Deck 13: Business Intelligence and Data Warehouses
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Deck 13: Business Intelligence and Data Warehouses
1
Decision support datais a snapshot of the operational data at a given point in time.
True
2
Data warehouse data are organized and summarized by table, such as CUSTOMER and ADDRESS.
False
3
By default, the fact table's primary key is always formed by combining the superkeys pointing to the dimension tables to which they are related.
False
4
Normalizing fact tables improves data access performance and saves data storage space.
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5
The data warehouse development life cycle differs from classical systems development.
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6
Advanced OLAP feature become more useful when access to them is kept simple.
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7
The ROLLUP extension is used with the GROUP BY clause to generate aggregates by the listed columns, including the last one.
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8
Operational data and decision support data serve the same purpose.
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9
A data warehouse designer must define common business dimensions that will be used by a data analyst to expand a search.
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10
Multidimensional data analysis techniques include advanced computational functions.
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11
Business intelligence is a framework that allows a business to transform data into information, information into knowledge, and knowledge into wisdom.
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12
Master data management's main goal is to provide a partial and segmented definition of all data within an organization.
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13
Queries against operational data typically are broad in scope and high in complexity.
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14
The CUBE extension enables you to get a subtotal for each column listed in the expression, in addition to a grand total for the last column listed.
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15
Relational data warehouses use the star schema design technique to handle multidimensional data.
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16
ROLAP and MOLAP vendors are working toward the integration of their respective solutions within a unified decision support framework.
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17
Business intelligence (BI) architecture is composed of data, people, processes, and technology working together to facilitate and enhance a business's management and governance.
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18
Periodicity, usually expressed as current year only, previous years, or all years, provides information about the time span of the data stored in a table.
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19
A star schema is designed to optimize data query operations rather than data update operations.
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20
A data store is used by data analysts to create queries that access the database.
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21
_____ provide a unified, single point of entry for information distribution.
A) Decision support systems
B) Portals
C) Data warehouses
D) Dashboards
A) Decision support systems
B) Portals
C) Data warehouses
D) Dashboards
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22
The attribute hierarchy provides a top-down data organization that is used for two main purposes: _____ and drill-down/roll-up data analysis.
A) decomposition
B) de-normalization
C) normalization
D) aggregation
A) decomposition
B) de-normalization
C) normalization
D) aggregation
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23
The _____ schema must support complex (non-normalized) data representations.
A) snowflake
B) online analytical processing
C) decision support database
D) multidimensional database
A) snowflake
B) online analytical processing
C) decision support database
D) multidimensional database
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24
Operational data are commonly stored in many tables, and the stored data represents information about a given _____ only.
A) transaction
B) database
C) table
D) concept
A) transaction
B) database
C) table
D) concept
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25
A(n) _____ is optimized for decision support and is generally represented by a data warehouse or a data mart.
A) data store
B) ETL tool
C) data visualization
D) data analysis tool
A) data store
B) ETL tool
C) data visualization
D) data analysis tool
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26
_____ can serve as a test vehicle for companies exploring the potential benefits of data warehouses.
A) Data networks
B) Data marts
C) Data cubes
D) OLAPs
A) Data networks
B) Data marts
C) Data cubes
D) OLAPs
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27
_____ tools focus on the strategic and tactical use of information.
A) Business
B) Relational database management
C) Business intelligence
D) Networking
A) Business
B) Relational database management
C) Business intelligence
D) Networking
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28
The basic star schema has four components: facts, _____, attributes, and attribute hierarchies.
A) keys
B) relationships
C) cubes
D) dimensions
A) keys
B) relationships
C) cubes
D) dimensions
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29
From a data analyst's point of view, decision support data differ from operational data in three main areas: time span, granularity, and _____.
A) usability
B) dimensionality
C) transaction processing
D) sparsity
A) usability
B) dimensionality
C) transaction processing
D) sparsity
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30
In a star schema, attributes are often used to search, filter, or classify _____.
A) tables
B) sales
C) facts
D) dimensions
A) tables
B) sales
C) facts
D) dimensions
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31
In a typical star schema, each dimension record is related to thousands of _____ records.
A) attribute
B) fact
C) key
D) primary
A) attribute
B) fact
C) key
D) primary
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32
_____ are in charge of presenting data to the end user in a variety of ways.
A) Data stores
B) ETL tools
C) Data visualization tools
D) Data analysis tools
A) Data stores
B) ETL tools
C) Data visualization tools
D) Data analysis tools
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33
In business intelligence framework, data are captured from a production system and placed in _____ on a near real-time basis.
A) decision support system
B) portal
C) data warehouse
D) dashboard
A) decision support system
B) portal
C) data warehouse
D) dashboard
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34
In star schema representation, a fact table is related to each dimension table in a _____ relationship.
A) many-to-one (M:1)
B) many-to-many (M:M)
C) one-to many (1:M)
D) one-to-one (1:1)
A) many-to-one (M:1)
B) many-to-many (M:M)
C) one-to many (1:M)
D) one-to-one (1:1)
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35
Bill Inmon and Chuck Kelley created a set of 12 rules to define a(n) _____.
A) data warehouse
B) multidimensional cube
C) OLAP tool
D) star schema
A) data warehouse
B) multidimensional cube
C) OLAP tool
D) star schema
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36
Data _____ implies that all business entities, data elements, data characteristics, and business metrics are described in the same way throughout the enterprise.
A) visualization
B) analytics
C) mining
D) integration
A) visualization
B) analytics
C) mining
D) integration
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37
Fact and dimension tables are related by _____ keys.
A) shared
B) primary
C) foreign
D) linked
A) shared
B) primary
C) foreign
D) linked
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38
Which of the following is a personal analytics vendor for BI applications?
A) IBM
B) Kognitio
C) Netezza
D) MicroStrategy
A) IBM
B) Kognitio
C) Netezza
D) MicroStrategy
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39
A _____ schema is a type of star schema in which dimension tables can have their own dimension tables.
A) snowflake
B) starflake
C) dimension
D) matrix
A) snowflake
B) starflake
C) dimension
D) matrix
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40
Computed or derived facts, at run time, are sometimes called _____ to differentiate them from stored facts.
A) schemas
B) attributes
C) metrics
D) dimensions
A) schemas
B) attributes
C) metrics
D) dimensions
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41
A(n)_____ is a read-only database optimized for data analysis and query processing.
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42
_____ is a term used to describe a comprehensive, cohesive, and integrated set of tools and processes used to capture, collect, integrate, store, and analyze data with the purpose of generating and presenting information used to support business decision making.
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43
The reliance on _____ as the design methodology for relational databases is seen as a stumbling block to its use in OLAP systems.
A) normalization
B) denormalization
C) star schema
D) multidimensional schema
A) normalization
B) denormalization
C) star schema
D) multidimensional schema
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44
An multidimensional database management systems (MDBMS) uses proprietary techniques to store data in _____ n-dimensional arrays.
A) table-like
B) matrix-like
C) network-like
D) cube-like
A) table-like
B) matrix-like
C) network-like
D) cube-like
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45
Decision support data tends to be non-normalized, _____, and pre-aggregated.
A) unique
B) duplicated
C) optimized
D) sorted
A) unique
B) duplicated
C) optimized
D) sorted
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46
_____ splits a table into subsets of rows or columns and places the subsets close to the client computer to improve data access time.
A) Normalization
B) Meta modeling
C) Replication
D) Partitioning
A) Normalization
B) Meta modeling
C) Replication
D) Partitioning
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47
Data visualization has its roots in the _____ sciences, which focus on how the human brain receives, interprets, organizes, and processes information.
A) cognitive
B) developmental
C) social
D) health
A) cognitive
B) developmental
C) social
D) health
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48
_____ functionality ranges from simple data gathering and transformation to very complex data analysis and presentation.
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49
______ is a method or process of government.
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50
Data _____ tools are tools that provide advanced statistical analysis to uncover problems and opportunities hidden within business data.
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51
_____ is a collection of concepts, techniques, and processes for the proper identification, definition, and management of data elements within an organization.
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52
To support a(n) _____ adequately, the DBMS might be required to support advanced storage technologies, and even more importantly, to support multiple-processor technologies, such as a symmetric multiprocessor (SMP) or a massively parallel processor (MPP).
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53
Conceptually, MDBMS end users visualize the stored data as a three-dimensional cube known as a _____.
A) multi-cube
B) database cube
C) data cube
D) hyper cube
A) multi-cube
B) database cube
C) data cube
D) hyper cube
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54
_____ extends SQL so that it can differentiate between access requirements for data warehouse data and operational data.
A) ROLAP
B) OLAP
C) DBMS
D) BI
A) ROLAP
B) OLAP
C) DBMS
D) BI
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55
_____ use web-based technologies to present key business performance indicators or information in a single integrated view, generally using graphics in a clear, concise, and easy to understand manner.
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56
_____ means to decompose data into more atomic components or data at lower levels of aggregation.
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57
A _____ is a dynamic table that not only contains the SQL query command to generate the rows, but also stores the actual rows.
A) SQL view
B) materialized view
C) star schema
D) data cube
A) SQL view
B) materialized view
C) star schema
D) data cube
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58
Which type of data describes numeric facts or measuresthat can becan be counted, ordered, and aggregated?
A) qualitative
B) ordinal
C) nominal
D) quantitative
A) qualitative
B) ordinal
C) nominal
D) quantitative
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59
A _____ index is based on 0 and 1 bits to represent a given condition.
A) logical
B) multidimensional
C) normal
D) bitmapped
A) logical
B) multidimensional
C) normal
D) bitmapped
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60
_____ are quantifiable measurements (numeric or scale based) that assess a company's effectiveness or success in reaching its strategic and operational goals.
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61
A data _____ is a centralized, consolidated database that integrates data derived from the entire organization and from multiple sources with diverse formats.
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62
A data _____ is a small, single-subject data warehouse subset that provides decision support to a small group of people.
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63
_____ are numeric measurements (values) that represent a specific business aspect or activity.
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64
To deliver efficient decision support, OLAP tools must have advanced data _____ features.
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65
What is the difference between decision support data and operational data from the point of view of data analyst?
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66
Describe the use of SQL in relation to ROLAP.
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67
In multidimensional terms, the ability to focus on slices of the cube to perform a more detailed analysis is known as _____.
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68
The most distinctive characteristic of modern OLAP tools is their capacity for _____ analysis.
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69
_____ is a measurement of the density of the data held in the data cube and is computed by dividing the total number of actual values in the cube by the total number of cells in the cube.
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70
What is data visualization? Name different techniques of data visualization.
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71
What is the ROLLUP extension to the GROUP BY clause? Provide the syntax for this extension.
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72
_____ are qualifying characteristics that provide additional perspectives to a given fact.
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73
_____ makes a copy of a table and places it in a different location to improve access time.
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74
The _____ hierarchy providesa top-down data organization that is used for two main purposes: aggregation and drill-down/roll-up data analysis.
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