C_SAC_2415 SAP Certified Associate - Data Analyst - SAP Analytics Cloud Questions and Answers

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

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

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

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.

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

In a data model within SAP Analytics Cloud, Properties are used to further describe dimensions. Properties provide additional context or metadata for dimension members, such as descriptions, classifications, or other attributes that help to better understand and analyze the data within the dimension. This makes properties essential for detailed data analysis and reporting.

References:

SAP Analytics Cloud Help Documentation: Dimension Properties

SAP Analytics Cloud User Guide: Enhancing Dimensions with Properties

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.

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 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

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

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