Learn, Practice, and Improve with SAP C_BCBDC_2505 Practice Test Questions

Explanation

Why A and D are incorrect:

A. To directly access the SAP HANA Cloud database of SAP Datasphere
Database users are space-specific technical users that enable direct SQL access (via JDBC/ODBC) to the underlying SAP HANA Cloud database of their assigned space — required when bypassing the Datasphere modeling/UI layer.

D. To provide a secure method for data exchange for 3rd party tools
They deliver a secure, credential-controlled connection allowing third-party tools (e.g., Power BI, Tableau, Alteryx, SQL clients, ETL tools) to safely read from or write to the space’s data — without granting full SAP Datasphere user accounts.

Why the other options are incorrect:

B. To create a graphical view in SAP Datasphere
→ Graphical views (calculation views, consumption views, etc.) are exclusively created in the Data Builder user interface using a regular SAP Datasphere user who has modeling privileges (e.g., DW Modeler role). Database users are technical SQL-only accounts and have no role or access in the graphical modeling environment.

C. To access all schemas in SAP Datasphere
→ Database users are strictly limited to their own space’s schema only. They are created per space and provide very narrow, isolated access — they do not allow viewing or querying schemas of other spaces, shared content, or system/global schemas. Broad cross-schema access requires special administrative roles or configurations, not a standard database user.

Official References:

SAP Learning: Exploring SAP Datasphere → Introducing Spaces
https://learning.sap.com/courses/exploring-sap-datasphere/introducing-sap-datasphere-spaces

SAP Help Portal: Managing Database Users in SAP Datasphere Spaces

Explanation

Why JavaScript is Correct

In SAP Analytics Cloud (SAC), scripting inside stories and analytic applications is done with JavaScript.

It is used to:
Respond to user interactions – e.g., clicking a button, changing filters, or selecting data points.
Control widgets and UI behavior – like dynamically showing/hiding charts, updating values, or triggering navigation between pages.
Perform custom calculations – manipulate data from models programmatically, beyond what standard SAC formulas can do.
SAC provides a JavaScript API for interacting with data models, charts, tables, and input controls, making it the standard scripting language for SAC stories and analytic applications.
This scripting runs on the client side, so it’s part of the SAC story environment, not on the SAP backend system. (SAP Help: Scripting in SAC)

Why the Other Options are Wrong

A. Wrangling Expression Language — Incorrect
Used exclusively for data preparation and transformation, not for scripting in stories.
Allows you to perform operations like filtering, merging, pivoting, or cleaning data before it’s loaded into models, but it cannot respond to user events or manipulate story elements.

B. ABAP — Incorrect
ABAP is SAP’s backend programming language, used for custom development in S/4HANA, SAP BW, and other SAP systems.
SAC stories run in the cloud front-end, so ABAP cannot be executed there. It is unrelated to client-side scripting or story interactivity.

C. Python — Incorrect
Python is used for data science scenarios, such as in SAP Data Intelligence or external machine learning environments.
SAC does not support Python for scripting inside stories or analytic applications. It cannot interact with SAC UI components or story events.

Reference:

SAP Help: Scripting in SAP Analytics Cloud

Explanation

Why A and B are correct:

✅ A. The DP Agent is installed and configured on a local host.
This is correct because the DP Agent software must be downloaded and installed on a server within your on-premise network. This server acts as the secure bridge that connects your local data sources to the cloud-based SAP Datasphere. After installation, it must be configured and connected to your specific SAP Datasphere tenant.

✅ B. The data provisioning adapter is installed.
This is correct. The core DP Agent requires a specific adapter to communicate with each type of source system (e.g., SAP HANA, SAP S/4HANA, SQL Server). You must install and then register the correct adapter with SAP Datasphere to enable the connection and data loading capabilities for that source.

Why others are wrong:

❌ C. The Cloud Connector is installed on a local host.
This is incorrect for general DP Agent data loading. The Cloud Connector is a separate component used for different scenarios, primarily for creating live data connections of the "Tunnel" type, which are required for specific tasks like importing analytic models from SAP BW/4HANA. It is not a prerequisite for the standard batch or real-time data replication performed by the DP Agent.

❌ D. The DP Agent is configured for a dedicated space in SAP Datasphere.
This is incorrect. The DP Agent is registered and managed at the system (tenant) level of SAP Datasphere, not within an individual Space. An administrator configures the agent once for the entire tenant. After this system-level setup, any user with the proper role can create a connection within their Space that uses this centrally available agent.

Official References

The information above is confirmed by the following official SAP sources:

The SAP Community blog on connectivity states the steps: "Install the Data Provisioning Agent" and then enable the required adapters.
https://community.sap.com/t5/technology-blog-posts-by-members/sap-datasphere-connectivity-with-s-4-hana-system-amp-sap-analytics-cloud/ba-p/13636045

The official SAP blog on prerequisites for BW/4HANA model transfer explicitly lists "Install and configure a Data Provisioning Agent" and "register the SAP HANA adapter with SAP Datasphere" as separate, required setup steps.
https://community.sap.com/t5/technology-blog-posts-by-sap/part-1-prerequisites-and-setup-instructions-for-datasphere-bw-4hana-model/ba-p/13762642

Explanation:

Why B is correct:

SAP Business Data Cloud is a unified SaaS platform that integrates SAP Datasphere and SAP Analytics Cloud (SAC). Within this ecosystem, SAP Datasphere serves as the primary "Business Data Fabric." The live connection "provided by SAP BDC" refers specifically to the deep integration where SAC consumes semantically rich, analytical models directly from SAP Datasphere. This allows for real-time visualization without data replication, maintaining the business context defined in Datasphere.

Why A is wrong:

While SAP Analytics Cloud can connect to SAP Business ByDesign, this is typically handled as an application-specific connection. It is not the core architectural "live connection" that defines the SAP BDC platform's data fabric.

Why C is wrong:

SAP SuccessFactors is a source of data for BDC (often via the Data Catalog or integration flows), but it is not the platform providing the live analytics connection within the BDC technical stack. You would typically ingest SuccessFactors data into Datasphere first to harmonize it before viewing it live in SAC.

Why D is wrong:

SAP ERP (like ECC or S/4HANA) can be a source for SAP Datasphere. However, in the BDC model, SAC connects live to the harmonized layer (Datasphere) rather than directly to the legacy ERP for its primary BDC-driven analytics.

Official References:

SAP Help Portal: Live Data Connections to SAP Datasphere

SAP Community: Real-time steering with LIVE planning in SAP BDC

Explanation

In SAP Datasphere, local tables with custom names are created as persistent, physical storage objects in the Data Builder, allowing you to specify business and technical names during setup.

​ Why B is correct:

Importing a CSV file directly creates a new local table where you define a custom business name (display-friendly) and technical name (unique ID, editable only at creation), automatically inferring columns from the file structure before deploying.

​ ​Why D is correct:

Adding an output from a data flow (transformation, replication, or integration flow) materializes results into a new local table with a custom name you specify, capturing processed data persistently for modeling or consumption.

​ ​Why others are wrong:

A is wrong:
Intelligent lookups create virtual lookup views/joins for enrichment, not physical local tables with custom names—they reference existing data without storage.

​ C is wrong:
Persistent snapshots create time-stamped views (read-only copies of query results), not editable local tables; snapshots are for auditing/historical views, not custom-named tables.

​ Official References:

SAP Help: Creating a Local Table - https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/2509fe4d86aa472b9858164b55b38077.html

Explanation:

The Foundation Services of SAP Business Data Cloud play a crucial role in preparing raw data for use by transforming and enriching it into a valuable data product. This process involves cleaning, processing, and integrating data from various sources to create a unified and usable dataset.

Why Correct Answer is Right:

The Foundation Services provide essential capabilities for data transformation, enrichment, and integration, making it possible to generate a data product that can be used for various purposes such as analytics, reporting, or machine learning.

This involves:

Data transformation: converting data into a suitable format
Data enrichment: adding value to the data
Data integration: combining data from multiple sources

Why Others are Wrong:

A. Execution of machine learning algorithms:
This option is incorrect because machine learning algorithms are typically executed in advanced analytics or data science environments, leveraging the prepared data products. Foundation Services focus on preparing the data, not applying advanced analytics.

B. Generation of an analytic model:
This option is incorrect because generating an analytic model involves adding semantic information and is more related to data modeling or analytics tools, not the primary function of Foundation Services.

D. Storage of raw data inside a CDS view:
This option is incorrect because CDS (Core Data Services) views are used for data modeling, exposure, and consumption, not for storing raw data. Raw data storage is typically handled by data lakes or other storage solutions.

Official References:

For more information, check the SAP Business Data Cloud documentation in the SAP Help Portal:
https://help.sap.com/docs/SAP_BUSINESS_DATA_CLOUD

Explanation:

✅ B. Integrate data from different sources into one table - CORRECT
SAP Datasphere Data Flows are graphical ETL tools designed to extract data from multiple sources (SAP and non-SAP), apply transformations (filtering, joins, aggregations, calculated columns), and load the combined result into a single target table. This is a fundamental and primary Data Flow capability.

✅ D. Fill different target tables in parallel - CORRECT
Data Flows support parallel processing through task chains, allowing you to populate multiple target tables simultaneously. This improves performance and enables efficient orchestration of complex data loading scenarios.

✅ E. Use a Python script for data transformation - CORRECT
Data Flows include a Script Operator that integrates Python libraries like Pandas and NumPy, enabling custom data transformations, cleansing, and complex manipulation tasks beyond standard operators. Note: Use Python judiciously as it can impact performance with large data volumes.

❌ A. Write data to a table in a different SAP Datasphere tenant - INCORRECT
Data Flows operate within a single tenant/space. Cross-tenant data writing is not supported due to security and governance requirements. Each tenant is isolated, and direct writes to other tenants are architecturally prevented.

❌ C. Delete records from a target table - INCORRECT
While Data Flows technically support a DELETE load mode with filter criteria, this is not a primary or standard capability. Deletion requires special configurations and is achieved indirectly. SAP Datasphere has separate dedicated features for managing deletions, indicating this is not a core Data Flow use case.

Official References:

SAP Help Portal - Creating a Data Flow:
https://help.sap.com/docs/SAP_DATASPHERE/c8a54ee704e94e15926551293243fd1d/e30fd1417e954577baae3246ea470c3f.html

SAP Community - Script Operator:
https://community.sap.com/t5/technology-blog-posts-by-members/sap-datasphere-data-flow-series-script-operator-part-1/ba-p/13573999

SAP Learning Hub - Flows and Task Chains:
https://learning.sap.com/courses/exploring-sap-datasphere/creating-flows-and-task-chains-in-the-data-builder