Question 1
HOTSPOT
You are deploying a multidimensional Microsoft SQL Server Analysis Services (SSAS) project. You add two new role-playing dimensions named Picker and Salesperson to the cube. Both of the cube dimensions are based upon the underlying dimension named
Employee in the data source view.
Users report that they are unable to differentiate the Salesperson attributes from the Picker attributes.
You need to ensure that the Salesperson and Picker attributes in each dimension use unique names.
In the table below, identify an option that you would use as part of the process to alter the names of the attributes for each of the dimensions.
NOTE: Make only one selection in each column.
You are deploying a multidimensional Microsoft SQL Server Analysis Services (SSAS) project. You add two new role-playing dimensions named Picker and Salesperson to the cube. Both of the cube dimensions are based upon the underlying dimension named
Employee in the data source view.
Users report that they are unable to differentiate the Salesperson attributes from the Picker attributes.
You need to ensure that the Salesperson and Picker attributes in each dimension use unique names.
In the table below, identify an option that you would use as part of the process to alter the names of the attributes for each of the dimensions.
NOTE: Make only one selection in each column.
Question 2
You are building a Microsoft SQL Server Analysis Services multidimensional model over a SQL Server database. In a cube named OrderAnalysis, there is a standard cube dimension named Stock Item.
This dimension has the following attributes:
Stock Item Key
WWI Stock Item ID
Stock Item
Color
Selling Package
Buying Package
Brand
Size
Lead Time Days
Quantity Per Outer
Is Chiller Stock
Barcode
Tax Rate
Unit Price
Recommended Retail Price
Typical Weight Per Unit
Photo
Valid From
Valid To
Lineages Key
Users report that the attributes Stock Item Key and Photo are distracting and are not providing any value.
They have asked for the attributes to be removed. However, these attributes are needed by other cubes.
You need to hide the specified attributes from the end users of the OrderAnalysis cube. You do not want to change the structure of the dimension.
Which change should you make to the properties for the Stock Item Key and Photo attributes?
This dimension has the following attributes:
Stock Item Key
WWI Stock Item ID
Stock Item
Color
Selling Package
Buying Package
Brand
Size
Lead Time Days
Quantity Per Outer
Is Chiller Stock
Barcode
Tax Rate
Unit Price
Recommended Retail Price
Typical Weight Per Unit
Photo
Valid From
Valid To
Lineages Key
Users report that the attributes Stock Item Key and Photo are distracting and are not providing any value.
They have asked for the attributes to be removed. However, these attributes are needed by other cubes.
You need to hide the specified attributes from the end users of the OrderAnalysis cube. You do not want to change the structure of the dimension.
Which change should you make to the properties for the Stock Item Key and Photo attributes?
Question 3
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution. Determine whether the solution meets the stated goals.
You have an existing multidimensional cube that provides sales analysis. The users can slice by date, product, location, customer, and employee.
The management team plans to evaluate sales employee performance relative to sales targets. You identify the following metrics for employees:
You need to implement the KPI based on the Status expression.
Solution: You design the following solution:
Does the solution meet the goal?
You have an existing multidimensional cube that provides sales analysis. The users can slice by date, product, location, customer, and employee.
The management team plans to evaluate sales employee performance relative to sales targets. You identify the following metrics for employees:
You need to implement the KPI based on the Status expression.
Solution: You design the following solution:
Does the solution meet the goal?
Question 4
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution. Determine whether the solution meets the stated goals.
You have an existing multidimensional cube that provides sales analysis. The users can slice by date, product, location, customer, and employee.
The management team plans to evaluate sales employee performance relative to sales targets. You identify the following metrics for employees:
Ninety percent or greater relative to sales target values is considered on target.
Between 75 percent and 90 percent is considered slightly off target.
Below 75 percent is considered off target.
You need to implement the KPI based on the Status expression.
Solution: You design the following solution:
Does the solution meet the goal?
You have an existing multidimensional cube that provides sales analysis. The users can slice by date, product, location, customer, and employee.
The management team plans to evaluate sales employee performance relative to sales targets. You identify the following metrics for employees:
Ninety percent or greater relative to sales target values is considered on target.
Between 75 percent and 90 percent is considered slightly off target.
Below 75 percent is considered off target.
You need to implement the KPI based on the Status expression.
Solution: You design the following solution:
Does the solution meet the goal?
Question 5
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution. Determine whether the solution meets the stated goals.
You deploy a tabular data model to an instance of Microsoft SQL Server Analysis Services (SSAS). The model uses an in-memory cache to store and query data. The data set is already the same size as the available RAM on the server. Data volumes are likely to continue to increase rapidly.
Your data model contains multiple calculated tables.
The data model must begin processing each day at 2:00 and processing should be complete by 4:00 the same day. You observe that the data processing operation often does not complete before 7:00. This is adversely affecting team members.
You need to improve the performance.
Solution: Change the storage mode for the data model to DirectQuery.
Does the solution meet the goal?
You deploy a tabular data model to an instance of Microsoft SQL Server Analysis Services (SSAS). The model uses an in-memory cache to store and query data. The data set is already the same size as the available RAM on the server. Data volumes are likely to continue to increase rapidly.
Your data model contains multiple calculated tables.
The data model must begin processing each day at 2:00 and processing should be complete by 4:00 the same day. You observe that the data processing operation often does not complete before 7:00. This is adversely affecting team members.
You need to improve the performance.
Solution: Change the storage mode for the data model to DirectQuery.
Does the solution meet the goal?