26 02 2015
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Exam Code: 70-458
Exam Name: Transition Your MCTS on SQL Server 2008 to MCSA: SQL Server 2012, Part 2 Exam
Drag and Drop Question
You are developing a SQL Server Integration Services (SSIS) package that imports data into a data warehouse.
You are developing the part of the SSIS package that populates the ProjectDates dimension table.
The business key of the ProjectDates table is the ProjectName column.
The business user has given you the dimensional attribute behavior for each of the four columns in the ProjectDates table:
– ExpectedStartDate – New values should be tracked over time.
– ActualStartDate – New values should not be accepted.
– ExpectedEndDate – New values should replace existing values.
– ActualEndDate – New values should be tracked over time.
You use the SSIS Slowly Changing Dimension Transformation.
You must configure the Change Type value for each source column.
Which settings should you select? (To answer, select the appropriate setting or settings in the answer area.)
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 answer presents a part of the solution. Choose all that apply.)
A. set the appropriate SortKeyPosition properties on the data sources.
B. set the ValidateExternalMetaData property on the Merge Join transformation to True.
C. set the IsSorted property on both data sources.
D. Set the MaxBuffersPerlnput property on the Merge Join transformation to 2.
http://msdn.microsoft.com/en-us/library/ms137653.aspx http://siddhumehta.blogspot.com/2009/05/validateexternalmetadata-property.html http://msdn.microsoft.com/en-us/library/ms135950.aspx
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 transformation types should you use? (Each correct answer presents a complete solution. Choose all that apply.)
B. Merge Join
C. Term Extraction
D. union All
You are designing a SQL Server Integration Services (SSIS) package that uses the Fuzzy Lookup transformation.
The reference data to be used in the transformation does not change.
You need to reuse the Fuzzy Lookup match index to increase performance and reduce maintenance.
What should you do?
A. Select the GenerateAndPersistNewIndex option in the Fuzzy Lookup Transformation Editor.
B. Select the GenerateNewIndex option in the Fuzzy Lookup Transformation Editor.
C. Select the DropExistingMatchlndex option in the Fuzzy Lookup Transformation Editor.
D. Execute the sp_FuzzyLookupTableMaintenanceUninstall stored procedure
E. Execute the sp_FuzzyLookupTableMaintenanceInvoke stored procedure.
You are developing a SQL Server Integration Services (SSIS) package.
You need to design a package to change a variable value during package execution by using the least amount of development effort.
What should you use?
A. Expression task
B. Script task
C. Execute SQL task
D. Execute Process task
E. Term Extraction transformation
You are creating a SQL Server Master Data Services (MDS) model for a company.
The source data for the company is stored in a single table that contains the manager-to-subordinate relationships.
You need to create a hierarchy representing the organizational structure of the company.
Which hierarchy type should you use?
You are completing the installation of the Data Quality Server component of SQL Server Data Quality Services (DQS).
You need to complete the post-installation configuration.
What should you do?
A. Run the Configuration component in the Data Quality Client.
B. Install ADOMD.NET.
C. Run the Data Quality Server Installer.
D. Make the data available for DQS operations.
You are the data steward for a Business Intelligence project.
You must identify duplicate rows stored in a SQL Server table and output discoveries to a CSV file.
A Data Quality Services (DQS) knowledge base has been created to support this project.
You need to produce the CSV file with the least amount of development effort.
What should you do?
A. Create an Integration Services package and use a Data Profiling transform.
B. Create a custom .NET application based on the Knowledgebase class.
C. Create a data quality project.
D. Create a CLR stored procedure based on the Knowledgebase class.
E. Create a Master Data Services (MDS) business rule.
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:
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
– 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?
A. Create one columnstore index that contains [ID], [Close], [High], [Low], [Volume],
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]
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. Join the two fact tables.
B. Merge the fact tables.
C. Create a time dimension that can join to both fact tables at their respective granularity.
D. Create a surrogate key for the time dimension.
With dimensionally modeled star schemas or snowflake schemas, decision support queries follow a typical pattern: the query selects several measures of interest from the fact table, joins the fact rows with one or several dimensions along the surrogate keys, places filter predicates on the business columns of the dimension tables, groups by one or several business columns, and aggregates the measures retrieved from the fact table over a period of time.
The following demonstrates this pattern, which is also sometimes referred to as a star join query:
– select ProductAlternateKey,
– from FactInternetSales Fact
– join DimTime
– on Fact.OrderDateKey = TimeKey
– join DimProduct
– on DimProduct.ProductKey =
– where CalendarYear between 2003 and 2004
– and ProductAlternateKey like ‘BK%’
– group by ProductAlternateKey,CalendarYear
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