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SAP C_BCBDC_2505 Exam Syllabus Topics:

Topic	Details
Topic 1	SAP Business Data Cloud: This section of the exam measures the skills of Data Analysts and covers core concepts of the SAP Business Data Cloud. Candidates are expected to understand its key components, integration mechanisms, and how it functions as a foundation for unified data management across SAP and non-SAP environments. The focus is on enabling data connectivity and providing governed access to data across the enterprise.
Topic 2	SAP Datasphere: This section of the exam measures the skills of Solution Consultants and covers a comprehensive understanding of SAP Datasphere. Candidates should demonstrate knowledge of data modeling, transformation, and harmonization using SAP Datasphere tools. It evaluates how well they can work with data layer structuring, semantic modeling, and integration to support real-time data access for various business applications.
Topic 3	SAP Analytics Cloud: This section of the exam measures the skills of Data Analysts and covers the use of SAP Analytics Cloud in data visualization, story building, and dashboard creation. Candidates should be familiar with its planning and predictive capabilities, along with how to utilize data insights to drive business decision-making. It also includes managing user access and collaborating through shared analytics assets.

SAP Certified Associate - SAP Business Data Cloud Sample Questions (Q46-Q51):

NEW QUESTION # 46
What are the styles of models?
There are 2 correct answers to this question.
Response:

A. Live
B. Analytic
C. On-premise
D. Planning

Answer: B,D

NEW QUESTION # 47
An international bank wants to provide regional analysts with business-ready data models without granting direct access to raw data sources. What features should be implemented?
There are 3 correct answers to this question.
Response:

A. Role-based permissions to restrict access
B. Direct SQL access to source systems
C. Business Builder to define and expose semantic views
D. Data Catalog to organize and tag shared models
E. Business Content Import from SAP NetWeaver

Answer: A,C,D

NEW QUESTION # 48
A logistics company aims to integrate operational data from SAP S/4HANA and enrich it with supplier metrics. They want to avoid replicating the data and maintain live updates. Which combination of features should be used?
There are 3 correct answers to this question.
Response:

A. Business Builder for semantic modeling
B. Data replication through CSV
C. Manual KPI calculation in spreadsheets
D. Role-based data access
E. Remote Tables from SAP S/4HANA

Answer: A,D,E

NEW QUESTION # 49
You want to combine external data with internal data via product ID. Although the data may be inconsistent, such as the external data contains the letter "O" where the internal data contains the digit 0, you still want to combine them. Which artifact should you use for matching?

A. Graphical View
B. Intelligent Lookup
C. Entity Relationship Model
D. Analytic Model

Answer: B

Explanation:
When faced with the challenge of combining data from different sources where the matching keys (like "Product ID") are inconsistent or contain variations (e.g., "O" vs. "0"), the recommended artifact in SAP Datasphere for such fuzzy or approximate matching scenarios is an Intelligent Lookup. An Intelligent Lookup (D) leverages machine learning capabilities to identify and map records that are semantically similar but not exact matches. Unlike standard joins in graphical views or SQL views which require precise key matches, Intelligent Lookups can handle data quality issues, typos, and variations, allowing you to successfully link disparate records that would otherwise be missed. This is particularly valuable when integrating data from external systems or legacy sources where perfect data standardization is not feasible, ensuring a more comprehensive and accurate combined dataset for analysis.

NEW QUESTION # 50
What is the purpose of SAP Datasphere in the context of SAP Business Data Cloud?
Please choose the correct answer.
Response:

A. To add semantically rich models to prepare data for analysis
B. To transfer data to SAP Databricks for machine learning processing
C. To extract data from the SAP source systems
D. To store data to create a data warehouse

Answer: A

NEW QUESTION # 51
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