C_SAC_2501 SAP Certified Associate - Data Analyst - SAP Analytics Cloud Questions and Answers

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.

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.

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.

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.

In SAP Analytics Cloud, data concatenation can be performed on Embedded datasets, Standalone datasets, and Imported data models. This process involves combining data from multiple sources or tables into a single dataset or model, providing a unified view of the data. This is particularly useful for analyses that require a comprehensive dataset compiled from various data 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.

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.

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.

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.

Table formulas in SAP Analytics Cloud are used to perform cell-based calculations within a story. These formulas can be applied directly to table cells, allowing for customized calculations that are specific to the data presented in the table. This feature is particularly useful for ad-hoc analysis and when specific calculations are needed that are not covered by predefined measures or dimensions.

To update the data in a model based on an import connection, two main approaches can be used:

Refresh the story: This action forces SAP Analytics Cloud to reload the data for the visualizations in a story, pulling in the most recent data available in the model. This is a manual process initiated by the user.

Schedule the import: This option allows users to set up a recurring data import schedule, ensuring the model is regularly updated with the latest data from the source system. This automated process helps maintain data freshness without manual intervention.

Both methods ensure that the story reflects the most current data, accommodating changes in the transaction data of the model's data source.

When the SAP Analytics Cloud (SAC) modeler removes the first three characters from a public dimension imported from a source system, this change impacts Stories that use this dimension. Specifically, any visualizations, calculations, or filters within those stories that rely on the original dimension values may need to be adjusted to account for the change. This modification does not affect the source system or public datasets directly, but it can impact how the data appears and behaves in stories that use the modified dimension.