C_SAC_2415 Sample Questions Answers

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

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.

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

For datasets that extract data from external systems like SAP Business Warehouse (SAP BW), SAP Analytics Cloud provides the capability to schedule updates on a regular basis. This feature ensures that the dataset within SAP Analytics Cloud remains up-to-date with the latest changes from the SAP BW system, without the need for manual reimport or creating a new dataset. Scheduled updates can be configured to run at specific intervals, automating the data refresh process.

References:

SAP Analytics Cloud Help Documentation: Scheduling Data Refresh

SAP Analytics Cloud User Guide: Automating Dataset Updates from External Sources

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

For designing a new story in SAP Analytics Cloud where the size and position of widgets need to adjust dynamically for different devices and screen sizes, the Responsive pagetype should be used. Responsive pages automatically adapt to the screen size of the device they are viewed on, providing an optimal viewing experience across desktops, tablets, and smartphones.

References:

SAP Analytics Cloud Help Documentation: Responsive Design in Stories

SAP Analytics Cloud User Guide: Building Responsive Pages

In SAP Analytics Cloud, a Time Series Forecast predictive scenario can write back to a planning model. Time Series Forecasting leverages historical data to predict future values over a specified time horizon, using statistical or machine learning methods. This feature is particularly useful in planning and forecasting processes, where future values are predicted based on past trends and seasonality. The ability to write these forecasts back into a planning model allows for the integration of predictive insights into the planning process, enhancing decision-making and strategic planning.

References:

SAP Analytics Cloud Help Documentation: Predictive Scenarios and Planning

SAP Analytics Cloud User Guide: Time Series Forecasting in Planning Models

In SAP Analytics Cloud, a data model must contain at least "Dimensions" and "Measures" to be functional. Dimensions are the qualitative aspects of the data (e.g., time, geography, product categories) that provide the context for analysis. Measures, on the other hand, are the quantitative data points (e.g., sales, costs, quantities) that are analyzed within the context provided by dimensions. Both are fundamental components of a data model, enabling structured data analysis and reporting.

References:

SAP Analytics Cloud Help Documentation: Building Data Models

SAP Analytics Cloud User Guide: Understanding Dimensions and Measures in Models

Collapsing the hierarchy in a story can help limit the refresh time, as it reduces the amount of data that needs to be processed and displayed at any given time. By presenting data at a higher aggregation level initially, you can improve performance and allow users to expand specific sections of the hierarchy as needed for more detailed analysis.

References:

SAP Analytics Cloud Help Documentation: Improving Story Performance

SAP Analytics Cloud User Guide: Managing Data Hierarchy for Performance

To apply a styling rule only to countries in a table with a hierarchy of Country, Region, and City in SAP Analytics Cloud, you must use the "Self" level option. This option ensures that the styling rule is applied specifically to the level of the hierarchy representing countries, without affecting the styling of regions, cities, or any other hierarchical levels.

References:

SAP Analytics Cloud Help Documentation: Styling Hierarchical Tables

SAP Analytics Cloud User Guide: Hierarchical Styling Options

To combine two dimensions, Firstname and Lastname, into a single dimension displaying as Lastname, Firstname in a live data model, you should create a calculated dimension in the data model. This calculated dimension will concatenate the two fields into one, according to the specified format. This approach allows for the creation of a new dimension that can be used across various reports and analyses within the model, maintaining the integrity of the original dimensions.

References:

SAP Analytics Cloud Help Documentation: Creating Calculated Dimensions

SAP Analytics Cloud User Guide: Data Modeling Best Practices

If SAP Analytics Cloud interprets a 'Year' column as a measure instead of a dimension, it should be changed to a dimension to ensure it is treated as a calendar year. This adjustment can be made within the model or dataset settings, where the column's role can be switched from a measure (quantitative value) to a dimension (qualitative value). Treating 'Year' as a dimension allows it to be used appropriately in time-based analyses, such as trends over time, without being aggregated like a numerical measure.