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[Hardware] Dumps C-BCBDC-2505 Torrent | C-BCBDC-2505 Exam Bootcamp

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【Hardware】 Dumps C-BCBDC-2505 Torrent | C-BCBDC-2505 Exam Bootcamp

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SAP C-BCBDC-2505 Exam Syllabus Topics:
TopicDetails
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 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 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.

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SAP Certified Associate - SAP Business Data Cloud Sample Questions (Q56-Q61):NEW QUESTION # 56
In SAP Business Data Cloud, what is the function of the Connection Management feature?
Please choose the correct answer.
Response:
  • A. Define connectivity to source systems
  • B. Create backup jobs for storage
  • C. Control dashboard visualization access
  • D. Establish data flows between widgets
Answer: A

NEW QUESTION # 57
Which of the following can you do with an SAP Datasphere Data Flow? Note: There are 3 correct answers to this question.
  • A. Use a Python script for data transformation.
  • B. Delete records from a target table.
  • C. Fill different target tables in parallel.
  • D. Write data to a table in a different SAP Datasphere tenant.
  • E. Integrate data from different sources into one table.
Answer: A,C,E
Explanation:
An SAP Datasphere Data Flow is a highly versatile and powerful tool for data integration, transformation, and loading. With a Data Flow, you can effectively integrate data from different sources into one table (B). This is a fundamental capability, allowing you to combine data from various tables, views, or even external connections, apply transformations, and consolidate it into a single target table. Another advanced capability is to fill different target tables in parallel (D). Data Flows are designed to handle complex scenarios efficiently, and this parallelism optimizes performance when you need to populate multiple destination tables simultaneously from a single flow. Furthermore, Data Flows support extensibility, allowing you to use a Python script for data transformation (E). This enables advanced, custom data manipulation logic that might not be available through standard graphical operations, providing immense flexibility for complex business rules. Writing data to a different Datasphere tenant (A) is not a direct capability of a Data Flow, and deleting records from a target table (C) is typically handled via specific operations within the target table's management or through SQL scripts rather than a standard data flow write operation.

NEW QUESTION # 58
Which data source does not require a model be created for it in SAP Analytics Cloud for use in stories?
Please choose the correct answer.
Response:
  • A. Microsoft SharePoint
  • B. SAP Business Warehouse (BW)
  • C. SAP Datasphere
Answer: C

NEW QUESTION # 59
Why would you choose the "Validate Remote Tables" feature in the SAP Datasphere repository explorer?
  • A. To preview data of remote tables
  • B. To detect if remote tables are defined that are not used in Views
  • C. To test if data has been replicated completely
  • D. To identify structure updates of the remote sources
Answer: D
Explanation:
The "Validate Remote Tables" feature in the SAP Datasphere repository explorer is primarily used to identify structure updates of the remote sources. When a remote table is created in Datasphere, it establishes a metadata connection to a table or view in an external source system. Over time, the structure of the source object (e.g., column additions, deletions, data type changes) might change. The "Validate Remote Tables" function allows you to compare the metadata currently stored in Datasphere for the remote table with the actual, current metadata in the source system. If discrepancies are found, Datasphere can highlight these structural changes, prompting you to update the remote table's definition within Datasphere to match the source. This ensures that views and data flows built on these remote tables continue to function correctly and align with the underlying source structure, preventing data access issues or incorrect data interpretations.

NEW QUESTION # 60
Which automatically created dimension type can you delete from an SAP Analytics Cloud analytic data model?
  • A. Version
  • B. Date
  • C. Organization
  • D. Generic
Answer: D
Explanation:
In an SAP Analytics Cloud (SAC) analytic data model, you typically have a degree of flexibility in managing dimensions. Among the automatically created dimension types, the Generic dimension can often be deleted if it's not relevant or desired for your analysis. Generic dimensions are often generated by the system based on identified data patterns but might not always align with specific business requirements or be redundant. In contrast, Date, Version, and Organization dimensions are fundamental and often system-critical, especially for planning models (Version, Organization) or time-based analysis (Date). These core dimensions are usually not freely deletable or are required by the system for specific functionalities. Therefore, for tailoring your analytic model to specific business needs, the ability to remove generic dimensions provides greater control and simplification.

NEW QUESTION # 61
......
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