Deck 16: Data Warehouse Technology and Management

Question 1

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

Answer 1

False
Explanation: Data warehouses are nonvolatile.

Question 2

Use of the ROLLUP operator with a GROUP BY clause is appropriate to summarize columns from independent dimensions.

Answer 2

False
Explanation: Use of the CUBE operator with a GROUP BY clause is appropriate to summarize columns from independent dimensions.

Question 3

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

Answer 3

False
Explanation: With MOLAP, virtual data cubes are dynamically built as needed.

Question 4

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.

Question 5

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

Question 6

In a star schema,dimension tables may violate 3NF but the fact table is usually normalized.

Question 7

The level of detail and amount of data required to engage in data mining is roughly the same as that needed for traditional data warehouse analysis tools.

Question 8

At this time,there is no major relational database vendor that provides support for the multidimensional data model used by data warehouses.

Question 9

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

Question 10

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.

Question 11

Data in a data warehouse is time-variant,which is a critical characteristic for identifying trends,predicting future operations,and setting operating targets.

Question 12

Time-series data in a data cube may complicate the structure of measures,but it decreases the number of dimensions needed and facilitates the computation of many statistical functions.

Question 13

A data warehouse refers to a central data repository where data from operational databases and other sources are integrated,cleaned,and standardized to support decision making.The transformational activities (cleaning,integrating,and standardizing)are essential for achieving benefits.

Question 14

In both query rewriting for materialized views and query modification for traditional views,the query optimizer must evaluate whether the substitution will improve performance over the original query.

Question 15

Data mining refers to the process of discovering implicit patterns in data and using these patterns for business advantage.

Question 16

When N is the number of grouped columns,the result of a ROLLUP operation can be produced by using N)additional SELECT statements connected by a UNION operator.

Question 17

Sparsity in a hypercube indicates the amount of empty cells in the cube,and can be a problem when two or more dimensions are related.

Question 18

The multidimensional data model is becoming dominant for both operational databases and data warehouses.

Question 19

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.

Question 20

Because of its advantages over MOLAP and ROLAP,HOLAP is expected to become the dominant storage engine for data warehouses.

Question 21

Which of the following may result from data warehouse applications?

A) Retention of customers
B) New market identification
C) Inventory cost reduction
D) All of the above

Question 22

In the two-tier data warehouse architecture:

A) Data access and processing are very fast
B) User departments access the external databases
C) User departments access their own data marts
D) User departments access the data warehouse

Question 23

Figure:
Table1
$$\begin{array} { | l | l | l | } \hline \text { Bank product } & \text { Location } & \text { Number-of-customers } \\\hline \text { Checking } & \text { USA } & 1,000,000 \\\hline \text { Checking } & \text { Europe } & 500,000 \\\hline \text { Checking } & \text { SE Asia } & 1,100,000 \\\hline \text { Checking } & \text { India } & 300,000 \\\hline \text { Savings } & \text { USA } & 700,000 \\\hline \text { Savings } & \text { Europe } & 400,000 \\\hline \text { Savings } & \text { SE Asia } & 900,000 \\\hline \text { Savings } & \text { India } & 800,000 \\\hline \text { Mutual funds } & \text { USA } & 300,000 \\\hline \text { Mutual funds } & \text { Europe } & \\\hline \text { Mutual funds } & \text { SE Asia } & 300,000 \\\hline \text { Mutual funds } & \text { India } & 80,000 \\\hline\end{array}$$
Table2
$$\begin{array} { | l | l | l | l | } \hline \text { Location } & \text { Checking } & \text { Savings } & \text { Mutual Funds } \\\hline \text { USA } & 1,000,000 & 700,000 & 300,000 \\\hline \text { Europe } & 500,000 & 400,000 & 200,000 \\\hline \text { SE Asia } & 1,100,000 & 900,000 & 300,000 \\\hline \text { India } & & & \\\hline\end{array}$$
The numeric values in Table2 indicate the number of customers.

-To have the data for Australia in Table2,you would:

A) Add 4 new rows to Table2
B) Add 1 new row to Table2
C) Add 1 new column to Table2
D) Add 1 new row and one new column to Table2

Question 24

What is true of hierarchies in a data cube?

A) Without them, the dimension must contain the most detailed level of data
B) They make it easier to compute aggregates across the dimension
C) Dimensions can have multiple hierarchies
D) All of the above

Question 25

Figure:
Table1
$$\begin{array} { | l | l | l | } \hline \text { Bank product } & \text { Location } & \text { Number-of-customers } \\\hline \text { Checking } & \text { USA } & 1,000,000 \\\hline \text { Checking } & \text { Europe } & 500,000 \\\hline \text { Checking } & \text { SE Asia } & 1,100,000 \\\hline \text { Checking } & \text { India } & 300,000 \\\hline \text { Savings } & \text { USA } & 700,000 \\\hline \text { Savings } & \text { Europe } & 400,000 \\\hline \text { Savings } & \text { SE Asia } & 900,000 \\\hline \text { Savings } & \text { India } & 800,000 \\\hline \text { Mutual funds } & \text { USA } & 300,000 \\\hline \text { Mutual funds } & \text { Europe } & \\\hline \text { Mutual funds } & \text { SE Asia } & 300,000 \\\hline \text { Mutual funds } & \text { India } & 80,000 \\\hline\end{array}$$
Table2
$$\begin{array} { | l | l | l | l | } \hline \text { Location } & \text { Checking } & \text { Savings } & \text { Mutual Funds } \\\hline \text { USA } & 1,000,000 & 700,000 & 300,000 \\\hline \text { Europe } & 500,000 & 400,000 & 200,000 \\\hline \text { SE Asia } & 1,100,000 & 900,000 & 300,000 \\\hline \text { India } & & & \\\hline\end{array}$$
The numeric values in Table2 indicate the number of customers.

-To have the data for Australia in Table1,you would:

A) Add 4 new rows to Table1
B) Add 1 new row to Table1
C) Add 1 new column to Table1
D) Add 1 new row and one new column to Table1

Question 26

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

Question 27

Figure:
Table1
$$\begin{array} { | l | l | l | } \hline \text { Bank product } & \text { Location } & \text { Number-of-customers } \\\hline \text { Checking } & \text { USA } & 1,000,000 \\\hline \text { Checking } & \text { Europe } & 500,000 \\\hline \text { Checking } & \text { SE Asia } & 1,100,000 \\\hline \text { Checking } & \text { India } & 300,000 \\\hline \text { Savings } & \text { USA } & 700,000 \\\hline \text { Savings } & \text { Europe } & 400,000 \\\hline \text { Savings } & \text { SE Asia } & 900,000 \\\hline \text { Savings } & \text { India } & 800,000 \\\hline \text { Mutual funds } & \text { USA } & 300,000 \\\hline \text { Mutual funds } & \text { Europe } & \\\hline \text { Mutual funds } & \text { SE Asia } & 300,000 \\\hline \text { Mutual funds } & \text { India } & 80,000 \\\hline\end{array}$$
Table2
$$\begin{array} { | l | l | l | l | } \hline \text { Location } & \text { Checking } & \text { Savings } & \text { Mutual Funds } \\\hline \text { USA } & 1,000,000 & 700,000 & 300,000 \\\hline \text { Europe } & 500,000 & 400,000 & 200,000 \\\hline \text { SE Asia } & 1,100,000 & 900,000 & 300,000 \\\hline \text { India } & & & \\\hline\end{array}$$
The numeric values in Table2 indicate the number of customers.

-We want to add the following capabilities to Table2: show the data for 3 age groups (20-39,40-60,over 60),3 revenue groups (less than $10,000,$10,000-$30,000,over $30,000)and add a new type of account: Money market.The number of dimensions of Table2 will be:

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

Question 28

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

Question 29

Which one of the following applications/activities is typical of an operational database?

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 products
C) Analyzing customer and sales data over the last two years to establish a correlation between customer purchases and customer age
D) Analyzing this year customer accounts to identify the best customers

Question 30

Which of the following features usually applies to data in a data warehouse?

A) New data supersede old data
B) New data are appended to old data
C) Update anomalies are common
D) Deletion anomalies are common

Question 31

Figure:
Table1
$$\begin{array} { | l | l | l | } \hline \text { Bank product } & \text { Location } & \text { Number-of-customers } \\\hline \text { Checking } & \text { USA } & 1,000,000 \\\hline \text { Checking } & \text { Europe } & 500,000 \\\hline \text { Checking } & \text { SE Asia } & 1,100,000 \\\hline \text { Checking } & \text { India } & 300,000 \\\hline \text { Savings } & \text { USA } & 700,000 \\\hline \text { Savings } & \text { Europe } & 400,000 \\\hline \text { Savings } & \text { SE Asia } & 900,000 \\\hline \text { Savings } & \text { India } & 800,000 \\\hline \text { Mutual funds } & \text { USA } & 300,000 \\\hline \text { Mutual funds } & \text { Europe } & \\\hline \text { Mutual funds } & \text { SE Asia } & 300,000 \\\hline \text { Mutual funds } & \text { India } & 80,000 \\\hline\end{array}$$
Table2
$$\begin{array} { | l | l | l | l | } \hline \text { Location } & \text { Checking } & \text { Savings } & \text { Mutual Funds } \\\hline \text { USA } & 1,000,000 & 700,000 & 300,000 \\\hline \text { Europe } & 500,000 & 400,000 & 200,000 \\\hline \text { SE Asia } & 1,100,000 & 900,000 & 300,000 \\\hline \text { India } & & & \\\hline\end{array}$$
The numeric values in Table2 indicate the number of customers.


-Which of the following statements is true?

A) Table1 contains multidimensional data and Table2 relational data
B) Table1 contains multidimensional data and Table2 multidimensional data
C) Table1 contains relational data and Table2 relational data
D) Table1 contains relational data and Table2 multidimensional data

Question 32

Data mining typically takes place on:

A) Data warehouses with highly aggregated data
B) Data warehouses that include detailed data
C) Operational databases
D) Highly aggregated data marts

Question 33

Which of the following features usually applies to data in a data warehouse?

A) Data are very detailed
B) Data are shown by process orientation
C) There is a high level of data redundancy
D) Data updates are very frequent

Question 34

Figure:
Table1
$$\begin{array} { | l | l | l | } \hline \text { Bank product } & \text { Location } & \text { Number-of-customers } \\\hline \text { Checking } & \text { USA } & 1,000,000 \\\hline \text { Checking } & \text { Europe } & 500,000 \\\hline \text { Checking } & \text { SE Asia } & 1,100,000 \\\hline \text { Checking } & \text { India } & 300,000 \\\hline \text { Savings } & \text { USA } & 700,000 \\\hline \text { Savings } & \text { Europe } & 400,000 \\\hline \text { Savings } & \text { SE Asia } & 900,000 \\\hline \text { Savings } & \text { India } & 800,000 \\\hline \text { Mutual funds } & \text { USA } & 300,000 \\\hline \text { Mutual funds } & \text { Europe } & \\\hline \text { Mutual funds } & \text { SE Asia } & 300,000 \\\hline \text { Mutual funds } & \text { India } & 80,000 \\\hline\end{array}$$
Table2
$$\begin{array} { | l | l | l | l | } \hline \text { Location } & \text { Checking } & \text { Savings } & \text { Mutual Funds } \\\hline \text { USA } & 1,000,000 & 700,000 & 300,000 \\\hline \text { Europe } & 500,000 & 400,000 & 200,000 \\\hline \text { SE Asia } & 1,100,000 & 900,000 & 300,000 \\\hline \text { India } & & & \\\hline\end{array}$$
The numeric values in Table2 indicate the number of customers.

-We want to have the data for the past five years in Table1.This would require:

A) Adding one column
B) Adding one column and 3 rows
C) Adding one column and 15 rows
D) Adding one column and more than 15 rows

Question 35

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

Question 36

The following is typical of many three-tier data warehouses:

A) Data marts are under the control of user departments
B) The data warehouse is under corporate control
C) The data marts and the data warehouse reside on different servers
D) All of the above

Question 37

Figure:
Table1
$$\begin{array} { | l | l | l | } \hline \text { Bank product } & \text { Location } & \text { Number-of-customers } \\\hline \text { Checking } & \text { USA } & 1,000,000 \\\hline \text { Checking } & \text { Europe } & 500,000 \\\hline \text { Checking } & \text { SE Asia } & 1,100,000 \\\hline \text { Checking } & \text { India } & 300,000 \\\hline \text { Savings } & \text { USA } & 700,000 \\\hline \text { Savings } & \text { Europe } & 400,000 \\\hline \text { Savings } & \text { SE Asia } & 900,000 \\\hline \text { Savings } & \text { India } & 800,000 \\\hline \text { Mutual funds } & \text { USA } & 300,000 \\\hline \text { Mutual funds } & \text { Europe } & \\\hline \text { Mutual funds } & \text { SE Asia } & 300,000 \\\hline \text { Mutual funds } & \text { India } & 80,000 \\\hline\end{array}$$
Table2
$$\begin{array} { | l | l | l | l | } \hline \text { Location } & \text { Checking } & \text { Savings } & \text { Mutual Funds } \\\hline \text { USA } & 1,000,000 & 700,000 & 300,000 \\\hline \text { Europe } & 500,000 & 400,000 & 200,000 \\\hline \text { SE Asia } & 1,100,000 & 900,000 & 300,000 \\\hline \text { India } & & & \\\hline\end{array}$$
The numeric values in Table2 indicate the number of customers.

-We want to have the data for the past five years in Table2.This would require:

A) Adding one column
B) Adding one column and 1 row
C) Adding a new dimension
D) Adding 5 columns and 1 row

Question 38

In a bottom-up data warehouse architecture:

A) Once the initial data marts have been built, no new data marts will be created
B) The data marts may ultimately evolve into one data warehouse
C) The data warehouse built from initial data marts does not change over time
D) None of the above

Question 39

When dimension tables are updated,overwriting the old values with the changed data is one way to maintain the historical integrity of the data warehouse.

Question 40

In data warehouse maintenance,the workflow phases of preparation,integration and update are performed in the initial data load,and also in the subsequent periodic refreshments of the data warehouse.

Question 41

Figuer: (Oracle)
CREATE DIMENSION StoreDim
LEVEL StoreID IS Store. StoreID
LEVEL City IS Store. StoreCity
LEVEL State IS Store. StoreState
LEVEL Zap IS Store.StoreZip
LEVEL Nation IS Store. StoreNation
LEVEL DivID IS Division. DivID
HIERARCHY CityRollup (
StoreID CHILD OF
City CHILD OF
State CHILD OF
Nation )
HIERARCHY ZipRollup
StoreID CHILD OF
Zip CHILD OF
State CHILD OF
Nation )
HIERARCHY DivisionRollup (
StoreID CHILD OF
DivID
JOIN KEY Store.DivID REFERENCES DivID )
ATTRIBUTE DivID DETERMINES Division. DivName
ATTRIBUTE DivID DETERMINES Division. DivManager;

-The dimension StoreDim contains how many hierarchies?

A) 1
B) 3
C) 6
D) None of the above

Question 42

An organization that does not want to take the time to develop an EDM sometimes uses a(n)_________________ architecture for data warehousing by modeling one entity at a time and storing them in separate data marts.

Question 43

Figure:
Table1
$$\begin{array} { | l | l | l | } \hline \text { Bank product } & \text { Location } & \text { Number-of-customers } \\\hline \text { Checking } & \text { USA } & 1,000,000 \\\hline \text { Checking } & \text { Europe } & 500,000 \\\hline \text { Checking } & \text { SE Asia } & 1,100,000 \\\hline \text { Checking } & \text { India } & 300,000 \\\hline \text { Savings } & \text { USA } & 700,000 \\\hline \text { Savings } & \text { Europe } & 400,000 \\\hline \text { Savings } & \text { SE Asia } & 900,000 \\\hline \text { Savings } & \text { India } & 800,000 \\\hline \text { Mutual funds } & \text { USA } & 300,000 \\\hline \text { Mutual funds } & \text { Europe } & \\\hline \text { Mutual funds } & \text { SE Asia } & 300,000 \\\hline \text { Mutual funds } & \text { India } & 80,000 \\\hline\end{array}$$
Table2
$$\begin{array} { | l | l | l | l | } \hline \text { Location } & \text { Checking } & \text { Savings } & \text { Mutual Funds } \\\hline \text { USA } & 1,000,000 & 700,000 & 300,000 \\\hline \text { Europe } & 500,000 & 400,000 & 200,000 \\\hline \text { SE Asia } & 1,100,000 & 900,000 & 300,000 \\\hline \text { India } & & & \\\hline\end{array}$$
The numeric values in Table2 indicate the number of customers.

-We want to add the following capabilities to Table2: show the data for 3 age groups (20-39,40-60,over 60),3 revenue groups (less than $10,000,$10,000-$30,000,over $30,000)and add a new type of account: Money market.The Types of account are either government-insured (checking,savings)or non-government-insured (Money Market,Mutual Funds).This latest requirement creates:

A) Sparsity
B) Classes
C) Hierarchies
D) All of the above

Question 44

Figure:
Table1
$$\begin{array} { | l | l | l | } \hline \text { Bank product } & \text { Location } & \text { Number-of-customers } \\\hline \text { Checking } & \text { USA } & 1,000,000 \\\hline \text { Checking } & \text { Europe } & 500,000 \\\hline \text { Checking } & \text { SE Asia } & 1,100,000 \\\hline \text { Checking } & \text { India } & 300,000 \\\hline \text { Savings } & \text { USA } & 700,000 \\\hline \text { Savings } & \text { Europe } & 400,000 \\\hline \text { Savings } & \text { SE Asia } & 900,000 \\\hline \text { Savings } & \text { India } & 800,000 \\\hline \text { Mutual funds } & \text { USA } & 300,000 \\\hline \text { Mutual funds } & \text { Europe } & \\\hline \text { Mutual funds } & \text { SE Asia } & 300,000 \\\hline \text { Mutual funds } & \text { India } & 80,000 \\\hline\end{array}$$
Table2
$$\begin{array} { | l | l | l | l | } \hline \text { Location } & \text { Checking } & \text { Savings } & \text { Mutual Funds } \\\hline \text { USA } & 1,000,000 & 700,000 & 300,000 \\\hline \text { Europe } & 500,000 & 400,000 & 200,000 \\\hline \text { SE Asia } & 1,100,000 & 900,000 & 300,000 \\\hline \text { India } & & & \\\hline\end{array}$$
The numeric values in Table2 indicate the number of customers.

-We have added the following capability to Table2: showing the data for 3 age groups (20-39,40-60,over 60)as a third dimension.Showing the data for age group 40-60 is called:

A) Slice
B) Dice
C) Drill-Down
D) Roll-up

Question 45

______________ is the process of discovering implicit data patterns in the data of a data warehouse,and using those patterns for business advantage.

Question 46

Figuer: (Oracle)
CREATE DIMENSION StoreDim
LEVEL StoreID IS Store. StoreID
LEVEL City IS Store. StoreCity
LEVEL State IS Store. StoreState
LEVEL Zap IS Store.StoreZip
LEVEL Nation IS Store. StoreNation
LEVEL DivID IS Division. DivID
HIERARCHY CityRollup (
StoreID CHILD OF
City CHILD OF
State CHILD OF
Nation )
HIERARCHY ZipRollup
StoreID CHILD OF
Zip CHILD OF
State CHILD OF
Nation )
HIERARCHY DivisionRollup (
StoreID CHILD OF
DivID
JOIN KEY Store.DivID REFERENCES DivID )
ATTRIBUTE DivID DETERMINES Division. DivName
ATTRIBUTE DivID DETERMINES Division. DivManager;

-The dimension StoreDim contains how many levels?

A) 1
B) 3
C) 6
D) None of the above

Question 47

In maintaining a data warehouse,which of the following is not a task in the preparation phase:

A) Extraction
B) Merging
C) Transportation
D) Cleaning

Question 48

A subset of a data warehouse,typically at a department level,that acts as an interface between the end users and the corporate data warehouse is called a(n)___________________.

Question 49

Figure:
Table1
$$\begin{array} { | l | l | l | } \hline \text { Bank product } & \text { Location } & \text { Number-of-customers } \\\hline \text { Checking } & \text { USA } & 1,000,000 \\\hline \text { Checking } & \text { Europe } & 500,000 \\\hline \text { Checking } & \text { SE Asia } & 1,100,000 \\\hline \text { Checking } & \text { India } & 300,000 \\\hline \text { Savings } & \text { USA } & 700,000 \\\hline \text { Savings } & \text { Europe } & 400,000 \\\hline \text { Savings } & \text { SE Asia } & 900,000 \\\hline \text { Savings } & \text { India } & 800,000 \\\hline \text { Mutual funds } & \text { USA } & 300,000 \\\hline \text { Mutual funds } & \text { Europe } & \\\hline \text { Mutual funds } & \text { SE Asia } & 300,000 \\\hline \text { Mutual funds } & \text { India } & 80,000 \\\hline\end{array}$$
Table2
$$\begin{array} { | l | l | l | l | } \hline \text { Location } & \text { Checking } & \text { Savings } & \text { Mutual Funds } \\\hline \text { USA } & 1,000,000 & 700,000 & 300,000 \\\hline \text { Europe } & 500,000 & 400,000 & 200,000 \\\hline \text { SE Asia } & 1,100,000 & 900,000 & 300,000 \\\hline \text { India } & & & \\\hline\end{array}$$
The numeric values in Table2 indicate the number of customers.

-In Table2,we show the "location" as columns and the "type of account" as rows.This is called:

A) Slice
B) Dice
C) Drill-Down
D) Pivot

Question 50

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.

Question 51

Figure:
Table1
$$\begin{array} { | l | l | l | } \hline \text { Bank product } & \text { Location } & \text { Number-of-customers } \\\hline \text { Checking } & \text { USA } & 1,000,000 \\\hline \text { Checking } & \text { Europe } & 500,000 \\\hline \text { Checking } & \text { SE Asia } & 1,100,000 \\\hline \text { Checking } & \text { India } & 300,000 \\\hline \text { Savings } & \text { USA } & 700,000 \\\hline \text { Savings } & \text { Europe } & 400,000 \\\hline \text { Savings } & \text { SE Asia } & 900,000 \\\hline \text { Savings } & \text { India } & 800,000 \\\hline \text { Mutual funds } & \text { USA } & 300,000 \\\hline \text { Mutual funds } & \text { Europe } & \\\hline \text { Mutual funds } & \text { SE Asia } & 300,000 \\\hline \text { Mutual funds } & \text { India } & 80,000 \\\hline\end{array}$$
Table2
$$\begin{array} { | l | l | l | l | } \hline \text { Location } & \text { Checking } & \text { Savings } & \text { Mutual Funds } \\\hline \text { USA } & 1,000,000 & 700,000 & 300,000 \\\hline \text { Europe } & 500,000 & 400,000 & 200,000 \\\hline \text { SE Asia } & 1,100,000 & 900,000 & 300,000 \\\hline \text { India } & & & \\\hline\end{array}$$
The numeric values in Table2 indicate the number of customers.

-We have added the following capability to Table2: showing the data for 3 age groups (20-39,40-60,over 60)as a third dimension.Showing the data for age groups 20-39 and 40-60 is called:

A) Slice
B) Dice
C) Drill-Down
D) Roll-up

Question 52

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

Question 53

Which of the following statements is not true about populating a data warehouse:

A) It involves matching decision support needs with available data
B) It is a matter of simply copying data from various sources
C) Sources of data may be internal or external or both
D) All of the above

Question 54

Figuer: (Oracle)
CREATE DIMENSION StoreDim
LEVEL StoreID IS Store. StoreID
LEVEL City IS Store. StoreCity
LEVEL State IS Store. StoreState
LEVEL Zap IS Store.StoreZip
LEVEL Nation IS Store. StoreNation
LEVEL DivID IS Division. DivID
HIERARCHY CityRollup (
StoreID CHILD OF
City CHILD OF
State CHILD OF
Nation )
HIERARCHY ZipRollup
StoreID CHILD OF
Zip CHILD OF
State CHILD OF
Nation )
HIERARCHY DivisionRollup (
StoreID CHILD OF
DivID
JOIN KEY Store.DivID REFERENCES DivID )
ATTRIBUTE DivID DETERMINES Division. DivName
ATTRIBUTE DivID DETERMINES Division. DivManager;

-The dimension StoreDim gets data from how many tables?

A) 1
B) 2
C) 3
D) None of the above

Question 55

The ATTRIBUTE clause:

A) Defines a functional dependency between a dimension level and a non-source column
B) Specifies a constraint of the dimension
C) Both of the above
D) None of the above

Question 56

Figure:
Table1
$$\begin{array} { | l | l | l | } \hline \text { Bank product } & \text { Location } & \text { Number-of-customers } \\\hline \text { Checking } & \text { USA } & 1,000,000 \\\hline \text { Checking } & \text { Europe } & 500,000 \\\hline \text { Checking } & \text { SE Asia } & 1,100,000 \\\hline \text { Checking } & \text { India } & 300,000 \\\hline \text { Savings } & \text { USA } & 700,000 \\\hline \text { Savings } & \text { Europe } & 400,000 \\\hline \text { Savings } & \text { SE Asia } & 900,000 \\\hline \text { Savings } & \text { India } & 800,000 \\\hline \text { Mutual funds } & \text { USA } & 300,000 \\\hline \text { Mutual funds } & \text { Europe } & \\\hline \text { Mutual funds } & \text { SE Asia } & 300,000 \\\hline \text { Mutual funds } & \text { India } & 80,000 \\\hline\end{array}$$
Table2
$$\begin{array} { | l | l | l | l | } \hline \text { Location } & \text { Checking } & \text { Savings } & \text { Mutual Funds } \\\hline \text { USA } & 1,000,000 & 700,000 & 300,000 \\\hline \text { Europe } & 500,000 & 400,000 & 200,000 \\\hline \text { SE Asia } & 1,100,000 & 900,000 & 300,000 \\\hline \text { India } & & & \\\hline\end{array}$$
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

Question 57

A(n)____________________ is a just-in-time data mart usually built from one operational database in response to a major event.

Question 58

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

Question 59

Figuer: (Oracle)
CREATE DIMENSION StoreDim
LEVEL StoreID IS Store. StoreID
LEVEL City IS Store. StoreCity
LEVEL State IS Store. StoreState
LEVEL Zap IS Store.StoreZip
LEVEL Nation IS Store. StoreNation
LEVEL DivID IS Division. DivID
HIERARCHY CityRollup (
StoreID CHILD OF
City CHILD OF
State CHILD OF
Nation )
HIERARCHY ZipRollup
StoreID CHILD OF
Zip CHILD OF
State CHILD OF
Nation )
HIERARCHY DivisionRollup (
StoreID CHILD OF
DivID
JOIN KEY Store.DivID REFERENCES DivID )
ATTRIBUTE DivID DETERMINES Division. DivName
ATTRIBUTE DivID DETERMINES Division. DivManager;

-The dimension StoreDim gets data from how many columns of the source table Store?

A) 1
B) 5
C) 6
D) None of the above

Question 60

A central repository for summarized and integrated data from operational databases and external sources,used to support decision making is a(n)___________________.

Question 61

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

Question 62

______________ indicates the extent of empty cells in a data cube.

Question 63

A stored view created to provide fast response for queries involving large fact tables is a(n)____________________ view.

Question 64

One of the decision support operations that can be performed on a data cube is the _______________ operation,which allows users to navigate from a specific level of a hierarchical dimension to a more general level.

Question 65

A ___________ is a storage engine that directly stores and manipulates data cubes.___________ engines generally offer the best query performance but place limits on the size of data cubes.

Question 66

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.

Question 67

One of the decision support operations that can be performed on a data cube is the _______________ operation,which allows users to navigate from a more general level of a hierarchical dimension to a more specific level.

Question 68

Numeric values such as unit sales dollars contained in the cells of a data cube are known as ______________.

Question 69

The subjects that data are grouped by in a data cube are known as ____________________.

Question 70

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.

Question 71

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

Question 72

__________________ is a substitution process that replaces references to fact and dimension tables with a materialized view.

Question 73

A multidimensional data model used in relational databases that has multiple fact tables in the center linked to multiple dimension tables,some of which are shared by the fact tables,is known as a(n)_____________________ schema.

Question 74

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.

Question 75

A multidimensional data model used in relational databases that has a fact table in the center linked in a radial manner to multiple dimension tables is known as a(n)_____________________ schema.

Question 76

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

Question 77

A multidimensional data model used in relational databases that has multiple levels of dimension tables related to one or more fact tables is known as a(n)_____________________ schema.

Question 78

In refreshing a data warehouse,________________ change data involves a periodic dump of source data which is compared using a difference operation to the previous dump.