SAP Certified Associate - SAP Business Data Cloud C_BCBDC_2505 Exam Practice Test

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Total 30 questions
Question 1

What are some use cases for an SAP Datasphere task chain? Note: There are 3 correct answers to this question.



Answer : A, C, D

SAP Datasphere task chains are powerful tools for orchestrating and automating sequences of operations, making them ideal for managing complex data pipelines and recurring processes. One key use case is to Create or Refresh View Persistency (A). If you have views for which you want to persist the data (materialize them into tables) for performance or specific analytical needs, a task chain can automate the scheduled recreation or refresh of these persistent views. Another common use case is to Execute a Replication Flow and Transformation Flow in sequence (C). This allows you to define a process where data is first replicated from a source system into Datasphere, and then immediately followed by transformation steps to cleanse, enrich, or aggregate that data, ensuring a fully automated end-to-end data preparation. Furthermore, task chains can be used to Run an Open SQL Schema Procedure (D). This provides flexibility to integrate custom SQL logic or stored procedures into an automated workflow, enabling advanced data manipulation or administrative tasks. Uploading a CSV file (B) is typically a manual import action, and executing a data action for a planning function (E) relates to planning models, not general Datasphere task chains.


Question 2

How can you join two existing artifacts in SAP Datasphere? Note: There are 2 correct answers to this question.



Answer : C, D

C . Create an SQL view with a JOIN operation

SQL views in Datasphere allow you to write SQL code directly.

You can use JOIN in your SQL script to combine multiple artifacts (tables/views).

SELECT a.CustomerID, b.SalesAmount

FROM Customers a

JOIN Sales b ON a.CustomerID = b.CustomerID;

D . Create a graphical view, drag an artifact to the canvas, and the second one on top of the first one

In the Datasphere graphical modeler, when you drag the second artifact onto the first one, the system automatically creates a Join node.

You can then define the join type (Inner, Left Outer, Right Outer, Full).

This is the drag-and-drop method for joins.


Question 3

In an SAP Analytics Cloud planning data model, which dimensions are included by default? Note: There are 2 correct answers to this question.



Answer : B, D

When creating a planning data model in SAP Analytics Cloud (SAC), certain dimensions are included by default to facilitate common planning scenarios. The two key dimensions automatically present are Version and Date. The Version dimension is crucial for distinguishing between different planning scenarios, such as 'Actual,' 'Budget,' 'Forecast,' or 'Plan 2025,' allowing users to compare and manage various iterations of their planning data. The Date dimension, on the other hand, is essential for time-based planning and analysis, enabling data entry, aggregation, and reporting across different time granularities like years, quarters, months, or days. These default dimensions provide a robust framework for financial and operational planning, serving as foundational elements around which planning activities are structured, and ensuring consistency and comparability across different planning versions and time periods.


Question 4

Which of the following SAP Datasphere objects can you create in the Data Builder? Note: There are 3 correct answers to this question.



Answer : A, D, E

The Data Builder in SAP Datasphere is the primary environment for data modeling and transformation activities. Within the Data Builder, users can create a variety of essential objects to build their data landscape. Among the options provided, you can create Intelligent Lookups (A), which are used for fuzzy matching and data cleansing operations to link disparate data sets. You can also create Task Chains (D), which are crucial for orchestrating and automating sequences of data integration and transformation processes, ensuring data pipelines run efficiently. Furthermore, Replication Flows (E) are designed and managed within the Data Builder, allowing you to configure and execute continuous or scheduled data replication from source systems into Datasphere. 'Spaces' (B) and 'Connections' (C) are typically managed at a higher administrative level within the SAP Datasphere tenant (e.g., in the System or Connection Management areas), not directly within the Data Builder itself, which focuses on data content and logic.


Question 5

What are some features of the out-of-the-box reporting with intelligent applications in SAP Business Data Cloud? Note: There are 2 correct answers to this question.



Answer : A, B

The out-of-the-box reporting capabilities with intelligent applications in SAP Business Data Cloud (BDC) are designed to streamline the analytical process and deliver immediate value. Two significant features include automated data provisioning from business application to dashboard. This means that intelligent applications handle the end-to-end flow of data, from its source in operational systems, through processing in BDC, and finally to visualization in dashboards, with minimal manual intervention. This automation ensures timely and consistent data delivery for reporting. Additionally, these intelligent applications leverage services for transforming and enriching data. As part of the pre-built logic within these applications, data is automatically transformed (e.g., aggregated, filtered) and enriched (e.g., adding calculated KPIs, combining with master data) to make it immediately suitable for reporting and analysis. This reduces the need for manual data manipulation by users, providing ready-to-consume insights.


Question 6

What features are supported by the SAP Analytics Cloud data analyzer? Note: There are 3 correct answers to this question.



Answer : A, B, C

The SAP Analytics Cloud Data Analyzer is designed for ad-hoc data exploration and analysis, providing a focused environment for users to quickly derive insights. Among its key supported features are calculated measures, which allow users to create new metrics on the fly based on existing data, enabling deeper analysis without modifying the underlying model. Input controls are also supported, providing interactive filtering capabilities that allow users to dynamically adjust the data displayed based on specific criteria, enhancing the flexibility of their analysis. Furthermore, conditional formatting is a valuable feature that enables users to apply visual styling (e.g., colors, icons) to data points based on defined rules, making it easier to identify trends, outliers, or specific conditions at a glance. While charts and linked dimensions are integral to full stories, the Data Analyzer's strength lies in its immediate, flexible analytical capabilities for a single data source.


Question 7

Which operation is implemented by the Foundation Services of SAP Business Data Cloud?



Answer : C

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.


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