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SAP Certified Associate
- SAP Business Data
Cloud
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[ Total Questions: 10]
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SAP - C_BCBDC_2505Pass Exam
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Question #:1
Which of the following data source objects can be used for an SAP Datasphere Replication Flow? Note: 
There are 2 correct answers to this question.
Google Big Query dataset
ABAP CDS view
Oracle database table
MS Azure SQL table
Answer: B C
Explanation
SAP Datasphere Replication Flows are designed for efficient and continuous data transfer from source 
systems into Datasphere. For these flows, specific types of data source objects are supported to ensure robust 
and reliable replication. Two common and highly supported data source objects are an ABAP CDS view 
(Core Data Services view) and an Oracle database table. ABAP CDS views are particularly relevant for 
replicating data from SAP source systems (like SAP S/4HANA or SAP ERP), as they provide a semantically 
rich and optimized way to access business data. Oracle database tables represent a broad category of third-
party relational databases from which data can be replicated. While Datasphere offers connectivity to various 
cloud data warehouses like Google BigQuery or MS Azure SQL for virtual access or snapshotting, 
Replication Flows are typically used for continuous, high-volume data ingestion from operational databases 
and SAP systems using their native database tables or views like CDS views.
Question #:2
What do you use to write data from a local table in SAP Datasphere to an outbound target?
Transformation Flow
Data Flow
Replication Flow
CSN Export
Answer: B
Explanation
To write data from a local table in SAP Datasphere to an outbound target, you primarily use a Data Flow. A 
Data Flow in SAP Datasphere is a powerful tool designed for comprehensive data integration and 
transformation. It allows you to extract data from various sources (including local tables within Datasphere), 
perform various transformations (like joins, aggregations, filtering, scripting), and then load the processed 
data into a specified target. This target can be another local table, a remote table, or an outbound target like an 
external database or a file system. While a Replication Flow (C) is used for ingesting data into Datasphere, 
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and a Transformation Flow (A) is not a standalone artifact for outbound writes (often part of a Data Flow), the 
Data Flow provides the complete framework for extracting, transforming, and loading data, including sending 
it to external destinations.
Question #:3
Which entity can be used as a direct source of an SAP Datasphere analytic model?
Business entities of semantic type Dimension
Views of semantic type Fact
Tables of semantic type Hierarchy
Remote tables of semantic type Text
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.
Question #:4
Why would you choose the "Validate Remote Tables" feature in the SAP Datasphere repository explorer?
To test if data has been replicated completely
To detect if remote tables are defined that are not used in Views
To preview data of remote tables
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 
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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.
Question #:5
Which steps are executed when an SAP Business Data Cloud Intelligent Application is installed? Note: There 
are 2 correct answers to this question.
Connection of SAP Datasphere with SAP Analytics Cloud
Creation of a dashboard for visualization
Execution of a machine-learning algorithm
Replication of data from the business applications to Foundation Services
Answer: A D
Explanation
When an SAP Business Data Cloud (BDC) Intelligent Application is installed, two key foundational steps are 
executed to ensure its operational readiness. Firstly, there is the connection of SAP Datasphere with SAP 
Analytics Cloud (SAC). This establishes the vital link that allows the intelligent application's analytical 
models, which reside and are managed within SAP Datasphere, to be consumed and visualized in SAC. SAC 
serves as the front-end for analytical consumption, enabling users to interact with the insights generated by 
the intelligent application. Secondly, the installation process involves the replication of data from the business 
applications to Foundation Services. Foundation Services act as the initial landing zone for raw business data 
within the BDC architecture. This data replication ensures that the intelligent application has access to the 
necessary operational data from source systems to perform its functions, whether it involves data processing, 
enrichment, or feeding machine learning models. These steps are crucial for the intelligent application to 
ingest, process, and ultimately present valuable business insights.
Question #:6
In SAP Analytics Cloud, you have a story based on an import model. The transactional data in the model's 
data source changes. How can you update the data in the model?
Refreshthe story
Allow model import
Refresh the data source
Schedule the import
Answer: D
SAP - C_BCBDC_2505Pass Exam
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Explanation
When an SAP Analytics Cloud (SAC) story is based on an import model, the data is physically copied and 
stored within SAC. Therefore, simply refreshing the story (option A) will only update the visualization with 
the data already in the model and will not pull new data from the source. Similarly, "Allow model import" 
(option B) isn't a direct action for updating data, but rather a prerequisite for the import process itself. 
"Refresh the data source" (option C) is not an action performed within SAC for an import model. To update 
the data in the model when the transactional data in its source changes, you must schedule the import (option 
D) or manually re-run the import process. This process re-fetches the latest data from the original source 
system and updates the SAC import model, ensuring your story reflects the most current information. This 
scheduling can be set up to occur at regular intervals, keeping the model synchronized with the source data.
Question #:7
What are the prerequisites for loading data using Data Provisioning Agent (DP Agent) for SAP Datasphere? 
Note: There are 2 correct answers to this question.
The DP Agent is installed and configured on a local host.
The data provisioning adapter is installed.
The Cloud Connector is installed on a local host.
The DP Agent is configured for a dedicated space in SAP Datasphere.
Answer: A B
Explanation
To load data into SAP Datasphere using the Data Provisioning Agent (DP Agent), two crucial prerequisites 
must be met. Firstly, the DP Agent must be installed and configured on a local host (A). The DP Agent acts as 
a bridge between your on-premise data sources and SAP Datasphere in the cloud. It needs to be deployed on a 
server within your network that has access to the source systems you wish to connect. Secondly, the relevant 
data provisioning adapter must be installed (B) within the DP Agent framework. Adapters are specific 
software components that enable the DP Agent to connect to different types of source systems (e.g., SAP 
HANA, Oracle, Microsoft SQL Server, filesystems). Without the correct adapter, the DP Agent cannot 
communicate with and extract data from your chosen source. While the Cloud Connector (C) is often used for 
secure access to SAP backend systems in the cloud, it's not a direct prerequisite for the DP Agent itself for all 
data sources. Configuring the DP Agent for a specific space (D) is a step after the initial installation and 
adapter setup.
Question #:8
Which of the following can you do with an SAP Datasphere Data Flow? Note: There are 3 correct answers to 
this question.
Write data to a table in a different SAP Datasphere tenant.
Integrate data from different sources into one table.
Delete records from a target table.
SAP - C_BCBDC_2505Pass Exam
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Fill different target tables in parallel.
Use a Python script for data transformation.
Answer: B D 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.
Question #:9
What is required to use version management in an SAP Analytics Cloud story?
Analytic model
Classic mode
Optimized mode
Planning model
Answer: D
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.
Question #:10
SAP - C_BCBDC_2505Pass Exam
6 of 6Verified Solution - 100% Result
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Which semantic usage type does SAP recommend you use in an SAP Datasphere graphical view to model 
master data?
Analytical Dataset
Relational Dataset
Fact
Dimension
Answer: D
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.
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