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Microsoft Transition Your MCITP: Business Intelligence Developer 2008 to MCSE: Business Intelligence Sample Questions:

1. You create a materialized reference dimension between a dimension named Regions and a measure group named Orders.
You use a table named Customers as a bridge as shown in the exhibit. (Click the Exhibit button.)

You need to ensure that data is updated to reflect any changes in the relationship. The solution must NOT require that the entire cube be reprocessed.
What should you reprocess?

A) The Regions dimension
B) The Customers dimension
C) The Orders partition
D) The Sales partition

2. DRAG DROP
You are developing a SQL Server Analysis Services (S5AS) cube. The cube consists of a
single measure group.
The measure group consists of one partition that uses MOLAP.
The proactive caching policy has the following requirements: - The cache must be updated when data is changed in the table named tbIOrders. - Changes must be notified through the use of the XML for Analysis (XMLA) NotifyTableChange command. You need to configure the proactive caching policy to meet the requirements.
Which three 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.)

3. You are designing a customer dimension for a multidimensional OLAP (MOLAP) database. The dimension table will contain millions of rows of data.
Customer dimension attributes will come from the following two sources:
- An enterprise resource planning (ERP) system that contains millions of rows of data. Attributes from this source will be updated daily.
- A marketing database that contains thousands of rows of data representing customers' geographic information. Attributes from this source will be updated monthly.
Most report queries against the cube use attributes from both sources. Customers in the
cube dimension will frequently be filtered by the geographic attributes.
You need to design the dimension table and cube dimension to ensure that report queries perform well, and minimize the time required to reprocess cube attributes.
What should you do? (More than one answer choice may achieve the goal. Select the BEST answer.)

A) Create a dimension table for each source. Create one cube dimension that joins the two dimension tables together.
B) Create a dimension table for each source, and a cube dimension for each source.
C) Create one dimension table that combines data from both sources. Create a cube dimension for each source.
D) Create one dimension table that combines data from both sources. Create one cube dimension that references the dimension table.

4. You need to define the trend calculation for the sales performance KPI. Which KPI trend MDX expression should you use?

A) Option A
B) Option C
C) Option B
D) Option D

5. You are modifying a star schema data mart that feeds order data from a Microsoft Azure SQL Database database into a SQL Server Analysis Services (SSAS) cube. The data mart contains two large tables that include flags and indicators for some orders. There are 100 different flag columns, each with 10 different indicator values. Some flags reuse indicators. The tables both have a granularity that matches the fact table.
You have the following requirements:
- Allow users to slice data by all flags and indicators. - Modify the date dimension table to include a surrogate key of a numeric data type and add the surrogate key to the fact table. - Use the most efficient design strategy for cube processing and queries.
You need to modify the schema.
What should you do? (More than one answer choice may achieve the goal. Select the BEST answer.)

A) Define the surrogate key as an INT data type. Create a single fact dimension in each table for its flags and indicators.
B) Define the surrogate key as an INT data type. Combine the distinct flag/indicator combinations into a single dimension.
C) Define the surrogate key as a BIGINT data type. Create a single fact dimension in each table for its flags and indicators.
D) Define the surrogate key as a BIGINT data type. Combine the distinct flag/indicator combinations into a single dimension.

Solutions: