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[Hardware] Real SAP Exam Questions And Answers From C-BW4H-2505​​​​

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【Hardware】 Real SAP Exam Questions And Answers From C-BW4H-2505​​​​

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SAP C-BW4H-2505 Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 2
  • 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.
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
  • SAP BW
  • 4HANA Data Flow: This section of the exam measures the practical ability of SAP Consultants to load data within the SAP BW
  • 4HANA environment. It assesses familiarity with data movement and transformation processes across different layers of the system.
Topic 5
  • 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 6
  • InfoObjects and InfoProviders:This section tests the knowledge of Data Engineers in working with InfoObjects and InfoProviders in SAP BW
  • 4HANA. It involves handling data structures used for organizing, storing, and accessing analytical data.
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 (Q76-Q81):NEW QUESTION # 76
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. A primary key needs to exist.
  • B. Real-time access needs to be enabled
  • C. Operational Data Provisioning (ODP) must be enabled
  • D. Extractor must be based on a function module
Answer: A,C
Explanation:
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).

NEW QUESTION # 77
You want to create an HD! Calculation View (data category Dimension) and integrate it into an HDI Calculation View (data category Cube with Star Join) of the same HDI container.What is the first required step you need to take?
  • A. Create and build the HDI Calculation View (data category Cube with Star Join).
  • B. Create a synonym for the HDI Calculation View (data category Cube with Star Join).
  • C. Create a synonym for the HDI Calculation View (data category Dimension).
  • D. Create and build the HDI Calculation View (data category Dimension).
Answer: D

NEW QUESTION # 78
Which modeling decisions may have side effects on runtime performance? Note: There are 3 correct answers to this question.
  • A. Include a characteristic from the underlying DataMart DataStore Object in the CompositeProvider instead of a navigation attribute.
  • B. Use a transitive attribute instead of an attribute that is directly assigned to a characteristic.
  • C. Uncheck the "Write change log" property for a Stard DataStore Object.
  • D. Change a time-independent attribute of a characteristic to a time-dependent attribute.
  • E. Move a characteristic within a DataMart DataStore object to a different group.
Answer: A,B,C
Explanation:
When modeling data in SAP BW/4HANA, certain decisions can have significant side effects on runtime performance. Let's analyze each option:
* Option A: Use a transitive attribute instead of an attribute that is directly assigned to a characteristic.
Transitive attributes are derived attributes that depend on other attributes in the data model. Using a transitive attribute instead of a directly assigned attribute introduces additional complexity during query execution because the system must calculate the value dynamically based on the underlying relationships. This can lead to slower query performance, especially for large datasets.
* Option B: Uncheck the "Write change log" property for a Standard DataStore Object.Disabling the
"Write change log" property improves performance rather than degrading it. By not writing changes to the change log, the system reduces the overhead associated with tracking historical data. Therefore, this decision does not negatively impact runtime performance.
* Option C: Move a characteristic within a DataMart DataStore object to a different group.Moving a characteristic to a different group within a DataMart DataStore Object primarily affects the logical organization of data but does not directly impact runtime performance. The physical storage and query execution remain unaffected by such changes.
* Option D: Change a time-independent attribute of a characteristic to a time-dependent attribute.
Converting a time-independent attribute to a time-dependent one introduces additional complexity into the data model. Time-dependent attributes require the system to manage multiple versions of the attribute over time, which increases the volume of data and the computational effort required for queries. This can significantly degrade runtime performance, especially for queries involving large datasets or frequent updates.
* Option E: Include a characteristic from the underlying DataMart DataStore Object in the CompositeProvider instead of a navigation attribute.Including a characteristic directly from the underlying DataMart DataStore Object in the CompositeProvider can improve performance compared to using a navigation attribute. Navigation attributes require additional joins during query execution, which can slow down performance. However, if the question implies replacing a navigation attribute with a direct characteristic, this decision can have positive performance implications. Conversely, if the reverse is implied (using navigation attributes instead of direct characteristics), it would degrade performance.
References:SAP BW/4HANA Modeling Guide: Explains the impact of transitive attributes, time-dependent attributes, and navigation attributes on query performance.
SAP Help Portal: Provides detailed documentation on best practices for optimizing data models in SAP BW
/4HANA.
SAP Community Blogs: Experts often discuss the performance implications of various modeling decisions in real-world scenarios.
In summary, options A, D, and E involve modeling decisions that can negatively impact runtime performance due to increased computational complexity or additional joins during query execution.

NEW QUESTION # 79
Which of the following are possible delta-specific fields for a generic DataSource in SAP S/4HANA? Note:
There are 3 correctanswers to this question.
  • A. Time stamp
  • B. Request ID
  • C. Record mode
  • D. Calendar day
  • E. Numeric pointer
Answer: A,D,E

NEW QUESTION # 80
What is the maximum number of reference characteristics that can be used for one key figure with a multi- dimensional exception aggregation in a BW query?
  • A. 0
  • B. 1
  • C. 2
  • D. 3
Answer: A

NEW QUESTION # 81
......
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