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You have a dimension with members for product groups and products. Each product group
has associated products. You want to plan by product group without disaggregating into the
products. How can you do this?
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.
SAP Analytics Cloud Help Documentation: Hierarchies in Planning
SAP Analytics Cloud User Guide: Managing Hierarchies for Planning
You are creating an allocation step to distribute expenses from the HR cost center to your operating
cost centers. Which dimension setting controls how much is distributed to each operating cost
center?
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.
SAP Analytics Cloud Help Documentation: Allocation Steps in Planning
SAP Analytics Cloud User Guide: Using Drivers for Allocation
Where can you create a blank planning version?
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 Help Documentation: Version Management in Planning
SAP Analytics Cloud User Guide: Creating New Versions for Planning
You have a dataset that extracts data from an SAP Business Warehouse (SAP BW) system. The
data in the SAP BW system changes. How can you update the dataset?
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.
SAP Analytics Cloud Help Documentation: Scheduling Data Refresh
SAP Analytics Cloud User Guide: Automating Dataset Updates from External Sources
What are the available connection types in SAP Analytics Cloud? Note: There are 2 correct answers
to this question.
SAP Analytics Cloud supports two primary types of data connections: Live Data Connection and Import Data Connection. Live Data Connection establishes a direct link to the data source, allowing real-time data access without replicating the data into SAP Analytics Cloud. This is ideal for scenarios where up-to-the-minute data is crucial, and data volume is large. On the other hand, Import Data Connection involves copying data from the source into SAP Analytics Cloud, which is suitable for scenarios where data doesn't change frequently, or there's a need for data transformation and enrichment within SAP Analytics Cloud.
SAP Analytics Cloud Help Documentation: Data Connections Overview
SAP Analytics Cloud User Guide: Live Data vs. Import Data Scenarios
