C_BW4H_2404 VALID BRAINDUMPS, C_BW4H_2404 NEW CRAM MATERIALS

C_BW4H_2404 Valid Braindumps, C_BW4H_2404 New Cram Materials

C_BW4H_2404 Valid Braindumps, C_BW4H_2404 New Cram Materials

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SAP Certified Associate - Data Engineer - Data Fabric Sample Questions (Q12-Q17):

NEW QUESTION # 12
InfoObject CITY is defined as a display attribute for InfoObject CUSTOMER, and InfoObject COUNTRY is defined as a display attribute for InfoObject CITY. In a master data report you want to display the COUNTRY of a CUSTOMER.
Which options do you have to realize the scenario? Note: There are 3 correct answer to this question."

  • A. Generate external views for CUSTOMER, CITY and COUNTRY, and join them in another calculation view.
  • B. Combine CUSTOMER, CITY and COUNTRY in a Composite Provider using a sequence of left outer join operators.
  • C. Combine CUSTOMER, CITY and COUNTRY in a Open ODS View using a sequence of associations.
  • D. Add COUNTRY as a transitive attribute for CUSTOMER in InfoObject definition.
  • E. Include CUSTOMER to rows in the BW Query on CUSTOMER and activate the Universal Display Hierarchy setting.

Answer: A,C,D


NEW QUESTION # 13
Which feature of a DataStore object (advanced) should be made available to improve the performance for data analysis?

  • A. ChangeLog
  • B. Partitioning
  • C. Inventory Management
  • D. Snapshot Support

Answer: B

Explanation:
* DataStore Object (Advanced): In SAP BW/4HANA, a DataStore Object (advanced) is a flexible data storage object that supports both staging and reporting. It allows for detailed data storage and provides advanced features like partitioning, compression, and snapshot support.
* Partitioning: Partitioning divides large datasets into smaller, manageable chunks based on specific criteria (e.g., time-based or value-based). This improves query performance by reducing the amount of data scanned during analysis.
* Snapshot Support: This feature allows periodic snapshots of data to be stored in the DataStore Object (advanced). While useful for historical analysis, it does not directly improve query performance.
* Inventory Management: This is unrelated to performance optimization in the context of data analysis.
* ChangeLog: The ChangeLog stores delta records for incremental updates. While important for data loading, it does not directly enhance query performance.
Key Concepts:Why Partitioning Improves Performance:Partitioning is a well-known technique in database management systems to optimize query performance. By dividing the data into partitions, queries can focus on specific subsets of data rather than scanning the entire dataset. For example:
* Time-based partitioning (e.g., by year or month) allows queries to target only relevant time periods.
* Value-based partitioning (e.g., by region or category) enables faster filtering of data.
In SAP BW/4HANA, enabling partitioning for a DataStore Object (advanced) significantly enhances the performance of data analysis by reducing I/O operations and improving parallel processing capabilities.
* A. Snapshot Support: While useful for historical reporting, it does not directly improve query performance.
* C. Inventory Management: This is unrelated to query performance and pertains to managing materialized data.
* D. ChangeLog: This is used for delta handling and does not impact query performance.
* SAP BW/4HANA Documentation: The official documentation highlights partitioning as a key feature for optimizing query performance in DataStore Objects (advanced).
* SAP Best Practices for Performance Optimization: Partitioning is recommended for large datasets to improve query execution times.
* SAP Note on DataStore Object (Advanced): Notes such as 2708497 discuss the benefits of partitioning for performance.
Why Other Options Are Incorrect:References:By enabling partitioning, you can significantly improve the performance of data analysis in a DataStore Object (advanced).


NEW QUESTION # 14
Which are use cases for sharing an object? Note: There are 3 correct answers to this question.

  • A. Use remote tables located in the SAP BW bridge space across SAP Datasphere core spaces.
  • B. A source connection needs to be used in different replication flows.
  • C. A product dimension view should be used in different fact models for different business segments.
  • D. A BW time characteristic should be used across multiple DataStore objects (advanced).
  • E. Time tables are defined in a central space should be used in many other spaces.

Answer: A,B,C


NEW QUESTION # 15
What is the maximum number of characteristics that can be used for a multi-dimensional exception aggregation in a BW query?

  • A. 0
  • B. 1
  • C. 2
  • D. 3

Answer: D


NEW QUESTION # 16
Which are purposes of the Open Operational Data Store layer in the layered scalable architecture (LSA++) of SAP BW/4HANA? Note: There are 2 correct answers to this question.

  • A. Initial staging of source system data
  • B. Harmonization of data from several source systems
  • C. Real-time reporting on source system data without staging
  • D. Transformations of data based on business logic

Answer: A,B

Explanation:
TheOpen Operational Data Store (ODS)layer in theLayered Scalable Architecture (LSA++)of SAP BW
/4HANA plays a critical role in managing and processing data as part of the overall data warehousing architecture. The Open ODS layer is designed to handle operational and near-real-time data requirements while maintaining flexibility and performance. Below is an explanation of the purposes of this layer and why the correct answers areAandC.
* A. Harmonization of data from several source systems
* The Open ODS layer is often used to harmonize data from multiple source systems. This involves consolidating and standardizing data from different sources into a unified format.
* For example, if you have sales data coming from different ERP systems with varying structures or naming conventions, the Open ODS layer can be used to align these differences before the data is further processed or consumed for reporting.


NEW QUESTION # 17
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