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| 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.
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| 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.
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| 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 Certified Associate - SAP Business Data Cloud Sample Questions (Q44-Q49):
NEW QUESTION # 44
What are the types of datasets in SAP Analytics Cloud?
There are 2 correct answers to this question.
Response:
- A. Import
- B. Public
- C. Live
- D. Embedded
Answer: B,D
NEW QUESTION # 45
What is required to use version management in an SAP Analytics Cloud story?
- A. Planning model
- B. Optimized mode
- C. Analytic model
- D. Classic mode
Answer: A
Explanation:
To leverage version management capabilities within an SAP Analytics Cloud (SAC) story, it is a fundamental requirement that the story is built on a planning model. Version management is a core feature specifically designed for planning functionalities. It enables users to create, manage, and compare different scenarios or iterations of data, such as "Actual," "Budget," "Forecast," or various planning versions. This is critical for budgeting, forecasting, and what-if analysis, allowing planners to work on different data sets concurrently and track changes over time. While analytic models are used for general reporting and analysis, they do not inherently support the robust version management features that are integral to planning processes. Therefore, if you intend to utilize version management to compare different data scenarios or manage planning cycles, your SAC story must be connected to a planning model.
NEW QUESTION # 46
Which of the following is true about data access control in SAP Business Data Cloud?
Please choose the correct answer.
Response:
- A. Access is managed using roles and policies
- B. Access can be assigned only at the workspace level
- C. All users have full read-write access by default
- D. There is no support for data-level authorization
Answer: A
NEW QUESTION # 47
Which data modeling component is used to define relationships between business entities?
Please choose the correct answer.
Response:
- A. Data Access Control
- B. Data Flow
- C. Association
- D. Semantic Join
Answer: C
NEW QUESTION # 48
Which operation is implemented by the Foundation Services of SAP Business Data Cloud?
- A. Data transformation and enrichment to generate a data product.
- B. Generation of an analytic model by adding semantic information.
- C. Storage of raw data inside a CDS view.
- D. Execution of machine learning algorithms to generate additional insights.
Answer: A
Explanation:
The Foundation Services component of SAP Business Data Cloud (BDC) is responsible for orchestrating the fundamental processes of data preparation and productization. Specifically, a key operation implemented by Foundation Services is data transformation and enrichment to generate a data product. Foundation Services takes raw data ingested from various business applications and applies necessary transformations, cleanses it, and enriches it with additional context or calculated attributes. This process is crucial for creating high-quality, consumable data products, which are curated and semantically rich datasets designed for specific business use cases. While machine learning algorithms are executed by Intelligent Applications (which consume these data products), and analytic models are built in SAP Datasphere (which is part of the BDC ecosystem), Foundation Services focuses on the foundational work of preparing and productizing the data itself, ensuring it's ready for advanced analytics and consumption.
NEW QUESTION # 49
......
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