set. Therefore, SIDs for
"CITY" will be generated when the InfoObject is activated, as is typical for standard InfoObjects without high cardinality.
* ForInfoObject "ADDRESS", since the High Cardinality flag is set,SID values are NOT generated during activation. Instead, they are generated dynamicallywhen data for "ADDRESS" is loadedinto the system. This behavior aligns with the design principle of high cardinality objects to defer SID generation until runtime.
* Forattribute "CITY", SID values are generated during activation because it does not have the High Cardinality flag set.
Key Concepts:Scenario Analysis:Effects on SID Values:Why Option D is Correct:The correct answer isD.
SID values are generated when data for InfoObject "ADDRESS" is loaded. This is consistent with the behavior of high cardinality InfoObjects in SAP BW. SID generation is deferred until data loading to optimize performance and storage.
References:SAP BW Documentation on High Cardinality: SAP BW systems use the High Cardinality flag to manage large datasets efficiently. For high cardinality objects, SIDs are generated at runtime during data loading rather than during activation.
SAP Note on SID Generation: SAP notes related to SID generation (e.g., Note 2008578) explain the behavior of high cardinality objects and their impact on SID management.
SAP Data Fabric Best Practices: In scenarios involving high cardinality, deferring SID generation until data loading is recommended to ensure optimal performance and resource utilization.
By understanding the implications of the High Cardinality flag and its interaction with attributes, we can confidently conclude that SID values for "ADDRESS" are generated only when data is loaded.

NEW QUESTION # 67
Which layer of the layered scalable architecture (LSA++) of SAP BW/4HANA is designed as the main storage for harmonized consistent data?
  • A. Flexible Enterprise Data Warehouse Core layer
  • B. Open Operational Data Store layer
  • C. Data Acquisition layer
  • D. Virtual Data Mart layer
Answer: A
Explanation:
TheLayered Scalable Architecture (LSA++)of SAP BW/4HANA is a modern data warehousing architecture designed to simplify and optimize the data modeling process. It provides a structured approach to organizing data layers, ensuring scalability, flexibility, and consistency in data management. Each layer in the LSA++ architecture serves a specific purpose, and understanding these layers is critical for designing an efficient SAP BW/4HANA system.
* LSA++ Overview:The LSA++ architecture replaces the traditional Layered Scalable Architecture (LSA) with a more streamlined and flexible design. It reduces complexity by eliminating unnecessary layers and focusing on core functionalities. The main layers in LSA++ include:
* Data Acquisition Layer: Handles raw data extraction and staging.
* Open Operational Data Store (ODS) Layer: Provides operational reporting and real-time analytics.
* Flexible Enterprise Data Warehouse (EDW) Core Layer: Acts as the central storage for harmonized and consistent data.
* Virtual Data Mart Layer: Enables virtual access to external data sources without physically storing the data.
* Flexible EDW Core Layer:TheFlexible EDW Core layeris the heart of the LSA++ architecture. It is designed to store harmonized, consistent, and reusable data that serves as the foundation for reporting, analytics, and downstream data marts. This layer ensures data quality, consistency, and alignment with business rules, making it the primary storage for enterprise-wide data.
* Other Layers:
* Data Acquisition Layer: Focuses on extracting and loading raw data from source systems into the staging area.It does not store harmonized or consistent data.
* Open ODS Layer: Provides operational reporting capabilities and supports real-time analytics.
However, it is not the main storage for harmonized data.
* Virtual Data Mart Layer: Enables virtual access to external data sources, such as SAP HANA views or third-party systems.It does not store data physically.
* Option A: Open Operational Data Store layerThis option is incorrect because the Open ODS layer is primarily used for operational reporting and real-time analytics. While it stores data, it is not the main storage for harmonized and consistent data.
* Option B: Data Acquisition layerThis option is incorrect because the Data Acquisition layer is responsible for extracting and staging raw data from source systems. It does not store harmonized or consistent data.
* Option C: Flexible Enterprise Data Warehouse Core layerThis option is correct because the Flexible EDW Core layer is specifically designed as the main storage for harmonized, consistent, and reusable data. It ensures data quality and alignment with business rules, making it the central repository for enterprise-wide analytics.
* Option D: Virtual Data Mart layerThis option is incorrect because the Virtual Data Mart layer provides virtual access to external data sources. It does not store data physically and is not the main storage for harmonized data.
* SAP BW/4HANA Modeling Guide: The official documentation highlights the role of the Flexible EDW Core layer as the central storage for harmonized and consistent data. It emphasizes the importance of this layer in ensuring data quality and reusability.
* SAP Note 2700850: This note explains the LSA++ architecture and its layers, providing detailed insights into the purpose and functionality of each layer.
* SAP Best Practices for BW/4HANA: SAP recommends using the Flexible EDW Core layer as the foundation for building enterprise-wide data models.It ensures scalability, flexibility, and consistency in data management.
Key Concepts:Verified Answer Explanation:SAP Documentation and Referencesractical Implications:
When designing an SAP BW/4HANA system, it is essential to:
* Use the Flexible EDW Core layer as the central repository for harmonized and consistent data.
* Leverage the Open ODS layer for operational reporting and real-time analytics.
* Utilize the Virtual Data Mart layer for accessing external data sources without physical storage.
By adhering to these principles, you can ensure that your data architecture is aligned with best practices and optimized for performance and scalability.
References:
SAP BW/4HANA Modeling Guide
SAP Note 2700850: LSA++ Architecture and Layers
SAP Best Practices for BW/4HANA

NEW QUESTION # 68
You defined a condition in a BW query for the top 10 of 100 customers based on sales revenuUsing key figure properties in the BW query which two scenarios regarding result presentation can be achieved? Note:
There are 2 correctanswers to this question.
  • A. One result row with the sales revenue sum of the top 10 customers a second result row with the sales revenue sum of all 100 customers
  • B. One result row with the sales revenue sum of all 100 customers
  • C. One result row with the sales revenue sum of the top 10 customers a second result row with the sales revenue sum of the other 90 customers
  • D. One result row with the sales revenue sum of the top 10 customers
Answer: B,C

NEW QUESTION # 69
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