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[General] 100% Pass Fantastic SAP - Valid C-BW4H-2505 Exam Fee

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【General】 100% Pass Fantastic SAP - Valid C-BW4H-2505 Exam Fee

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SAP Certified Associate - Data Engineer - SAP BW/4HANA Sample Questions (Q71-Q76):NEW QUESTION # 71
Your company manufactures products with country-specific serial numbers.For this scenario you have created
3 custom characteristics with the technical names "RODUCT" "COUNTRY" "SERIAL_NO".How do you need to model the characteristic "RODUCT" to store different attribute values for serial numbers?
  • A. Use "COUNTRY" as a compounding characteristic for "RODUCT".
  • B. Use "SERIAL_NO" as a compounding characteristic for "RODUCT".
  • C. Use "SERIAL_NO" as a transitive attribute for "RODUCT".
  • D. Use "COUNTRY" as a navigation attribute for "RODUCT".
Answer: B

NEW QUESTION # 72
Which node type is available for both graphical modeling of SAP HANA HDI Calculation Views (XS Advanced) and SAP HANA Calculation Views (XS Classic)?
  • A. Minus
  • B. Non Equi Join
  • C. Graph
  • D. Rank
Answer: D

NEW QUESTION # 73
Which entity can be used as a source of an Analytic Model?
  • A. Remote tables of semantic type Text
  • B. Business entities of semantic type Dimension
  • C. Views of semantic type Fact
  • D. Tables of semantic type Hierarchy
Answer: C
Explanation:
AnAnalytic Modelin SAP Data Fabric or SAP BW/4HANA is designed to analyze data by combining facts (measures) and dimensions (attributes). To create an Analytic Model, you need a source entity that represents the fact data. Below is a detailed explanation of why the correct answer is B:
* Incorrect: Business entities of semantic typeDimensionrepresent descriptive attributes (e.g., customer name, product category) rather than measurable data. While dimensions are essential for enriching fact data, they cannot serve as the primary source of an Analytic Model.
Option A: Business entities of semantic type Dimension
* Correct: Views of semantic typeFactcontain measurable data (e.g., sales revenue, quantity sold) and are the primary source for an Analytic Model. These views provide the numerical data required for analysis and reporting.
Option B: Views of semantic type Fact
* Incorrect: Tables of semantic typeHierarchydefine hierarchical relationships (e.g., organizational structures or product hierarchies). While hierarchies are useful for organizing and navigating data, they do not contain measurable data and cannot serve as the source of an Analytic Model.
Option C: Tables of semantic type Hierarchy
* Incorrect: Remote tables of semantic typeTextstore textual descriptions (e.g., product names, region names). These tables are used to enhance dimensions but do not contain measurable data and are not suitable as the source of an Analytic Model.
Option D: Remote tables of semantic type Text
* SAP Data Fabric Documentation: Explains the role of semantic types in defining the purpose of entities (e.g., Fact, Dimension, Hierarchy, Text).
* SAP BW/4HANA Modeling Guide: Describes how Analytic Models are built using fact data as the primary source and dimensions for contextual enrichment.
* SAP Analytics Cloud Integration: Highlights the importance of fact views in enabling advanced analytics and reporting.
References to SAP Data Engineer - Data Fabric ConceptsBy understanding the semantic types and their roles, you can effectively design Analytic Models that meet business requirements for data analysis and reporting.

NEW QUESTION # 74
Which external hierarchy properties can be changed in the query definition? Note: There are 3 correctanswers to this question.
  • A. Display text nodes
  • B. Exp to level
  • C. Time dependency
  • D. Sort direction
  • E. Position of child nodes
Answer: B,D,E

NEW QUESTION # 75
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 # 76
......
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