Microsoft-Implementing a Data Warehouse with Microsoft SQL Server 2012 Set 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. 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?

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?

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?

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?

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?

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] FROMQuotes 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'. 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?

You are designing an enterprise star schema that will consolidate data from three independent data marts. One of the data marts is hosted on SQL Azure. Most of the dimensions have the same structure and content. However, the geography dimension is slightly different in each data mart. You need to design a consolidated dimensional structure that will be easy to maintain while ensuring that all dimensional data from the three original solutions is represented. What should you do?

You develop and deploy a SQL Server Integration Services (SSIS) package. The package is stored in the file system. You need to execute the package without importing it to the SSIS server. What should you use to execute the package? (Each correct answer presents a complete solution. Choose all that apply.)

You are developing a data flow transformation to merge two data sources. One source contains product data and the other source contains data about the country in which the product was manufactured. Both data sources contain a two-character CountryCode column and both use SQL Server. Both data sources contain an ORDER BY clause to sort the data by the CountryCode column in ascending order. You use a Merge Join transformation to join the data. You need to ensure that the Merge Join transformation works correctly without additional transformations. What should you do? (Each correct answer presents part of the solution. Choose all that apply.)

You are creating a SQL Server Integration Services (SSIS) package to retrieve product data from two different sources. One source is hosted in a SQL Azure database. Each source contains products for different distributors. Products for each distributor source must be combined for insertion into a single product table destination. You need to select the appropriate data flow transformation to meet this requirement. Which two transformations can you use to achieve the goal? Each correct answer presents a complete solution.