Question #1
What source system can you connect to with a live connection?
- A . SAP ERP Central Component
- B . SAP SuccessFactors
- C . SAP Business ByDesign Analytics
- D . SAP Datasphere
Correct Answer: D
D
Explanation:
SAP Analytics Cloud can establish a live connection with various source systems, including SAP Datasphere. This allows for real-time data access and analysis without the need to replicate data into the cloud, which is beneficial for scenarios where data privacy and security are paramount.
Reference: SAP Analytics Cloud Connection Guide1
SAC Live and Import Connection Overview2
SAP Analytics Cloud: Expand Live Data Source Options3
Live connection in SAP Analytics Cloud: advantages and challenges4 Explaining Where the Data Comes From – SAP Learning5
D
Explanation:
SAP Analytics Cloud can establish a live connection with various source systems, including SAP Datasphere. This allows for real-time data access and analysis without the need to replicate data into the cloud, which is beneficial for scenarios where data privacy and security are paramount.
Reference: SAP Analytics Cloud Connection Guide1
SAC Live and Import Connection Overview2
SAP Analytics Cloud: Expand Live Data Source Options3
Live connection in SAP Analytics Cloud: advantages and challenges4 Explaining Where the Data Comes From – SAP Learning5
Question #2
You are using a live connection for a model. Where is the data stored?
- A . Public dataset
- B . SAP Analytics Cloud model
- C . Source system
- D . Embedded data set
Correct Answer: C
C
Explanation:
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.
Reference: 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.
C
Explanation:
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.
Reference: 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.
Question #3
You are using a live connection for a model. Where can you define data security?
- A . Source system
- B . Data access control
- C . SAP Analytics Cloud model
- D . SAP Analytics Cloud role
Correct Answer: A
A
Explanation:
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.
A
Explanation:
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.
Question #4
What must you use to transform data in a dataset using if/then/else logic?
- A . Calculations editor
- B . Custom expression editor
- C . Formula bar
- D . Transform bar
Correct Answer: B
B
Explanation:
To transform data in a dataset using if/then/else logic in SAP Analytics Cloud, you must use the Custom expression editor. This tool allows you to write complex logical conditions and perform conditional data transformations. The steps involved are: Open the dataset you want to transform.
Navigate to the "Custom expression editor".
Write your if/then/else logic using the syntax supported by SAP Analytics Cloud. For example:
IF([Sales] > 1000, "High", "Low")
Apply the expression to the relevant column.
Validate and save your changes.
This approach allows for flexibility and precision in transforming your data based on specific
conditions.
Reference: =
SAP Help Portal: SAP Analytics Cloud
Official SAP Analytics Cloud Documentation
B
Explanation:
To transform data in a dataset using if/then/else logic in SAP Analytics Cloud, you must use the Custom expression editor. This tool allows you to write complex logical conditions and perform conditional data transformations. The steps involved are: Open the dataset you want to transform.
Navigate to the "Custom expression editor".
Write your if/then/else logic using the syntax supported by SAP Analytics Cloud. For example:
IF([Sales] > 1000, "High", "Low")
Apply the expression to the relevant column.
Validate and save your changes.
This approach allows for flexibility and precision in transforming your data based on specific
conditions.
Reference: =
SAP Help Portal: SAP Analytics Cloud
Official SAP Analytics Cloud Documentation
Question #5
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 . Includes the Year measure in a level-based time hierarchy in the dataset.
- C . Insert a character into the Year measure using the transform bar.
- D . Add the month as a suffix to the Year measure.
Correct Answer: A
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.
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.
Question #6
You have a story based on an import model. The transaction data in the model’s data source changes.
How can you update the data in the model? Note: There are 2 correct answers to this question.
- A . Allow model import
- B . Refresh the story
- C . Refresh the import job
- D . Schedule the import
Correct Answer: B D
B D
Explanation:
To update the data in a model based on an import connection, two main approaches can be used: Refresh the story: This action forces SAP Analytics Cloud to reload the data for the visualizations in a story, pulling in the most recent data available in the model. This is a manual process initiated by the user.
Schedule the import: This option allows users to set up a recurring data import schedule, ensuring the model is regularly updated with the latest data from the source system. This automated process helps maintain data freshness without manual intervention.
Both methods ensure that the story reflects the most current data, accommodating changes in the transaction data of the model’s data source.
B D
Explanation:
To update the data in a model based on an import connection, two main approaches can be used: Refresh the story: This action forces SAP Analytics Cloud to reload the data for the visualizations in a story, pulling in the most recent data available in the model. This is a manual process initiated by the user.
Schedule the import: This option allows users to set up a recurring data import schedule, ensuring the model is regularly updated with the latest data from the source system. This automated process helps maintain data freshness without manual intervention.
Both methods ensure that the story reflects the most current data, accommodating changes in the transaction data of the model’s data source.
Question #7
You need to delete characters from a column in a dataset.
What can you use? Note: There are 2 correct answers to this question.
- A . Custom expression editor
- B . Formula bar
- C . Calculation editor
- D . Transform bar
Correct Answer: A D
Question #8
What can you use to organize dimensions into logical categories in a live model?
- A . Level-based hierarchy
- B . Groups
- C . Value driver tree
- D . Parent-child hierarchy
Correct Answer: D
D
Explanation:
In a live model within SAP Analytics Cloud, dimensions can be organized into logical categories using either level-based hierarchies or parent-child hierarchies. Level-based hierarchies are used when the relationships between items are defined by distinct levels, such as Geography might be divided into Country, State, and City levels. Parent-child hierarchies, on the other hand, are useful when the data’s hierarchy is not strictly defined by levels but by a parent relationship where a child member is associated with a parent member, which is common in organizational structures or product categories.
Reference: SAP Analytics Cloud Help Documentation: Creating and Managing Hierarchies SAP Analytics Cloud User Guide: Working with Models
D
Explanation:
In a live model within SAP Analytics Cloud, dimensions can be organized into logical categories using either level-based hierarchies or parent-child hierarchies. Level-based hierarchies are used when the relationships between items are defined by distinct levels, such as Geography might be divided into Country, State, and City levels. Parent-child hierarchies, on the other hand, are useful when the data’s hierarchy is not strictly defined by levels but by a parent relationship where a child member is associated with a parent member, which is common in organizational structures or product categories.
Reference: SAP Analytics Cloud Help Documentation: Creating and Managing Hierarchies SAP Analytics Cloud User Guide: Working with Models
Question #9
What are the available connection types in SAP Analytics Cloud? Note: There are 2 correct answers to this question.
- A . Live
- B . On-premise
- C . Cloud
- D . Import
Correct Answer: A D
A D
Explanation:
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
A D
Explanation:
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
Question #10
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?
- A . Create a group that includes the dimensions.
- B . Create a level-based hierarchy in the dataset.
- C . Create a parent-child hierarchy in the dataset.
- D . Convert the embedded dataset to a model.
Correct Answer: B
B
Explanation:
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
B
Explanation:
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
Question #11
You are creating a script for an advanced data action.
Which character designates a virtual variable member?
- A . %
- B . /
- C . *
- D . #
Correct Answer: B
Question #12
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?
- A . Level
- B . Hierarchy
- C . Cardinality
- D . Granularity
Correct Answer: C
Question #13
You want to total several income and expense accounts using the account type property.
What configuration option in the advanced formula must you use?
- A . Unbooked
- B . Append
- C . Signflip
- D . Aggregate To
Correct Answer: C
Question #14
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?
- A . Use two properties
- B . Dis-able allocations
- C . Use two hierarchies
- D . Dis-able distribution
Correct Answer: C
C
Explanation:
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.
Reference: SAP Analytics Cloud Help Documentation: Hierarchies in Planning
SAP Analytics Cloud User Guide: Managing Hierarchies for Planning
C
Explanation:
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.
Reference: SAP Analytics Cloud Help Documentation: Hierarchies in Planning
SAP Analytics Cloud User Guide: Managing Hierarchies for Planning
Question #15
What can you do with a multi action? Note: There are 2 correct answers to this question.
- A . Run allocation data actions
- B . Import transaction data
- C . Approve data
- D . Run allocation processes
Correct Answer: A B
Question #16
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 . Reference
- B . Driver
- C . Distribute
- D . Redistribute
Correct Answer: B
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
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
Question #17
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?
- A . Fluid
- B . Single
- C . Mass
Correct Answer: A
Question #18
Where can you change a data lock status? Note: There are 2 correct answers to this question.
- A . Data action
- B . Value lock management
- C . Multi action
- D . Calendar task
Correct Answer: C D
Question #19
How can you improve the performance of advanced data actions? Note: There are 3 correct answers to this question.
- A . Use fewer MEMBERSET statements
- B . Use fewer FOREACH functions
- C . Use fewer IF statements
- D . Use fewer data functions
- E . Use fewer aggregation dimension functions
Correct Answer: BCD
Question #20
What type of predictive scenario can write back to a planning model?
- A . Regression
- B . Value driver tree
- C . Classification
- D . Time series forecast
Correct Answer: D
D
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
D
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