[Q31-Q47] Get up-to-date Real Exam Questions for C-SAC-2501 UPDATED [2025]

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Get up-to-date Real Exam Questions for C-SAC-2501 UPDATED [2025]

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SAP C-SAC-2501 Exam Syllabus Topics:

Topic	Details
Topic 1	Story Design: This section of the exam measures the skills of Data Analysts and covers story design concepts, accounting for The focus on creating effective visualizations and narratives using data.
Topic 2	Connections and Data Preparation: This section targets Data Engineers and covers connections and data preparation, The focus is on connecting to data sources and preparing data for visualization.
Topic 3	Planning: This section evaluates the expertise of Business Intelligence Developers. The focus is on planning and strategic aspects of data visualization projects.
Topic 4	Performance, Troubleshooting, and Security Management: This section measures the skills of IT Administrators and covers performance, troubleshooting, and security management, making up less than 10% of the exam. It focuses on optimizing performance, resolving issues, and ensuring data security in data visualization environments.
Topic 5	Data Modeling, Analysis, and Integration: This section assesses the skills of Data Architects and covers data modeling, analysis, and integration.

NO.31 You are creating a new public version. Which categories can you use? Note: There are 2 correct answers to this question.

Budget

Actual

Predictive

Forecast

NO.32 You are entering values for several expense accounts in a data table. Which data entry mode must you use to process the data with a delay defined in System Administration?

Fluid

Single

Mass

NO.33 Your embedded dataset in SAP Analytics Cloud has columns for Country, Region, City, and Customer Name. You want to aggregate measures for these columns as a single column. What can you do?

Create a group that includes the dimensions.

Create a level-based hierarchy in the dataset.

Create a parent-child hierarchy in the dataset.

Convert the embedded dataset to a model.

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).
Reference:
SAP Analytics Cloud Help Documentation: Creating Hierarchies in Models
SAP Analytics Cloud User Guide: Data Modeling and Hierarchies

NO.34 What are the available connection types in SAP Analytics Cloud? Note: There are 2 correct answers to this question.

Live

On-premise

Cloud

Import

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.
Reference:
SAP Analytics Cloud Help Documentation: Data Connections Overview
SAP Analytics Cloud User Guide: Live Data vs. Import Data Scenarios

NO.35 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

NO.36 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

NO.37 You are creating a data action to copy data from one year to the next.In the parameter for the source year, which default setting must you change?

Level

Hierarchy

Cardinality

Granularity

NO.38 You have a live data model with two dimensions: Firstname and Lastname. Users want a single dimension in the data model that displays the dimensions as Lastname, Firstname. What must you do?

Create the combined data in the source system.

Create a calculated dimension in the data model.

Create a calculated dimension in the story.

Group the Firstname and Lastname in the data model.

To combine two dimensions, Firstname and Lastname, into a single dimension displaying as Lastname, Firstname in a live data model, you should create a calculated dimension in the data model. This calculated dimension will concatenate the two fields into one, according to the specified format. This approach allows for the creation of a new dimension that can be used across various reports and analyses within the model, maintaining the integrity of the original dimensions.
Reference:
SAP Analytics Cloud Help Documentation: Creating Calculated Dimensions
SAP Analytics Cloud User Guide: Data Modeling Best Practices

NO.39 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

NO.40 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

NO.41 In a planning data model, what dimensions are included by default? Note: There are 2 correct Answers to this question.

Date

Version

Entity

Organization

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.
Reference:
SAP Analytics Cloud Help Documentation: Planning Data Model Basics
SAP Analytics Cloud User Guide: Understanding Default Dimensions in Planning Models

NO.42 Which programming language is used for scripting in an SAP Analytics Cloud story?

Python

Wrangling Expression Language

ABAP

JavaScript

NO.43 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.

NO.44 What account types use the average rate type? Note: There are 2 correct answers to this question.

INC

AST

EXP

LEQ

NO.45 Which calculation types include dynamic date options? Note: There are 2 correct answers to this Question.

Aggregation

Date Difference

Restricted Measure

Difference From

NO.46 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

NO.47 In which types of data source can you concatenate data? Note: There are 3 correct answers to this question.

Embedded data set

Live data model

Data analyzer insight

Standalone dataset

Imported data model

In SAP Analytics Cloud, data concatenation can be performed on Embedded datasets, Standalone datasets, and Imported data models. This process involves combining data from multiple sources or tables into a single dataset or model, providing a unified view of the data. This is particularly useful for analyses that require a comprehensive dataset compiled from various data sources.
Reference:
SAP Analytics Cloud Help Documentation: Working with Datasets
SAP Analytics Cloud User Guide: Concatenating Data in Models and Datasets

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