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Question No : 9 – (Topic 1)  You are reviewing the design of an existing fact table named factSales, which is loaded from a SQL Azure database by a SQL Server Integration Services (SSIS) package each day. The fact table has approximately 1 billion rows and is dimensioned by product, sales
date, and sales time of day.
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The database administrator is concerned about the growth of the database. Users report poor reporting performance against this database. Reporting requirements have recently changed and the only remaining report that uses this fact table reports sales by product name, sale month, and sale year. No other reports will be created against this table. You need to reduce the report processing time and minimize the growth of the database.
What should you do?
A. Partition the table by product type.
B. Create a view over the fact table to aggregate sales by month.
C. Change the granularity of the fact table to month.
D. Create an indexed view over the fact table to aggregate sales by month.
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Answer: C
Question No : 10 – (Topic 1)  You are designing a data warehouse with two fact tables. The first table contains sales per month and the second table contains orders per day. Referential integrity must be enforced declaratively. You need to design a solution that can join a single time dimension to both fact tables. What should you do?
A. Create a time mapping table.
B. Change the level of granularity in both fact tables to be the same.
C. Merge the fact tables.
D. Create a view on the sales table.
Answer: C
Question No : 11 – (Topic 1) You are designing a data warehouse for a software distribution business that stores sales by software title. It stores sales targets by software category. Software titles are classified into subcategories and categories. Each software title is included in only a single software subcategory, and each subcategory is included in only a single category. The data warehouse will be a data source for an Analysis Services cube. The data warehouse contains two fact tables:
factSales, used to record daily sales by software title factTarget, used to record the monthly sales targets by software category Reports must be developed against the warehouse that reports sales by software title, category and subcategory, and sales targets. You need to design the software title dimension. The solution should use as few tables as possible while supporting all the requirements. What should you do?
A. Create three software tables, dimSoftware, dimSoftwareCategory, and dimSoftwareSubcategory and a fourth bridge table that joins software titles to their
appropriate category and subcategory table records with foreign key constraints. Direct the cube developer to use key granularity attributes.
B. Create three software tables, dimSoftware, dimSoftwareCategory, and dimSoftwareSubcategory. Connect factSales to all three tables and connect factTarget to dimSoftwareCategory with foreign key constraints. Direct the cube developer to use key granularity attributes.
C. Create one table, dimSoftware, which contains Software Detail, Category, and Subcategory columns. Connect factSales to dimSoftware with a foreign key constraint. Direct the cube developer to use a non-key granularity attribute for factTarget.
D. Create two tables, dimSoftware and dimSoftwareCategory. Connect factSales to dimSoftware and factTarget to dimSoftwareCategory with foreign key constraints. Direct the cube developer to use key granularity attributes.
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Answer: C
Question No : 12 – (Topic 1)  You are designing a data warehouse hosted on SQL Azure. The data warehouse currently includes the dimUser and dimDistrict dimension tables and the factSales fact table. The dimUser table contains records for each user permitted to run reports against the warehouse; and the dimDistrict table contains information about sales districts. The system is accessed by users from certain districts, as well as by area supervisors and
users from the corporate headquarters. You need to design a table structure to ensure that certain users can see sales data for only certain districts. Some users must be permitted to see sales data from multiple districts.
What should you do?
A. Add a district column to the dimUser table.
B. Partition the factSales table on the district column.
C. Create a userDistrict table that contains primary key columns from the dimUser and dimDistrict tables.
D. For each district, create a view of the factSales table that includes a WHERE clause for the district.
Answer: C
Question No : 13 – (Topic 1)  You are reviewing the design of a customer dimension table in an existing data warehouse hosted on SQL Azure. The current dimension design does not allow the retention of historical changes to customer attributes such as Postcode. You need to redesign the dimension to enable the full historical reporting of changes to multiple customer attributes including Postcode. What should you do?
A. Add StartDate and EndDate columns to the customer dimension.
B. Add an IsCurrent column to the customer dimension.
C. Enable Snapshot Isolation on the data warehouse.
D. Add CurrentValue and PreviousValue columns to the customer dimension.
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Answer: A
You are implementing the indexing strategy for a fact table in a data warehouse. The fact table is named Quotes. The table has no indexes and consists of seven columns:
[ID]
[QuoteDate]
[Open]
[Close]
[High]
[Low]
[Volume]
Each of the following queries must be able to use a columnstore index:
SELECT AVG ([Close]) AS [AverageClose] FROM Quotes WHERE [QuoteDate]
BETWEEN ‘20100101’ AND ‘20101231’.
SELECT AVG([High] – [Low]) AS [AverageRange] FROM Quotes WHERE
[QuoteDate] BETWEEN ‘20100101’ AND ‘20101231’.
SELECT SUM([Volume]) AS [SumVolume] FROM Quotes WHERE [QuoteDate]
BETWEEN ‘20100101’ AND ‘20101231’.
Question No : 14 – (Topic 1)  You need to ensure that the indexing strategy meets the requirements. The strategy must also minimize the number and size of the indexes. What should you do?
A. Create one columnstore index that contains [ID], [Close], [High], [Low], [Volume], and [QuoteDate].
B. Create three coiumnstore indexes:
One containing [QuoteDate] and [Close]
One containing [QuoteDate], [High], and [Low]
One containing [QuoteDate] and [Volume]
C. Create one columnstore index that contains [QuoteDate], [Close], [High], [Low], and [Volume].
D. Create two columnstore indexes:
One containing [ID], [QuoteDate], [Volume], and [Close]
One containing [ID], [QuoteDate], [High], and [Low]
Answer: C

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