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SAP C_BCBDC_2505 Exam Syllabus Topics:

Topic	Details
Topic 1	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.
Topic 2	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 3	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.

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SAP C_BCBDC_2505 Exam | Real C_BCBDC_2505 Exam Questions - Help you Prepare C_BCBDC_2505: SAP Certified Associate - SAP Business Data Cloud Exam Easily

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SAP Certified Associate - SAP Business Data Cloud Sample Questions (Q54-Q59):

NEW QUESTION # 54
What is the role of "Spaces" in SAP Business Data Cloud?
Please choose the correct answer.
Response:

A. Manage user accounts across SAP applications
B. Deploy machine learning models
C. Store logs and audit information
D. Define and isolate resources for specific teams or projects

Answer: D

NEW QUESTION # 55
Which entity can be used as a direct source of an SAP Datasphere analytic model?

A. Business entities of semantic type Dimension
B. Views of semantic type Fact
C. Remote tables of semantic type Text
D. Tables of semantic type Hierarchy

Answer: B

Explanation:
An SAP Datasphere analytic model is specifically designed for multi-dimensional analysis, and as such, it requires a central entity that contains the measures (key figures) to be analyzed and links to descriptive dimensions. Therefore, a View of semantic type Fact (B) is the most appropriate and commonly used direct source for an analytic model. A "Fact" view typically represents transactional data, containing measures (e.g., sales amount, quantity) and foreign keys that link to dimension views (e.g., product, customer, date). While "Dimension" type entities (A) provide descriptive attributes and are linked to the analytic model, they are not the direct source of the model itself. Tables of semantic type Hierarchy (C) are used within dimensions, and remote tables of semantic type Text (D) typically provide text descriptions for master data, not the core fact data for an analytic model. The Fact view serves as the central point for an analytic model's measures and its connections to all relevant dimensions.

NEW QUESTION # 56
What are the styles of models?
There are 2 correct answers to this question.
Response:

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

Answer: A,B

NEW QUESTION # 57
What are the working modes of a story?
There are 3 correct answers to this question.
Response:

A. Story
B. Data
C. Edit
D. Presentation
E. View

Answer: C,D,E

NEW QUESTION # 58
Which semantic usage type does SAP recommend you use in an SAP Datasphere graphical view to model master data?

A. Analytical Dataset
B. Dimension
C. Relational Dataset
D. Fact

Answer: B

Explanation:
When modeling master data within an SAP Datasphere graphical view, SAP strongly recommends using the Dimension semantic usage type. Master data, such as customer information, product details, or organizational hierarchies, provides context and descriptive attributes for transactional data. Marking a view as a "Dimension" explicitly signals to downstream consumption tools (like SAP Analytics Cloud) and other Datasphere models that this view contains descriptive attributes that can be used for filtering, grouping, and providing context to analytical queries. This semantic tagging ensures that the data is interpreted and utilized correctly in analytical scenarios, distinguishing it from "Fact" data (which represents transactional measures) or "Relational Dataset" (a more generic type without specific analytical semantics). Using the "Dimension" semantic usage type aligns with best practices for building robust and understandable data models for analytics.

NEW QUESTION # 59
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