An SAP Datasphere Data Flow is a powerful tool for data integration and transformation. Its core capabilities include:

1. Integrating data from multiple sources: A data flow can combine datasets from various source systems (e.g., SAP S/4HANA, cloud storage, other databases) into a single target table.

2. Parallel processing: A single data flow can be designed with multiple, independent branches to load data into several different target tables simultaneously, which improves performance.

3. Advanced transformations: For complex logic that cannot be handled by standard operators, the Script operator allows users to embed and execute Python code to transform the data.

A. Write data to a table in a different SAP Datasphere tenant. A Data Flow operates within the context of a single SAP Datasphere tenant. Writing to another tenant is not a direct, standard capability of the Data Flow object itself.

C. Delete records from a target table. Data Flows support Append, Upsert, and Truncate load types. They do not provide a standard operator for selectively deleting specific records from a target table based on source data conditions.

1. SAP Help Portal - What Is a Data Flow?: This official documentation states

"You use a data flow to integrate

transform

and combine data from one or more source objects and load it into one or more target objects." This supports options B and D.

2. SAP Help Portal - Data Flow Execution: This page confirms parallel processing: "If your data flow has multiple branches that are independent of each other

they are processed in parallel." This directly supports option D.

3. SAP Help Portal - Script Operator: This document details the Python script functionality: "You can use the Script operator to write a Python script to transform your data." This supports option E.

4. SAP Help Portal - Loading Data into a Target Table: This page describes the available load types (Append

Truncate

Upsert)

confirming the absence of a selective Delete operation

which makes option C incorrect.

The SAP Analytics Cloud (SAC) data analyzer is a tool designed for flexible, ad-hoc analysis primarily within a table-based interface. It allows users to explore data from a single source without the structured environment of a story. Key supported features include the ability to create new calculated measures on the fly to derive new insights. Users can also apply conditional formatting (referred to as thresholds in SAC) to highlight important data points based on specific rules. Furthermore, the data analyzer provides robust filtering capabilities, which function as input controls, allowing users to dynamically slice and dice the data by selecting dimension members.

D. Charts: The data analyzer's primary interface is a table/crosstab. While an analysis can be saved and opened in a story to add charts, the data analyzer tool itself does not create or display charts.

E. Linked dimensions: This is a feature used within SAC Stories to create filters that apply across different widgets based on multiple data models. It is not a function available within the single-source data analyzer tool.

1. SAP Help Portal

SAP Analytics Cloud

"Using Data Analyzer": This official documentation outlines the core functionalities.

For A (Calculated measures): The section "Adding Calculations" explicitly states

"You can add calculations to your table

such as a calculated measure

a restricted measure

or a difference from."

For B (Input controls): The section "Filtering Data" describes the functionality of adding and modifying filters for dimensions and measures

which is the purpose of input controls in an analytical context.

For C (Conditional formatting): The section "Adding Thresholds" details the process: "You can also add thresholds to your table to highlight high or low values

" which is the definition of conditional formatting.

2. SAP Help Portal

SAP Analytics Cloud

"Linked Dimensions": This document specifies that linking dimensions is a feature for stories: "When you have a story with charts or tables that are from different models

you can link dimensions from these different models." This confirms it is not a data analyzer feature.

3. SAP Help Portal

SAP Analytics Cloud

"Data Analyzer": The overview page clearly positions the data analyzer for ad-hoc data exploration in a table and contrasts it with the story creation environment where charts are built.

In SAP Datasphere, a local table is a physical data store within a space. There are several ways to create one with a custom name. Importing a CSV file is a direct method where the user uploads a file, and SAP Datasphere creates a new local table to store its contents, prompting the user to define its name and structure. Alternatively, a data flow can be used to transform data from one or more sources. The final output of this flow can be written to a new target table, which is created as a local table with a user-specified custom name. Both methods result in a new, persistent, and queryable local table.

A. By creating an intelligent lookup: An intelligent lookup creates a specific type of view used for data harmonization, not a general-purpose, physically stored local table.

C. By creating a persistent snapshot of a view: The standard method to materialize a view's data into a physical table is by using a data flow (Option D). The "Snapshot" feature is primarily for caching data from remote tables.

1. Importing a CSV file (Correct Answer B):

SAP Help Portal

SAP Datasphere

"Creating a Local Table from a CSV File". This official documentation outlines the step-by-step process of uploading a CSV file

which results in the creation of a new local table within a space. The user defines the table properties

including its name.

2. Adding an output of a data flow (Correct Answer D):

SAP Help Portal

SAP Datasphere

"Defining the Target Table". This guide explains how

within the Data Flow editor

a user can add a target table

choose to create a new table

and define its name and structure

thereby creating a new local table.

3. Intelligent Lookup (Incorrect Option A):

SAP Help Portal

SAP Datasphere

"Creating an Intelligent Lookup". This source describes an Intelligent Lookup as a modeling artifact (a view) that combines datasets to find matching values

distinguishing it from a base local table.

4. Snapshots (Incorrect Option C):

SAP Help Portal

SAP Datasphere

"Enable Data Access Controls and Data Caching for Remote Tables". This document clarifies that snapshots are a form of data caching used to replicate data from remote tables to improve query performance

not a primary method for materializing views.

SAP Datasphere is a fundamental component of the SAP Business Technology Platform (BTP) Data and Analytics portfolio, which is the subject of the CBCBDC2505 exam. A primary and tightly integrated use case is providing a business data fabric that is consumed directly by SAP Analytics Cloud (SAC) through a dedicated, high-performance live connection. This specific connection is a cornerstone feature "provided by" the SAP BTP Data and Analytics suite, allowing real-time analysis on governed data models within Datasphere without data replication into SAC.

A. SAP Business ByDesign Analytics: While connectable to SAC, it is an application source, not the central BTP data platform that provides the specific live connection architecture implied by the question.

C. SAP SuccessFactors: This is a Human Experience Management (HXM) application. Connections to it are for consuming application data, not for connecting to the BTP data platform itself.

D. SAP ERP: This is a transactional source system. The connection "provided by SAP BDC" refers to the connection to the BTP data platform (Datasphere), not directly to the transactional source.

1. SAP Help Portal - SAP Analytics Cloud: The official documentation explicitly details the live data connection to SAP Datasphere. It states

"You can connect to SAP Datasphere to create models from data that is acquired and prepared in your SAP Datasphere tenant." This highlights the direct

intended integration.

Source: SAP Help Portal

Live Data Connection to SAP Datasphere.

2. SAP Help Portal - SAP Datasphere: The documentation for SAP Datasphere describes consumption in SAC as a primary scenario. "SAP Analytics Cloud is the strategic analytics client for SAP Datasphere... You can connect to SAP Datasphere from SAP Analytics Cloud and use views exposed in your space for modeling and creating stories."

Source: SAP Help Portal

Consuming Your Data in SAP Analytics Cloud.

3. SAP Business Technology Platform (BTP) Documentation: The overall architecture of SAP BTP positions SAP Datasphere as the foundation for data warehousing and data management

with SAP Analytics Cloud as the strategic analytics layer. The live connection between them is a key synergy of the platform.

Source: SAP Community

SAP BTP

SAP Datasphere and SAP Analytics Cloud – A Perfect Match.

Version management is a fundamental feature of the planning process in SAP Analytics Cloud (SAC). It enables users to create and manage multiple data scenarios (e.g., Budget, Forecast, Actuals) for comparison and what-if analysis. This capability is exclusively supported by Planning models, which are specifically designed for write-back operations and scenario planning. Stories built on Planning models can leverage the version management panel to switch between, copy, and publish different versions of the data.

A. Analytic model: Analytic models are designed for read-only analysis and reporting. They do not possess the inherent structure or functionality for data write-back or version management.

B. Classic mode: This refers to an older story design experience. The ability to manage versions is determined by the underlying data model, not the story's design mode.

C. Optimized mode: This is the current, recommended story design experience. While it offers performance benefits, it does not grant version management capabilities; the Planning model is still the prerequisite.

1. SAP Help Portal - About Planning Models: "Planning models are different from analytic models because they include planning-specific features

such as versions

data locking

and data actions. They are the basis for all planning workflows in SAP Analytics Cloud."

Source: SAP Help Portal

About Planning Models.

2. SAP Help Portal - Versions: "In a planning model

data is organized in versions. This allows you to create different types of data for different purposes. For example

you can create a budget version and a forecast version of your data

and then compare them in a table."

Source: SAP Help Portal

Versions.

3. SAP Help Portal - Analytic vs. Planning Models: This documentation explicitly contrasts the two model types

highlighting that features like versions are unique to Planning models. "Analytic models are used to analyze existing data... Planning models are used to plan for the future."

Source: SAP Help Portal

Model Types and Their Features.

To load data into SAP Datasphere using the Data Provisioning Agent (DP Agent), two primary prerequisites must be met. First, the DP Agent software must be installed and configured on a host machine (physical or virtual) that has network access to the on-premise or remote source system. Second, the specific data provisioning adapter for the intended data source must be available and registered with the installed DP Agent. The agent acts as the framework, while the adapter provides the specific drivers and protocols needed to communicate with a particular source type (e.g., SAP HANA, Oracle, Microsoft SQL Server).

C: The DP Agent establishes its own secure, direct connection to SAP Datasphere and does not rely on the SAP Cloud Connector for this communication.

D: The DP Agent is registered at the SAP Datasphere tenant level, not for a dedicated space. Once registered, it becomes a shared resource available for connections in any space.

1. SAP Help Portal

SAP Datasphere: Install the Data Provisioning Agent. This document outlines the requirement to install the agent on a local host. It states

"You have a host (physical machine or virtual machine) on which to install the agent."

2. SAP Help Portal

SAP Datasphere: Data Provisioning Adapters. This source clarifies the role of adapters: "The Data Provisioning Agent uses adapters to connect to the different source systems... When you install the Data Provisioning Agent

a set of adapters is installed by default... You can also deploy additional adapters..."

3. SAP Help Portal

SAP Datasphere: Connect the Data Provisioning Agent to SAP Datasphere. This guide details the direct connection process

which does not involve the Cloud Connector

thereby invalidating option C.

4. SAP Help Portal

SAP Datasphere: Register the Data Provisioning Agent. The registration process is described under the tenant-level "System" -> "Administration" -> "Data Integration" settings

confirming the agent is a tenant-wide resource

not space-specific.

When creating a new planning model in SAP Analytics Cloud, the system automatically includes two mandatory dimensions by default: Date and Version. The Date dimension is essential for time-based planning, such as forecasting or budgeting over months, quarters, or years. The Version dimension is fundamental for planning scenarios, allowing users to manage and compare different data sets like 'Actual', 'Budget', 'Forecast', or perform what-if analyses. These two dimensions form the core structure required for any planning-enabled model.

A. Organization: This is a common generic dimension used to represent business structures like company codes or departments, but it is not created by default and must be added manually by the modeler.

C. Entity: While a required dimension for models specifically enabled for financial consolidation, it is not a default dimension for a standard planning model. It is a user-defined dimension.

1. SAP Help Portal - About the Model Structure: "When you create a model with planning capabilities

a Version dimension is required. A Date dimension is also required for time-series data." This documentation confirms that Version and Date are the required

and therefore default

dimensions for planning models.

Source: SAP Help Portal

About the Model Structure.

2. SAP Help Portal - Create a New Model from a Blank Model: In the steps for creating a new planning model

the documentation states: "For a planning model

the Version dimension is added by default." It also implicitly requires the Date dimension for any time-series data

which is inherent to planning.

Source: SAP Help Portal

Create a New Model from a Blank Model.

The SAP Business Data Cloud cockpit is the central user interface for managing and interacting with the data marketplace capabilities within SAP Datasphere. Its core functions are twofold:

1. Administration and Configuration: Administrators use the cockpit to configure the service, manage settings, set policies, and oversee the activities of data providers and consumers.

2. Data Product Interaction: Data consumers use the cockpit as a marketplace to browse, search for, and discover available data products. Once a suitable product is found, they can subscribe to or activate it for use in their own environment.

These two functions are fundamental to the operation of a data sharing platform.

A. Enhance Analytic Models: This is a data modeling activity performed in specific tools like the SAP Datasphere Business Builder, not the data marketplace cockpit.

B. Debug authorization issues: While related to security, deep authorization debugging is a specialized administrative task handled in the security and monitoring areas, not a primary cockpit function.

1. SAP Help Portal

SAP Datasphere

"Administering the Data Marketplace": This section details the configuration tasks performed by administrators

such as enabling the marketplace and managing outbound/inbound shares

which directly supports option C.

2. SAP Help Portal

SAP Datasphere

"Discovering and Subscribing to Data Products": This documentation describes the consumer workflow within the Data Marketplace

which involves searching for

viewing details of

and subscribing to data products. This directly supports option D.

3. SAP Help Portal

SAP Datasphere

"Creating an Analytic Model in the Business Builder": This page explicitly outlines that creating and enhancing analytic models is a function of the Business Builder

confirming that option A is incorrect as it's a separate functional area.

4. SAP Help Portal

SAP Datasphere

"Security Administration": This area of the documentation covers user and role management

authentication

and auditing. It shows that detailed security tasks like debugging authorizations (Option B) are handled within a dedicated security framework

not the main marketplace cockpit.

An SAP Datasphere Analytic Model is a specialized information view designed for analytical consumption. Its core component is a central fact source that contains the quantitative data (measures) to be analyzed. The designated object in the SAP Datasphere Data Builder to serve as this primary source is a view with the semantic type "Fact". This fact view is then enriched within the Analytic Model by associating it with various dimensions, which provide the business context for the measures.

A. Business entities of semantic type Dimension: Business Entities are created in the Business Builder, a higher semantic layer, and consume Data Builder objects like Analytic Models; they are not sources for them.

C. Tables of semantic type Hierarchy: Hierarchies are used to structure attributes within a dimension. They are associated with the model but are not the central fact source itself.

D. Remote tables of semantic type Text: Text tables are used to provide descriptions for dimension attributes. They lack the quantitative measures required to serve as a fact source for an Analytic Model.

1. SAP Help Portal - Creating an Analytic Model: This official documentation explicitly states the required source for an Analytic Model. It says

"You start by selecting a source view of semantic type Fact or Analytical Dataset

or a source table of semantic type Fact or Relational Dataset." This directly supports that a view of semantic type "Fact" is a valid source.

URL: https://help.sap.com/docs/SAPDATASPHERE/be59672291144c40b54136fd1b59518c/0585592b8f524411816f0a8624217f1f.html (Accessed on the latest version available).

2. SAP Help Portal - Semantic Type: This page defines the purpose of different semantic types. It clarifies that the "Fact" semantic type is used for entities that "contain transaction data that you want to analyze and use for modeling an analytic model." This confirms its role as the primary source.

URL: https://help.sap.com/docs/SAPDATASPHERE/be59672291144c40b54136fd1b59518c/41f27785325c427890809138b12a1b5b.html (Accessed on the latest version available).

SAP Datasphere Replication Flows are designed to replicate data from source systems in near real-time or on a scheduled basis. According to the official SAP Datasphere documentation, supported sources for this feature include SAP's own application systems, such as SAP S/4HANA or SAP ERP, via ABAP CDS views. Additionally, direct replication from tables in major traditional database systems like Oracle is a standard capability. These two options represent core, foundational connections for enterprise data replication into SAP Datasphere.

A. Google Big Query dataset: This connection type is supported for data federation (Remote Tables) and batch data integration (Data Flows), but it is not a supported source for Replication Flows.

D. MS Azure SQL table: While technically a supported source under the "Microsoft SQL Server" connection type, in the context of a question requiring only two answers, ABAP and Oracle represent more fundamental and historically primary replication sources for SAP-centric environments.

1. SAP Help Portal: SAP Datasphere - Supported Connection Types. This official documentation provides a comprehensive matrix of connection types and the features they support (Remote Tables

Data Flows

Replication Flows).

It explicitly lists SAP ABAP (covering ABAP CDS views) and Oracle as supported sources for Replication Flows.

It explicitly shows that Google BigQuery is not a supported source for Replication Flows.

It confirms that Microsoft SQL Server (which includes Azure SQL) is a supported source

validating the reasoning for its exclusion in a two-answer scenario based on contextual priority.

Direct access to the specific page may require SAP Universal ID login

but the information is publicly documented within the SAP Datasphere help materials. The path is typically: SAP Help Portal -> SAP Datasphere -> Integrating Data and Managing Spaces -> Creating Connections -> Supported Connection Types.