C_SAC_2402 Sample Questions Answers

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.

References:

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

When using a live connection in SAP Analytics Cloud, data security is defined and managed within the source system. This approach leverages the existing security protocols and permissions set up in the source system, ensuring that data governance and access controls remain consistent and are centrally managed. Users accessing data through SAP Analytics Cloud with a live connection will be subject to the same security constraints and permissions as if they were accessing the data directly from the source system. This integration ensures a unified security model, simplifying administration and ensuring data security and compliance.

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

When creating a data action to copy data from one year to the next, it's crucial to adjust the Granularity setting in the parameter for the source year. Granularity refers to the level of detail at which the data action will operate. For time-based data copying, ensuring the granularity aligns with the desired year level is essential for accurately copying the data. If granularity is set at a finer level (like months or days), it may not correctly copy annual data. Therefore, adjusting the granularity to the year level is necessary for the data action to function as intended.

References:

• SAP Analytics Cloud Help Documentation: Data Actions
• SAP Analytics Cloud User Guide: Configuring Data Actions for Time-Based Copy

The Just Ask feature in SAP Analytics Cloud utilizes natural language processing (NLP) to interpret user queries formulated in natural language. This feature allows users to ask questions about their data in a conversational manner, and the system intelligently interprets and responds with relevant visualizations and insights, making data analysis more accessible and intuitive.

References:

• SAP Analytics Cloud Help Documentation: Just Ask Feature and NLP
• SAP Analytics Cloud User Guide: Leveraging Natural Language Processing

In SAP Analytics Cloud, the calculation types that include dynamic date options are "Date Difference" and "Difference From." Date Difference allows for the calculation of the time interval between two dates, which can be dynamic based on the data or context. Difference From enables comparisons of values across different time periods, incorporating dynamic date calculations to understand changes or trends over time.

References:

• SAP Analytics Cloud Help Documentation: Date Calculations
• SAP Analytics Cloud User Guide: Dynamic Date Options in Calculations

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

In SAP Analytics Cloud, you can create Embedded and Analytic data models to support data analysis in stories. Embedded models are created within specific stories and are tailored for that story's analysis needs. Analytic models, on the other hand, are standalone models that can be reused across multiple stories and provide a broader, more general-purpose data structure for analysis.

References:

• SAP Analytics Cloud Help Documentation: Data Models in Stories
• SAP Analytics Cloud User Guide: Embedded vs. Analytic Models

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

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.

References:

• SAP Analytics Cloud Help Documentation: Allocation Steps in Planning
• SAP Analytics Cloud User Guide: Using Drivers for Allocation

In SAP Analytics Cloud, enabling Advanced Mode in story design is necessary for activities that involve custom development or advanced integration, such as adding JavaScript to a button for custom interactions or behaviors, and adding a custom widget, which allows for the integration of bespoke visualizations or functionalities that are not available out-of-the-box. These features require a deeper technical engagement, hence the need for Advanced Mode.

References:

• SAP Analytics Cloud Help Documentation: Advanced Story Design Features
• SAP Analytics Cloud User Guide: Custom Widgets and JavaScript Functions

To display differences between measures in a chart within SAP Analytics Cloud, you can use the Variance feature. Variance helps to highlight the differences or gaps between two data points or measures, making it easier to identify trends, outliers, or areas requiring attention in visual representations like charts.

References:

• SAP Analytics Cloud Help Documentation: Variance Analysis in Charts
• SAP Analytics Cloud User Guide: Displaying Measure Differences Using Variance

In SAP Analytics Cloud, the average rate type is typically used with "AST" (Assets) and "LEQ" (Liabilities and Equity) account types. These account types represent balance sheet items, for which averages are often used in financial reporting and analysis,especially for calculating ratios or performance indicators that require average values over a period, such as average assets or average equity.

References:

• SAP Analytics Cloud Help Documentation: Account Types and Rate Types
• SAP Analytics Cloud User Guide: Financial Planning and Analysis

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 creating a styling rule for a table in SAP Analytics Cloud, the context refers to "The most granular level in the table." This means that the styling rule will be applied to the lowest level of data detail available in the table, such as individual cells or rows, allowing for precise control over the appearance of specific data points or categories within the table.

References:

• SAP Analytics Cloud Help Documentation: Styling Tables
• SAP Analytics Cloud User Guide: Applying Styling Rules to Table Data