SAP SAP Certified Associate - Data Engineer - Data Fabric C_BW4H_2404 Exam Dumps: Updated Questions & Answers (May 2025)

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.

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.

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.

References: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.

In SAP BW/4HANA, InfoObjects are fundamental building blocks used to define characteristics (attributes) and key figures in data models. They play a critical role in organizing and managing master data and transactional data. Certain features of InfoObjects are specifically designed to optimize storage and reduce physical data redundancy. Below is a detailed explanation of the correct answers:

Explanation: A reference characteristic allows one characteristic to "reuse" the master data and attributes of another characteristic. Instead of duplicating the master data for the referencing characteristic, it simply points to the referenced characteristic's master data. This significantly reduces physical storage space by avoiding redundancy.

In this scenario, the company manufactures products with country-specific serial numbers, and you need to model the characteristic "PRODUCT" to store different attribute values for serial numbers. Let's analyze each option:

Option A: Use "COUNTRY" as a navigation attribute for "PRODUCT".Navigation attributes are used to provide additional descriptive information about a characteristic. However, they do not allow for unique identification of specific values (like serial numbers) based on another characteristic. Navigation attributes are typically used for reporting purposes and do not fulfill the requirement of storing different attribute values for serial numbers.

Option B: Use "SERIAL_NO" as a transitive attribute for "PRODUCT".Transitive attributes are derived attributes that depend on other attributes in the data model. They are not suitable for directly storing unique values like serial numbers. Transitive attributes are more about deriving values rather than uniquely identifying them.

Option C: Use "COUNTRY" as a compounding characteristic for "PRODUCT".Compounding characteristics involve combining multiple characteristics into a single key. While this could theoretically work if "COUNTRY" were part of the key, it does not address the requirement of associating serial numbers with products. The primary focus here is on "SERIAL_NO," not "COUNTRY."

Option D: Use "SERIAL_NO" as a compounding characteristic for "PRODUCT".This is the correct approach. By defining "SERIAL_NO" as a compounding characteristic for "PRODUCT," you create a composite key that uniquely identifies each product instance based on its serial number. This ensures that different attribute values (e.g., country-specific details) can be stored for each serial number associated with a product.

SAP BW/4HANA Modeling Guide: Explains the concept of compounding characteristics and their use cases in modeling scenarios.

SAP Help Portal: Provides detailed documentation on how to define and use compounding characteristics in SAP BW/4HANA.

SAP Community Blogs: Experts often discuss practical examples of using compounding characteristics to handle complex data relationships.

References:By using "SERIAL_NO" as a compounding characteristic for "PRODUCT," you ensure that the data model supports the storage of unique attribute values for each serial number, meeting the business requirement effectively.

In SAP BW (Business Warehouse), the key date is a critical parameter used in queries to determine the validity of data based on time-dependent objects. The key date allows users to retrieve data as it was valid on a specific date, which is particularly important for time-dependent master data and hierarchies. Below is a detailed explanation of how the key date affects different types of objects in a BW query:

Explanation: Display attributes are additional descriptive fields associated with characteristics in SAP BW. These attributes can be time-dependent, meaning their values may change over time. When a key date is specified in a BW query, the system retrieves the value of the display attribute that was valid on that specific date.

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.

An InfoSource in SAP BW/4HANA is a logical object used in data flows to facilitate the movement and transformation of data between source systems and target objects (e.g., DataStore Objects, InfoCubes). Let’s analyze each option to determine why A and C are correct:

Explanation: An InfoSource allows you to break down a complex transformation into smaller, manageable steps. This modular approach simplifies the design and maintenance of data flows. Importantly, the intermediate results are not stored permanently, which optimizes storage usage and improves performance.

The behavior of a modeled dataflow in SAP BW/4HANA depends on several factors, including theDelta Management methodof the DataSource, thetype of DataStore object (advanced)used as the target, and theupdate methodapplied to key figures in the transformation. To ensure consistent and accurate information in the target, these components must align correctly.

Option B:

DataSource with Delta Management method ABR:TheABR (After Image + Before Image)method tracks both the before and after states of changed records. This is ideal for scenarios where updates need to be accurately reflected in the target system.

DataStore Object (advanced) type Stard:AStaging and Reporting DataStore Object (Stard)is designed for staging data and enabling reporting simultaneously. It supports detailed tracking of changes, making it compatible with ABR.

Update method Summation:Thesummationupdate method aggregates key figures by adding new values to existing ones. This is suitable for ABR because it ensures that updates are accurately reflected without overwriting previous data.

Option C:

DataSource with Delta Management method ABR:As explained above, ABR is ideal for tracking changes.

DataStore Object (advanced) type Stard:Stard supports detailed tracking of changes, making it compatible with ABR.

Update method Move:Themoveupdate method overwrites existing key figure values with new ones. This is also valid for ABR because it ensures that the latest state of the data is reflected in the target.

Option D:

DataSource with Delta Management method ABR:ABR ensures accurate tracking of changes.

DataStore Object (advanced) type Data Mart:AData MartDataStore Object is optimized for reporting and analytics. It can handle aggregated data effectively, making it compatible with ABR.

Update method Summation:Summation is appropriate for aggregating key figures in a Data Mart, ensuring consistent and accurate results.

Correct Combinations:

Option A:

DataSource with Delta Management method ADD:TheADDmethod only tracks new records (inserts) and does not handle updates or deletions. This makes it incompatible with Stard and summation/move update methods, which require full change tracking.

DataStore Object (advanced) type Stard:Stard requires detailed change tracking, which ADD cannot provide.

Update method Move:Move is not suitable for ADD because it assumes updates or changes to existing data.

Option E:

DataSource with Delta Management method AIE:TheAIE (After Image Enhanced)method tracks only the after state of changed records.While it supports some scenarios, it is less comprehensive than ABR and may lead to inconsistencies in certain combinations.

DataStore Object (advanced) type Data Mart:Data Mart objects require accurate aggregation, which AIE may not fully support.

Update method Summation:Summation may not work reliably with AIE due to incomplete change tracking.

Incorrect Options:

SAP Data Engineer - Data Fabric Context:In the context ofSAP Data Engineer - Data Fabric, ensuring consistent and accurate dataflows is critical for building reliable data pipelines. The combination of Delta Management methods, DataStore object types, and update methods must align to meet specific business requirements. For example:

Stardobjects are often used for staging and operational reporting, requiring detailed change tracking.

Data Martobjects are used for analytics, requiring aggregated and consistent data.

For further details, refer to:

SAP BW/4HANA Data Modeling Guide: Explains Delta Management methods and their compatibility with DataStore objects.

SAP Learning Hub: Offers training on designing and implementing dataflows in SAP BW/4HANA.

By selectingB,C, andD, you ensure that the combinations provide consistent and accurate information for the target.

InSAP BW/4HANA, separatingmaster datafromtransactional datais a fundamental design principle that provides numerous benefits for data management, reporting, and system performance. Below is an explanation of the correct answers and why they are valid.

B. Allowing different data load frequency

Master data (e.g., customer names, product descriptions) typically changes less frequently than transactional data (e.g., sales orders, invoices). By separating these two types of data, you can schedule independent data loads for each.

For example, master data might be updated weekly or monthly, while transactional data could be loaded daily or even in real-time. This separation ensures efficient data management and reduces unnecessary processing overhead.

To federate data betweenSAP Datasphereand anInfoCubeinSAP BW powered by SAP HANA, you need to establish a connection that allows SAP Datasphere to access the data stored in the InfoCube. Below is an explanation of the options:

Explanation: This is the correct answer. AnSAP ABAP Connectionallows SAP Datasphere to connect to an SAP BW system and access its data objects, including InfoCubes. This connection leverages theABAP stackto enable seamless integration between SAP Datasphere and SAP BW.