[Mar 30, 2025] Get Unlimited Access to C_SAC_2415 Certification Exam Cert Guide [Q12-Q26]

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[Mar 30, 2025] Get Unlimited Access to C_SAC_2415 Certification Exam Cert Guide

Reliable Study Materials for C_SAC_2415 Exam Success For Sure

SAP C_SAC_2415 Exam Syllabus Topics:

Topic	Details
Topic 1	Data Modeling, Analysis, and Integration: This section evaluates the expertise of professionals in data modeling and analytics. It emphasizes the creation and configuration of models within SAP Analytics Cloud (SAC), detailing the role and functionalities of the Data Analyzer, and integrating SAC with external applications.
Topic 2	Planning: This section measures the proficiency of planning and financial analysts in utilizing SAP Analytics Cloud’s enterprise planning features. It covers working with planning models and stories, configuring data actions, managing versions, and conducting forecasting and simulations.
Topic 3	Story Design: This section assesses the skills of data analysts and report designers. It includes the design and presentation of stories within SAC, with a focus on configuring story elements such as pages, tables, charts, and widgets.
Topic 4	Performance, Troubleshooting, and Security Management: This section is designed for system administrators and security managers. It involves identifying performance issues, understanding their causes and solutions, and explaining the security model of SAP Analytics Cloud.
Topic 5	Connections and Data Preparation: This section targets data integration specialists and preparers. It involves identifying the various methods SAC employs to access data and understanding the appropriate use cases for each.

Q12. What can you do with a multi action? Note: There are 2 correct answers to this question.

Run allocation data actions

Import transaction data

Approve data

Run allocation processes

Q13. In a data model, what can you use to further describe a dimension?

Data action

Measure

Property

Variable

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.
Reference:
SAP Analytics Cloud Help Documentation: Dimension Properties
SAP Analytics Cloud User Guide: Enhancing Dimensions with Properties

Q14. You need to delete characters from a column in a dataset. What can you use? Note: There are 2 correct answers to this question.

Custom expression editor

Formula bar

Calculation editor

Transform bar

Q15. When scrolling down in a long table, how can you retain column headers? Note: There are 2 correct answers to this question.

Enable Keeping member names visible

Enable Auto-Size And Page Table Vertically

Freeze Up to row

Freeze Up to column

Q16. You are using a live connection for a model. Where is the data stored?

Public dataset

SAP Analytics Cloud model

Source system

Embedded data set

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.
Reference:
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.
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.

Q17. Which of the following data sources can you use in SAP Analytics Cloud data analyzer? Note: There are 3 correct answers to this question.

SAP Analytics Cloud public dataset

SAP HANA view

SAP Datasphere model

SAP Analytics Cloud analytic model

SAP BusinessObjects Universe

Q18. Where can you create a blank planning version?

In a data cell

In version management

In the version dimension

In the planning model

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

Q19. The SAP Analytics Cloud (SAC) modeler has removed the first three characters from an SAP Analytics Cloud public dimension imported from a source system. What is impacted by this change?

Public datasets

Source system

Stories

Embedded data sets

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

Q20. Which dimension type can you use like a measure?

Account

Date

Organization

Entity

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

Q21. You input new data for a private version in a story. What must you do to ensure the new data is added to the model?

Save

Send

Publish

Nothing

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.
Reference:
SAP Analytics Cloud Help Documentation: Private Versions and Publishing SAP Analytics Cloud User Guide: Working with Private Versions in Stories

Q22. Which features are available in the Optimized Design Experience? Note: There are 3 correct answers to this question.

Undo button

Grid pages

Linked widgets diagram

Composites

Explorer

Q23. Which automatically created dimension type can you delete from an analytic data model?

Version

Date

Organization

Generic

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

Q24. 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?

Change the Year measure to a dimension in the dataset.

Includes the Year measure in a level-based time hierarchy in the dataset.

Insert a character into the Year measure using the transform bar.

Add the month as a suffix to the Year measure.

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.

Q25. You are creating a script for an advanced data action. Which character designates a virtual variable member?