SAP C_BCBDC_2505 Online Practice Questions and Exam Preparation

SAP C_BCBDC_2505 Online Questions & Answers

• Question 1:

  Which of the following can you do with an SAP Datasphere Data Flow? Note: There are 3 correct answers to this question.

  A. Write data to a table in a different SAP Datasphere tenant.
  B. Integrate data from different sources into one table.
  C. Delete records from a target table.
  D. Fill different target tables in parallel.
  E. Use a Python script for data transformation.
  B. Integrate data from different sources into one table.
  D. Fill different target tables in parallel.
  E. Use a Python script for data transformation.

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  explanation:

  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 2:

  You want to combine external data with internal data via product ID. Although the data may be inconsistent, such as the external data contains the letter "O" where the internal data contains the digit 0, you still want to combine them. Which artifact should you use for matching?

  A. Analytic Model
  B. Entity Relationship Model
  C. Graphical View
  D. Intelligent Lookup
  D. Intelligent Lookup

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  explanation:

  Explanation

  When faced with the challenge of combining data from different sources where the matching keys (like "Product ID") are inconsistent or contain variations (e.g., "O" vs. "0"), the recommended artifact in SAP Datasphere for such fuzzy or approximate matching scenarios is an Intelligent Lookup. An Intelligent Lookup

  (D) leverages machine learning capabilities to identify and map records that are semantically similar but not exact matches. Unlike standard joins in graphical views or SQL views which require precise key matches, Intelligent Lookups can handle data quality issues, typos, and variations, allowing you to successfully link disparate records that would otherwise be missed. This is particularly valuable when integrating data from external systems or legacy sources where perfect data standardization is not feasible, ensuring a more comprehensive and accurate combined dataset for analysis.

• Question 3:

  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?

  A. Refresh the story
  B. Allow model import
  C. Refresh the data source
  D. Schedule the import
  D. Schedule the import

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  explanation:

  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 4:

  Why would you choose the "Validate Remote Tables" feature in the SAP Datasphere repository explorer?

  A. To test if data has been replicated completely
  B. To detect if remote tables are defined that are not used in Views
  C. To preview data of remote tables
  D. To identify structure updates of the remote sources
  D. To identify structure updates of the remote sources

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  explanation:

  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 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:

  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.

  A. Automated data provisioning from business application to dashboard
  B. Services for transforming and enriching data
  C. Manual creation of artifacts across all involved components
  D. AI-based suggestions for intelligent applications in the SAP Business Data Cloud Cockpit
  A. Automated data provisioning from business application to dashboard
  B. Services for transforming and enriching data

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  explanation:

  Explanation

  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:

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

  A. Create an Analytic Model based on the first artifact and add the second artifact as the Used in property.
  B. Create a graphical view and select the Join node icon.
  C. Create an SQL view with a JOIN operation.
  D. Create a graphical view, drag an artifact to the canvas, and the second one on top of the first one.
  B. Create a graphical view and select the Join node icon.
  C. Create an SQL view with a JOIN operation.

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  explanation:

  Explanation

  In SAP Datasphere, there are distinct methods for joining two existing artifacts, enabling powerful data integration. One common and intuitive approach is to create a graphical view and select the Join node icon (B). In the graphical view editor, you can drag your source artifacts (tables or views) onto the canvas, and then use the dedicated "Join" node to define the join conditions between them, visually mapping the columns and selecting the join type (e.g., inner, left outer). This method is highly visual and user-friendly. Another powerful and flexible way to join artifacts is to create an SQL view with a JOIN operation (C). For users comfortable with SQL, an SQL view allows for direct coding of complex join conditions, including various join types and advanced filtering, offering fine-grained control over the data integration logic. Both methods achieve the goal of combining data from multiple artifacts into a single result set, tailored to different user preferences and complexity requirements.

• Question 7:

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

  A. Organization
  B. Version
  C. Entity
  D. Date
  B. Version
  D. Date

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  explanation:

  Explanation

  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 8:

  How can you create a local table with a custom name in SAP Datasphere? Note: There are 2 correct answers to this question.

  A. By creating an intelligent lookup
  B. By importing a CSV file
  C. By creating a persistent snapshot of a view
  D. By adding an output of a data flow
  B. By importing a CSV file
  D. By adding an output of a data flow

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  explanation:

  Explanation

  In SAP Datasphere, there are several ways to create a local table with a custom name, providing flexibility for data management. Two common methods are by importing a CSV file and by adding an output of a data flow. When you import a CSV file, Datasphere allows you to specify a custom name for the new local table that will store the imported data. This is a quick and straightforward way to bring external, flat-file data into Datasphere. Secondly, a data flow in Datasphere allows you to define a sequence of operations (e.g., transformations, aggregations) and write the processed data to a target. When configuring the output of a data flow, you can specify a new local table and provide it with a custom name. This method is ideal for creating structured tables as a result of complex data integration or transformation processes. These options ensure that users can create and name tables according to their specific data modeling and organizational requirements.

• Question 9:

  Which steps are executed when an SAP Business Data Cloud Intelligent Application is installed? Note: There are 2 correct answers to this question.

  A. Connection of SAP Datasphere with SAP Analytics Cloud
  B. Creation of a dashboard for visualization
  C. Execution of a machine-learning algorithm
  D. Replication of data from the business applications to Foundation Services
  A. Connection of SAP Datasphere with SAP Analytics Cloud
  D. Replication of data from the business applications to Foundation Services

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  explanation:

  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 10:

  What is the main storage type of the object store in SAP Business Data Cloud?

  A. SAP HANA extended tables
  B. SAP BW/4HANA DataStore objects (advanced)
  C. SAP HANA data lake files
  D. SAP BW/4HANA InfoObjects
  C. SAP HANA data lake files

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  explanation:

  Explanation

  The primary storage type for the object store within the SAP Business Data Cloud (BDC) architecture is SAP HANA data lake files. SAP BDC is designed to handle vast amounts of diverse data, including semi-structured and unstructured data, which is efficiently stored in a data lake. The SAP HANA data lake, specifically its file storage component, provides a highly scalable and cost-effective solution for retaining raw, historical, and detailed data. This contrasts with traditional relational databases (like SAP HANA extended tables) or data warehousing constructs (like BW/4HANA DataStore objects or InfoObjects), which are optimized for structured, aggregated data and specific query patterns. The object store's reliance on data lake files in BDC underscores its capability to manage enterprise-wide data regardless of its structure, making it suitable for a wide range of analytical workloads, including those involving machine learning and advanced analytics where raw data access is crucial.