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In SAP Business Data Cloud, what is the function of the Connection Management feature?
Please choose the correct answer.
Response:
A. Control dashboard visualization access
B. Establish data flows between widgets
C. Define connectivity to source systems
D. Create backup jobs for storage
Answer: C
NEW QUESTION # 21
What features are supported by the SAP Analytics Cloud data analyzer? Note: There are 3 correct answers to this question.
A. Input controls
B. Linked dimensions
C. Charts
D. Calculated measures
E. Conditional formatting
Answer: A,D,E
Explanation:
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.
NEW QUESTION # 22
What is the first action Allen (Business Analyst) takes when Natalie (Chief Finance Officer) reaches out to him for insights on working capital?
Please choose the correct answer.
Response:
A. He develops a custom ML model.
B. He explores the available Intelligent Application in SAP Business Data Cloud Catalog.
C. He shares the Data Product with SAP Databricks.
D. He reviews the details and installs the Intelligent Application.
Answer: B
NEW QUESTION # 23
Which of the following can you do with an SAP Datasphere Data Flow? Note: There are 3 correct answers to this question.
A. Integrate data from different sources into one table.
B. Delete records from a target table.
C. Use a Python script for data transformation.
D. Fill different target tables in parallel.
E. Write data to a table in a different SAP Datasphere tenant.
Answer: A,C,D
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.
NEW QUESTION # 24
What are some use cases for an SAP Datasphere task chain? Note: There are 3 correct answers to this question.
A. Execute a data action for a planning function
B. Create or Refresh View Persistency
C. Execute a Replication Flow and Transformation Flow in sequence
D. Upload a CSV file into a local table
E. Run an Open SQL Schema Procedure
Answer: B,C,E
Explanation:
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.
NEW QUESTION # 25
......
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