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SAP Certified Associate - Data Analyst - SAP Analytics Cloud Sample Questions (Q30-Q35):
NEW QUESTION # 30
Where can you create a blank planning version?
- A. In the version dimension
- B. In a data cell
- C. In the planning model
- D. In version management
Answer: D
Explanation:
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.
Reference:
SAP Analytics Cloud Help Documentation: Version Management in Planning
SAP Analytics Cloud User Guide: Creating New Versions for Planning
NEW QUESTION # 31
Which automatically created dimension type can you delete from an analytic data model?
- A. Generic
- B. Date
- C. Version
- D. Organization
Answer: A
Explanation:
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-generated dimensions like Date or Version, can be modified or removed as per the specific needs of your data model and analysis requirements.
Reference:
SAP Analytics Cloud Help Documentation: Data Model Dimensions
SAP Analytics Cloud User Guide: Managing Dimensions in Analytic Models
NEW QUESTION # 32
What type of predictive scenario can write back to a planning model?
- A. Regression
- B. Value driver tree
- C. Time series forecast
- D. Classification
Answer: C
Explanation:
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.
Reference:
SAP Analytics Cloud Help Documentation: Predictive Scenarios and Planning SAP Analytics Cloud User Guide: Time Series Forecasting in Planning Models
NEW QUESTION # 33
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?
- A. Redistribute
- B. Driver
- C. Distribute
- D. Reference
Answer: B
Explanation:
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.
Reference:
SAP Analytics Cloud Help Documentation: Allocation Steps in Planning
SAP Analytics Cloud User Guide: Using Drivers for Allocation
NEW QUESTION # 34
You import data into a dataset. One of the columns imported is Year, and SAP Analytics Cloud interprets it as a measure. How can you ensure that it is treated as a calendar year?
- A. Change the Year measure to a dimension in the dataset.
- B. Insert a character into the Year measure using the transform bar.
- C. Add the month as a suffix to the Year measure.
- D. Includes the Year measure in a level-based time hierarchy in the dataset.
Answer: A
Explanation:
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.
NEW QUESTION # 35
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