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Free SAP C_BCBDC_2505 Exam Braindumps (page: 2)

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PDF with 30 Questions

Which programming language is used for scripting in an SAP Analytics Cloud story?

  1. Wrangling Expression Language
  2. ABAP
  3. Python
  4. JavaScript

Answer(s): D

Explanation:

JavaScript is the programming language utilized for scripting within an SAP Analytics Cloud (SAC) story.
While SAC offers various functionalities through its intuitive user interface, scripting with JavaScript provides advanced capabilities for customizing the behavior and interactivity of a story. This allows developers and power users to create highly tailored analytical applications and dashboards that go beyond standard features. For instance, JavaScript can be used to dynamically change chart properties, implement complex filtering logic, trigger data actions, or integrate with external services. Unlike analytic applications, which typically offer more extensive scripting options, storytelling in SAC focuses on enabling business users to create interactive reports with a degree of customization through embedded scripts. The scripts are executed by the web browser, leveraging its built-in JavaScript execution engine, ensuring a flexible and widely understood development environment for enhancing story functionality.



Which SAP Analytics Cloud feature uses natural language processing?

  1. Smart insight
  2. Just Ask feature
  3. Data analyzer
  4. Digital boardroom

Answer(s): B

Explanation:

The "Just Ask" feature in SAP Analytics Cloud (SAC) is a prime example of its integration with natural language processing (NLP). This innovative AI-powered capability allows users to interact with their data by simply typing questions in plain, everyday language, rather than needing to navigate complex menus or understand underlying data structures. For instance, a user might type "Show me sales by region for the last quarter," and "Just Ask" will interpret this query, identify relevant dimensions and measures, and automatically generate an appropriate visualization or insight. This significantly democratizes data analysis, making it accessible to a wider audience, including business users who may not have extensive technical skills. By leveraging NLP, "Just Ask" bridges the gap between human language and data queries, transforming how users discover and consume insights within SAC, ultimately accelerating decision-making.



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

  1. Calculated measures
  2. Input controls
  3. Conditional formatting
  4. Charts
  5. Linked dimensions

Answer(s): A,B,C

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.



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?

  1. Refresh the story
  2. Allow model import
  3. Refresh the data source
  4. Schedule the import

Answer(s): D

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.



For a model in SAP Analytics Cloud you are using a live connection.
Where is the data stored?

  1. Public dataset
  2. SAP Analytics Cloud model
  3. Source system
  4. Embedded dataset

Answer(s): C

Explanation:

When an SAP Analytics Cloud (SAC) model utilizes a live connection, the data is not stored within SAP Analytics Cloud itself. Instead, the data resides entirely in the source system. This means that SAC directly queries the data from the connected system (e.g., SAP HANA, SAP BW, SAP S/4HANA, or SAP Datasphere) in real-time every time a user interacts with the story or application. Only metadata, such as dimension definitions and measure aggregations, is stored in SAC. This approach offers several significant advantages: it ensures that users always work with the most current data, eliminates the need for data replication, and often addresses data privacy and security concerns by keeping sensitive data within the customer's secure landscape. The "live" nature means that any changes in the source system are immediately reflected in SAC.



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