C_SAC_2402 Exam Dumps - SAP Certified Associate - Data Analyst - SAP Analytics Cloud

A blank planning version in SAP Analytics Cloud can be created within the Version Management feature. This area of the platform allows users to manage different versions of their data, such as budgets, forecasts, and what-if scenarios. Creating a blank version provides a clean slate for planning activities, without pre-existing data, enabling users to start fresh with their assumptions and inputs.

References:

• SAP Analytics Cloud Help Documentation: Version Management in Planning
• SAP Analytics Cloud User Guide: Creating New Versions for Planning

SAP Analytics Cloud supports two primary types of data connections: Live Data Connection and Import Data Connection. Live Data Connection establishes a direct link to the data source, allowing real-time data access without replicating the data into SAP Analytics Cloud. This is ideal for scenarios where up-to-the-minute data is crucial, and data volume is large. On the other hand, Import Data Connection involves copying data from the source into SAP Analytics Cloud, which is suitable for scenarios where data doesn't change frequently, or there's a need for data transformation and enrichment within SAP Analytics Cloud.

References:

• SAP Analytics Cloud Help Documentation: Data Connections Overview
• SAP Analytics Cloud User Guide: Live Data vs. Import Data Scenarios

In SAP Analytics Cloud, when creating scripts for advanced data actions, a virtual variable member is designated by the "#" character. Virtual variable members are used within the scripting environment to represent temporary placeholders or variables that can hold values or results during the execution of the script. They are essential for dynamic calculations, conditional logic, and storing interim results that are not part of the permanent data model.

References:

• SAP Analytics Cloud Help Documentation: Advanced Data Actions and Scripting
• SAP Analytics Cloud User Guide: Scripting and Variables

Version management in SAP Analytics Cloud stories requires a Planning model. This feature enables users to create and manage different versions of their data, such as budget, forecast, and actual versions. Planning models provide the necessary structure and functionality for versioning, allowing for comparisons and what-if analyses within the story.

References:

• SAP Analytics Cloud Help Documentation: Version Management in Planning
• SAP Analytics Cloud User Guide: Using Planning Models for Versioning

SAP Analytics Cloud can establish a live connection with various source systems, including SAP Datasphere. This allows for real-time data access and analysis without the need to replicate data into the cloud, which is beneficial for scenarios where data privacy and security are paramount.

References:

• SAP Analytics Cloud Connection Guide1
• SAC Live and Import Connection Overview2
• SAP Analytics Cloud: Expand Live Data Source Options3
• Live connection in SAP Analytics Cloud: advantages and challenges4
• Explaining Where the Data Comes From - SAP Learning5

The Optimized Design Experience in SAP Analytics Cloud features several enhancements to facilitate story creation and exploration, including the Undo button, Grid pages, and the Explorer mode. The Undo button provides the ability to revert actions, enhancing the editing process. Grid pages allow for precise layout control of story elements. Explorer mode offers an interactive way to discover insights within the data, using dynamic visualizations and natural language queries.

References:

• SAP Analytics Cloud Help Documentation: Optimized Design Experience Features
• SAP Analytics Cloud User Guide: Exploring Data with Optimized Design Experience

When creating a styling rule for a table in SAP Analytics Cloud, the context refers to "The most granular level in the table." This means that the styling rule will be applied to the lowest level of data detail available in the table, such as individual cells or rows, allowing for precise control over the appearance of specific data points or categories within the table.

References:

• SAP Analytics Cloud Help Documentation: Styling Tables
• SAP Analytics Cloud User Guide: Applying Styling Rules to Table Data

Data Analyzer in SAP Analytics Cloud supports a variety of features to enhance data analysis, including Charts, Conditional Formatting, and Input Controls. Charts allow for graphical representation of data, making it easier to identify trends and outliers. Conditional Formatting highlights data points based on certain conditions, helping to draw attention to critical insights. Input Controls provide a way to dynamically filter and manipulate data displayed in the analysis, offering an interactive way to explore data sets.

References:

• SAP Analytics Cloud Help Documentation: Data Analyzer Features
• SAP Analytics Cloud User Guide: Using Charts, Conditional Formatting, and Input Controls in Data Analyzer

In an analytic data model within SAP Analytics Cloud, the automatically created dimension type that you can delete is the Generic dimension. This type of dimension is typically used for custom or ad-hoc categorizations and, unlike system-generated dimensions like Date or Version, can be modified or removed as per the specific needs of your data model and analysis requirements.

References:

• SAP Analytics Cloud Help Documentation: Data Model Dimensions
• SAP Analytics Cloud User Guide: Managing Dimensions in Analytic Models

Input controls in a story in SAP Analytics Cloud can be used for changing dimensions or measures displayed in a table and for filtering data on a page. Input controls provide a way for users to interactively modify the view of the data, such as selecting which dimensions or measures to display, or filtering the entire page's content based on selected criteria, enhancing the interactivity and flexibility of data analysis within stories.

References:

• SAP Analytics Cloud Help Documentation: Input Controls in Stories
• SAP Analytics Cloud User Guide: Using Input Controls for Data Interaction

The Just Ask feature in SAP Analytics Cloud utilizes natural language processing (NLP) to interpret user queries formulated in natural language. This feature allows users to ask questions about their data in a conversational manner, and the system intelligently interprets and responds with relevant visualizations and insights, making data analysis more accessible and intuitive.

References:

• SAP Analytics Cloud Help Documentation: Just Ask Feature and NLP
• SAP Analytics Cloud User Guide: Leveraging Natural Language Processing

Connections and data preparation

When using a live connection in SAP Analytics Cloud, the data remains stored in the source system. This means that no data is imported or replicated into SAP Analytics Cloud; instead, it is accessed and analyzed in real-time directly from the source system. This approach ensures that the most current data is always used for analysis and that data governance and security policies of the source system remain in control.

References:

• Live Data Connections to SAP S/4HANA | SAP Help Portal1
• SAP Analytics Cloud Connection Guide2
• SAP Analytics Cloud Data Connections - InsightCubes

In the context of SAP Analytics Cloud, when using a live connection to connect to a data source, the data remains stored in the source system. This setup means that SAP Analytics Cloud directly queries the data in its original location, without importing or copying it into the SAP Analytics Cloud environment. This approach is advantageous for several reasons, including maintaining a single source of truth, reducing data redundancy, and ensuring data is always up-to-date without the need for synchronization processes. Live connections are particularly useful for real-time or near-real-time data analysis and reporting, providing insights based on the most current data available without the overhead of data replication.

References:

• SAP Analytics Cloud documentation and user guides typically emphasize the benefits and use cases of live connections, highlighting how they maintain data in the source system to ensure real-time data access and analysis.
• SAP training materials for Data Analysts using SAP Analytics Cloud, including study guides and official certification resources, explain the technical and practical aspects of live connections, including where data is stored and how it is accessed.
• Best practice guides for SAP Analytics Cloud, often available through the SAP Community or SAP Knowledge Base, provide insights and recommendations on setting up and using live connections, reinforcing the concept that data stays in the source system.

When entering values for several expense accounts in a data table in SAP Analytics Cloud, the "Mass" data entry mode should be used to process the data with a delay as defined in the System Administration settings. This mode is designed for bulk data entry, allowing users to input multiple values across different accounts or dimensions before the system processes these entries. This can be particularly useful in scenarios where a large volume of data needs to be entered and processed efficiently, and a delay is preferable to ensure data integrity and accuracy.

References:

• SAP Analytics Cloud Help Documentation: Data Entry and Data Action Processing
• SAP Analytics Cloud User Guide: Mass Data Entry and Processing

When using an input control to filter data in a SAP Analytics Cloud story, the filter is applied at the Page level. This means that the selected filter criteria will affect all the components (tables, charts, etc.) on that particular page, allowing for a cohesive and consistent view of the data filtered according to the chosen parameters.

References:

• SAP Analytics Cloud Help Documentation: Filtering Data with Input Controls
• SAP Analytics Cloud User Guide: Page-Level Filtering in Stories

In a live model within SAP Analytics Cloud, dimensions can be organized into logical categories using either level-based hierarchies or parent-child hierarchies. Level-based hierarchies are used when the relationships between items are defined by distinct levels, such as Geography might be divided into Country, State, and City levels. Parent-child hierarchies, on the other hand, are useful when the data's hierarchy is not strictly defined by levels but by a parent relationship where a child member is associated with a parent member, which is common in organizational structures or product categories.

References:

• SAP Analytics Cloud Help Documentation: Creating and Managing Hierarchies
• SAP Analytics Cloud User Guide: Working with Models