SAP C_SAC_2402 SAP Certified Associate - Data Analyst - SAP Analytics Cloud Exam Practice Test

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 creating a new public version in SAP Analytics Cloud, you can categorize the version as either "Budget" or "Forecast". These categories help to distinguish between different types of planning data and scenarios. A "Budget" version typically represents a formal, approved plan for a specific period, often used as a benchmark. A "Forecast" version, on the other hand, is more flexible and represents predictions based on current data and trends, which can be updated more frequently to reflect changing conditions.

References:

• SAP Analytics Cloud Help Documentation: Version Management
• SAP Analytics Cloud User Guide: Creating and Managing Public Versions

When using an input control to filter data in a SAP Analytics Cloud story, the filter is applied at the Page level. This means that the selected filter criteria will affect all the components (tables, charts, etc.) on that particular page, allowing for a cohesive and consistent view of the data filtered according to the chosen parameters.

References:

• SAP Analytics Cloud Help Documentation: Filtering Data with Input Controls
• SAP Analytics Cloud User Guide: Page-Level Filtering in Stories

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 allow your colleague to review and comment on a story stored in My Files, you should share the story with them and grant View access. This will enable them to open, review, and add comments to the story directly within SAP Analytics Cloud, facilitating collaboration and feedback.

References:

• SAP Analytics Cloud Help Documentation: Sharing Stories and Collaboration
• SAP Analytics Cloud User Guide: Story Sharing and Reviewing

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

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

In SAP Analytics Cloud, the Account dimension can be used similarly to a measure. This dimension is specifically designed for financial data and can hold various types of financial metrics, such as revenues, expenses, assets, and liabilities. It allows for the application of financial calculations and aggregations, which is why it can function similarly to measures in the context of financial reporting and analysis.

References:

• SAP Analytics Cloud Help Documentation: Understanding Dimensions and Measures
• SAP Analytics Cloud User Guide: Working with Account Dimensions

To retain column headers visible when scrolling down in a long table in SAP Analytics Cloud, you should enable "Keeping member names visible" and use the "Freeze Up to row" feature. "Keeping member names visible" ensures that headers remain on the screen as you scroll, while "Freeze Up to row" allows you to specify how many top rows (including the headers) remain fixed during vertical scrolling, enhancing table readability.

References:

• SAP Analytics Cloud Help Documentation: Table Configuration and Customization
• SAP Analytics Cloud User Guide: Enhancing Table Navigation and Readability

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 ensuring that 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

To reduce data refresh time when opening a story, it's beneficial to avoid tables with excessive rows and columns, load widgets in the background, and minimize the use of prompts. Large tables can significantly impact performance, background widget loading optimizes resource use, and fewer prompts mean less user interaction is required before the story is fully loaded, all contributing to faster story opening times.

References:

• SAP Analytics Cloud Help Documentation: Optimizing Story Performance
• SAP Analytics Cloud User Guide: Best Practices for Story Design and Performance

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 page type 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

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.

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

When entering values for several expense accounts in a data table in SAP Analytics Cloud, the "Mass" data entry mode should be used to process the data with a delay as defined in the System Administration settings. This mode is designed for bulk data entry, allowing users to input multiple values across different accounts or dimensions before the system processes these entries. This can be particularly useful in scenarios where a large volume of data needs to be entered and processed efficiently, and a delay is preferable to ensure data integrity and accuracy.

References:

• SAP Analytics Cloud Help Documentation: Data Entry and Data Action Processing
• SAP Analytics Cloud User Guide: Mass Data Entry and Processing

In SAP Analytics Cloud stories, conditional formatting can be applied to Table and Chart elements. This feature allows users to visually highlight data points based on specific conditions, such as thresholds, ranges, or comparisons, making it easier to identify trends, outliers, or areas of interest within the data presented in tables and charts.

References:

• SAP Analytics Cloud Help Documentation: Conditional Formatting
• SAP Analytics Cloud User Guide: Applying Conditional Formatting to Tables and Charts

To transform data in a dataset using if/then/else logic within SAP Analytics Cloud, the Calculations editor is used. This feature allows users to create calculated columns or measures based on conditional logic, enabling complex data manipulation and enhancement directly within the platform. The Calculations editor provides a flexible environment to apply logical expressions, arithmetic operations, and functions to dataset columns, facilitating customized data analysis and reporting needs.

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.

References:

• SAP Analytics Cloud Help Documentation: Modifying Dimensions
• SAP Analytics Cloud User Guide: Impact of Dimension Changes on Stories