C_SAC_2415 Questions and Answers

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

In Data Analyzer, users can perform various tasks to analyze their data, including filtering data and drilling down on data. Filtering allows users to narrow down the dataset to focus on specific subsets of data, enhancing the analysis's relevancy. Drilling down provides the ability to explore data at a more granular level, revealing deeper insights into the data points and trends within the larger dataset.

References:

SAP Analytics Cloud Help Documentation: Data Analyzer Usage

SAP Analytics Cloud User Guide: Filtering and Drilling Down in Data Analyzer

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

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 the context of creating an allocation step to distribute expenses from the HR cost center to operating cost centers in SAP Analytics Cloud, the "Driver" dimension setting is crucial. This setting determines the basis or criteria on which the distribution is calculated and applied to each operating cost center. For instance, the driver could be the number of employees, square footage, or any other relevant metric that justifies the distribution of costs. By defining a driver, you ensure that the allocation of expenses is proportional and fair based on the selected criteria.

References:

SAP Analytics Cloud Help Documentation: Allocation Steps in Planning

SAP Analytics Cloud User Guide: Using Drivers for Allocation

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-generateddimensions 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

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.

References:

SAP Analytics Cloud Help Documentation: Using Table Formulas

SAP Analytics Cloud User Guide: Cell-Based Calculations in Stories