C_SAC_2415 SAP Certified Associate - Data Analyst - SAP Analytics Cloud Free Practice Exam Questions (2025 Updated)

When you have a dimension with members for product groups and associated products and want to plan by product group without disaggregating into the individual products, using two hierarchies is the best approach. One hierarchy can represent the product groups at a higher level, allowing for planning and analysis at the group level. The second hierarchy can include both the product groups and their associated individual products for more detailed analysis when needed. This approach provides flexibility in planning and analyzing data at different levels of detail without the necessity of disaggregating data at the product group level.

References:

SAP Analytics Cloud Help Documentation: Hierarchies in Planning

SAP Analytics Cloud User Guide: Managing Hierarchies for Planning

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 storiesthat 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.

References:

SAP Analytics Cloud Help Documentation: Modifying Dimensions

SAP Analytics Cloud User Guide: Impact of Dimension Changes on Stories

To ensure that labels are always visible on a column chart in a story, you should select the "Avoid Data Label Overlap" checkbox in the chart's settings. This option adjusts the labels' positioning to prevent overlap, making them visible at all times without the need for mouse-over interaction.

References:

SAP Analytics Cloud Help Documentation: Chart Design and Label Management

SAP Analytics Cloud User Guide: Enhancing Data Label Visibility in Charts

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

To aggregate measures for columns such as Country, Region, City, and Customer Name as a single column in an embedded dataset, creating a level-based hierarchy is the most effective approach. This type of hierarchy allows you to define a multi-level structure that represents the logical relationship between different geographical entities and customer names. By doing so, you can easily perform aggregations and analyze data at various levels of detail, from the broadest level (e.g., Country) down to the most specific one (e.g., Customer Name).

References:

SAP Analytics Cloud Help Documentation: Creating Hierarchies in Models

SAP Analytics Cloud User Guide: Data Modeling and Hierarchies

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

In a planning data model in SAP Analytics Cloud, the Date and Version dimensions are included by default. The Date dimension is essential for time-based planning and analysis, while the Version dimension allows for the creation and management of different data scenarios, such as budget, forecast, and actuals. These dimensions provide a foundational structure for planning activities within the model.

References:

SAP Analytics Cloud Help Documentation: Planning Data Model Basics

SAP Analytics Cloud User Guide: Understanding Default Dimensions in Planning Models

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

When inputting new data for a private version in a story in SAP Analytics Cloud, it is necessary to "Publish" the data to ensure it is added to the model. Publishing the private version commits the changes to the underlying model, making the new data visible and accessible to other users according to their permissions. This step is crucial for ensuringthat the updated data is incorporated into the shared model for further analysis and decision-making.

References:

SAP Analytics Cloud Help Documentation: Private Versions and Publishing

SAP Analytics Cloud User Guide: Working with Private Versions in Stories