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Q11. - (Topic 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 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: Enable Buffer Cache Extensions. Does the solution meet the goal?
A. Yes
B. No
Answer: B
Explanation:
In this scenario we would need both Buffer Cache Extensions and SSD.
The buffer pool extension provides the seamless integration of a nonvolatile random access memory (that is, solid-state drive) extension to the Database Engine buffer pool to
significantly improve I/O throughput.
References:https://docs.microsoft.com/en-us/sql/database-engine/configure-windows/buffer-pool-extension
Q12. - (Topic 4)
Note: This question is part of a series of questions that use the same or similar answer choices. An answer choice may be correct for more than one question in the series. Each question is independent of the other questions in this series. Information and details provided in a question apply only to that question.
You have a Microsoft SQL Server Analysis Services (SSAS) instance that is configured to use multidimensional mode. You create the following cube:
Users need to be able to analyze sales by product and color. You need to create the dimension.
Which relationship type should you use between the InternetSales table and the new dimension?
A. no relationship
B. regular
C. fact
D. referenced
E. many-to-many
F. data mining
Answer: D
Explanation:
A reference dimension relationship between a cube dimension and a measure group exists when the key column for the dimension is joined indirectly to the fact table through a key in another dimension table, as shown in the following illustration.
A reference dimension relationship represents the relationship between dimension tables and a fact table in a snowflake schema design. When dimension tables are connected in a snowflake schema, you can define a single dimension using columns from multiple tables, or you can define separate dimensions based on the separate dimension tables and then define a link between them using the reference dimension relationship setting. The following figure shows one fact table named InternetSales, and two dimension tables called Customer and Geography, in a snowflake schema.
You can create two dimensions related to the InternetSales measure group: a dimension based on the Customer table, and a dimension based on the Geography table. You can then relate the Geography dimension to the InternetSales measure group using a reference dimension relationship using the Customer dimension.
Q13. - (Topic 3)
A database named DB2 uses the InMemory query mode. Users frequently run the following query:
You need to ensure no users see the PriorYearSales measure in the field list for the Sales table.
What should you do?
A. Create a perspective, and ensure that the PriorYearSales measure is not added to the perspective. Ensure that users connect to the model by using the perspective.
B. Set the Display Folder property for PriorYearSales toHidden.
C. Remove the PriorYearSales measure from the default field set of the Sales table.
D. Create a role using Read permissions, and define a DAX expression to filter out the PriorYearSales measure. Add all users to the role.
Answer: A
Explanation:
Using perspectives in the data model might help you expose a subset of tables, columns, and measures that are useful for a particular type of analysis.
Usually, every user needs only a subset of data you create, and showing him or her the model through perspectives can offer a better user experience.
From scenario; The PriorYearSales measure is referenced by other measures, and is not intended to be analyzed directly by users.
References: Microsoft SQL Server 2012 Analysis Services, The BISM Tabular Model, Microsoft Press (July 2012), page 305
Q14. DRAG DROP - (Topic 1)
You need to create the cube processing job and the dimension processing job.
Which processing task should you use for each job? To answer, drag the appropriate processing tasks to the correct locations. Each processing task may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
Answer:
Explanation:
Box 1: ProcessData
Processes data only without building aggregations or indexes. If there is data is in the partitions, it will be dropped before re-populating the partition with source data.
Box 2: Process Update
Forces a re-read of data and an update of dimension attributes. Flexible aggregations and indexes on related partitions will be dropped.
References:https://docs.microsoft.com/en-us/sql/analysis-services/multidimensional-models/processing-options-and-settings-analysis-services
Topic 2, Case Study #2
Background
Wide World Importers has multidimensional cubes named SalesAnalysis and ProductSales. The SalesAnalysis cube is refreshed from a relational data warehouse. You have a Microsoft SQL Server Analysis Services instance that is configured to use tabular mode. You have a tabular data model named CustomerAnalysis.
Sales Analysis
The SalesAnalysis cube contains a fact table named CoffeeSale loaded from a table named FactSale in the data warehouse. The time granularity within the cube is 15 minutes. The cube is processed every night at 23:00. You determine that the fact table cannot be fully processed in the expected time. Users have reported slow query response times.
The SalesAnalysis model contains tables from a SQL Server database named SalesDB. You set the DirectQueryMode option to DirectQuery. Data analyst access data from a cache that is up to 24 hours old. Data analyst report performance issues when they access the SalesAnalysis model.
When analyzing sales by customer, the total of all sales is shown for every customer, instead of the customer’s sales value. When analyzing sales by product, the correct totals for each product are shown.
Customer Analysis
You are redesigning the CustomerAnalysis tabular data model that will be used to analyze customer sales. You plan to add a table named CustomerPermission to the model. This table maps the Active Directory login of an employee with the CustomerId keys for all customers that the employee manages.
The CustomerAnalysis data model will contain a large amount of data and needs to be shared with other developers even if a deployment fails. Each time you deploy a change during development, processing takes a long time.
Data analysts must be able to analyze sales for financial years, financial quarters, months, and days. Many reports are based on analyzing sales by month.
Product Sales
The ProductSales cube allows data analysts to view sales information by product, city, and time. Data analysts must be able to view ProductSales data by Year to Date (YTD) as a measure. The measure must be formatted as currency, associated with the Sales measure group, and contained in a folder named Calculations.
Requirements
You identify the following requirements:
*Data available during normal business hours must always be up-to-date.
*Processing overhead must be minimized.
*Query response times must improve.
*All queries that access the SalesAnalysis model must use cached data by default.
*Data analysts must be able to access data in near real time.
Q15. DRAG DROP - (Topic 2)
You need to configure the SalesAnalysis cube to correct the sales analysis by customer calculation.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Step 1: Open the cube editor, and open the Dimension Usage tab.
Step 2: Configure a relationship between the Customer dimension and the Sales measure group. Use Day as the granularity.
From scenario: The SalesAnalysis cube contains a fact table named CoffeeSale loaded from a table named FactSale in the data warehouse. The time granularity within the cube is 15 minutes. The cube is processed every night at 23:00. You determine that the fact table cannot be fully processed in the expected time. Users have reported slow query response times.
Step 3: Reprocess the cube.
Step 4: Deploy the project changes.
Q16. - (Topic 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:
You need to implement the KPI based on the Status expression. Solution: You design the following solution:
Does the solution meet the goal?
A. Yes
B. No
Answer: B
Q17. - (Topic 4)
Note: This question is part of a series of questions that use the same or similar answer choices. An answer choice may be correct for more than one question in the series. Each question is independent of the other questions in this series. Information and details provided in a question apply only to that question.
You have a Microsoft SQL Server Analysis Services (SSAS) instance that is configured to use multidimensional mode. You create the following cube:
Users need to be able to analyze sales by color.
You need to create a dimension that contains all of the colors for products sold by the company.
Which relationship type should you use between the InternetSales table and the new dimension?
A. no relationship
B. regular
C. fact
D. referenced
E. many-to-many
F. data mining
Answer: B
Explanation:
A regular dimension relationship between a cube dimension and a measure group exists when the key column for the dimension is joined directly to the fact table.
References: https://docs.microsoft.com/en-us/sql/analysis-services/multidimensional-models-olap-logical-cube-objects/dimension-relationships
Q18. - (Topic 3)
A database named DB2 uses the InMemory query mode. Users frequently run the following query:
You need to ensure no users see the PriorYearSales measure in the field list for the Sales table.
What should you do?
A. Create a perspective, and ensure that the PriorYearSales measure is not added to the perspective. Ensure that users connect to the model by using the perspective.
B. Set the Display Folder property for PriorYearSales toHidden.
C. Remove the PriorYearSales measure from the default field set of the Sales table.
D. Create a role using Read permissions, and define a DAX expression to filter out the PriorYearSales measure. Add all users to the role.
Answer: A
Explanation:
Using perspectives in the data model might help you expose a subset of tables, columns, and measures that are useful for a particular type of analysis.
Usually, every user needs only a subset of data you create, and showing him or her the model through perspectives can offer a better user experience.
From scenario; The PriorYearSales measure is referenced by other measures, and is not intended to be analyzed directly by users.
References: Microsoft SQL Server 2012 Analysis Services, The BISM Tabular Model, Microsoft Press (July 2012), page 305
Q19. - (Topic 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 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: Enable Buffer Cache Extensions. Does the solution meet the goal?
A. Yes
B. No
Answer: B
Explanation:
In this scenario we would need both Buffer Cache Extensions and SSD.
The buffer pool extension provides the seamless integration of a nonvolatile random access memory (that is, solid-state drive) extension to the Database Engine buffer pool to
significantly improve I/O throughput.
References:https://docs.microsoft.com/en-us/sql/database-engine/configure-windows/buffer-pool-extension
Q20. - (Topic 1)
You need to configure the server to optimize the afternoon report generation based on the OrderAnalysis cube.
Which property should you configure?
A. LowMemoryLimit
B. VertiPaqPagingPolicy
C. TotalMemoryLimit
D. VirtualMemoryLimit
Answer: A
Explanation:
LowMemoryLimit: For multidimensional instances, a lower threshold at which the server first begins releasing memory allocated to infrequently used objects.
From scenario: Reports that are generated based on data from the OrderAnalysis cube take more time to complete when they are generated in the afternoon each day. You examine the server and observe that it is under significant memory pressure.