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Figure: Table1  Bank product  Location  Number-of-customers  Checking  USA 1,000,000 Checking  Europe 500,000 Checking  SE Asia 1,100,000 Checking  India 300,000 Savings  USA 700,000 Savings  Europe 400,000 Savings  SE Asia 900,000 Savings  India 800,000 Mutual funds  USA 300,000 Mutual funds  Europe  Mutual funds  SE Asia 300,000 Mutual funds  India 80,000\begin{array} { | l | l | l | } \hline \text { Bank product } & \text { Location } & \text { Number-of-customers } \\\hline \text { Checking } & \text { USA } & 1,000,000 \\\hline \text { Checking } & \text { Europe } & 500,000 \\\hline \text { Checking } & \text { SE Asia } & 1,100,000 \\\hline \text { Checking } & \text { India } & 300,000 \\\hline \text { Savings } & \text { USA } & 700,000 \\\hline \text { Savings } & \text { Europe } & 400,000 \\\hline \text { Savings } & \text { SE Asia } & 900,000 \\\hline \text { Savings } & \text { India } & 800,000 \\\hline \text { Mutual funds } & \text { USA } & 300,000 \\\hline \text { Mutual funds } & \text { Europe } & \\\hline \text { Mutual funds } & \text { SE Asia } & 300,000 \\\hline \text { Mutual funds } & \text { India } & 80,000 \\\hline\end{array} Table2  Location  Checking  Savings  Mutual Funds  USA 1,000,000700,000300,000 Europe 500,000400,000200,000 SE Asia 1,100,000900,000300,000 India \begin{array} { | l | l | l | l | } \hline \text { Location } & \text { Checking } & \text { Savings } & \text { Mutual Funds } \\\hline \text { USA } & 1,000,000 & 700,000 & 300,000 \\\hline \text { Europe } & 500,000 & 400,000 & 200,000 \\\hline \text { SE Asia } & 1,100,000 & 900,000 & 300,000 \\\hline \text { India } & & & \\\hline\end{array} The numeric values in Table2 indicate the number of customers. -We add the following two dimensions to Table2: age groups (20-39,40-60,over 60) and revenue groups (less than $10,000,$10,000-$30,000,over $30,000) .The star schema consists of the following number of tables:


A) 2
B) 3
C) 4
D) 5

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An oper mart is a just-in-time data mart,usually built from one operational database in anticipation or in response to major events such as disasters and new product introductions.

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A(n)___________________ is a multidimensional format sometimes known as a hypercube,because conceptually it could have an infinite number of dimensions.

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The _____________ operator is an extension of the SQL GROUP BY clause that produces all combinations of subtotals in addition to the normal totals.

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The two tiered data warehouse architecture incorporates data marts to provide different departments or sections of the organization with faster access while isolating them from data needed by other user groups.

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Data in a data warehouse is usually normalized to fourth normal form to avoid insert and update anomalies.

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Vendors of relational DBMSs have extended their products with additional features to support operations and storage structures for multidimensional data.These product extensions are collectively known as _________._________ engines support a variety of storage and optimization techniques for summary data retrieval.

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In a(n)______________ data warehouse architecture,operational data are transformed and loaded into the data warehouse,which is accessed directly by the user departments.

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In the data warehouse maintenance process,auditing the data to resolve any data quality problems occurs in both the preparation and integration phases.

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True

HOLAP (Hybrid OLAP)involves both relational and multidimensional data storage,and can combine data from both of these sources for data cube operations.

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One of the decision support operations that can be performed on a data cube is the _______________ operation,in which one or more dimensions are set to specific values and the remaining data cube is displayed.

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Data warehouses have four distinguishing characteristics.Data warehouses are subject-oriented,integrated,time-variant,and volatile.

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In refreshing a data warehouse,________________ change data involves notification from a source system,and typically occurs after a transaction is completed using a trigger.

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When N is the number of grouped columns,the result of a CUBE operation can be produced by using (2*N)- 1 additional SELECT statements connected by a UNION operator.

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ROLAP (Relational OLAP)and MOLAP (Multidimensional OLAP)are similar in that a data cube is actually built and stored for use with queries.

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One of the decision support operations that can be performed on a data cube is the _______________ operation,which rearranges the dimensions in a data cube so that the data can be presented in a more visually appealing order.

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Which one of the following applications/activities is typical of a data warehouse?


A) Updating of the inventory data as the sales occur in the supermarket
B) Comparing last year sales with this year sales to identify the most promising product
C) Looking at sales this month to identify the most valuable salesperson of the month
D) Reviewing last month customer accounts to prepare overdue notices

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B

A(n)_______________________ is a conceptual data model of a data warehouse which defines the structure of the data warehouse and the metadata to access the operational databases and external data sources.

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enterprise...

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In refreshing a data warehouse,________________ change data involves files that record changes or other user activity.

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Which of the following is not an example of application of data mining?


A) Identify likely buyers of luxury cars
B) Identify customers eligible for a defective car recall
C) Target potential customers of banking products
D) Identify best travel products for an age range

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B

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