Deck 13: Business Intelligence and Data Warehouses
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Deck 13: Business Intelligence and Data Warehouses
1
Multidimensional data analysis techniques include advanced computational functions.
True
2
Master data management's main goal is to provide a partial and segmented definition of all data within an organization.
False
3
Normalizing fact tables improves data access performance and saves data storage space.
False
4
Queries against operational data typically are broad in scope and high in complexity.
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5
Relational data warehouses use multidimensional data schema support to handle multidimensional data.
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6
In the prognosis phase, the data-mining findings are used to predict future behavior and forecast business outcomes.
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7
Business intelligence (BI) architecture is composed of data, people, processes, technology, and the management of such components.
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8
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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9
Explanatory analytics employs mathematical and statistical algorithms, neural networks, artificial intelligence, and other advanced modeling tools to create actionable predictive models based on available data.
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10
Decision support data are a snapshot of the operational data at a given point in time.
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11
Operational data and decision support data serve the same purpose.
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12
A star schema is designed to optimize data query operations rather than data update operations.
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13
The data warehouse development life cycle differs from classical systems development.
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14
To provide better performance, some OLAP systems merge data warehouse and data mart approaches by storing small extracts of the data warehouse at end-user workstations.
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15
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.
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16
Data warehouse data are organized and summarized by table, such as CUSTOMER and ADDRESS.
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17
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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18
Business intelligence tools use the data warehouse data as the raw materials for data analytics to generate business knowledge.
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19
A data store is used by data analysts to create queries that access the database.
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20
A data warehouse designer must define common business dimensions that will be used by a data analyst to narrow a search, group information, or describe attributes.
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21
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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22
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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23
_____ 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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24
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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25
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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26
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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27
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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28
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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29
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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30
Operational data are commonly stored in many tables, and the stored data represent 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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31
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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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
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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34
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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35
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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36
ROLAP and MOLAP vendors are working toward the integration of their respective solutions within a unified decision support framework.
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37
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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38
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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39
A 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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40
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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41
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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42
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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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
Data _____ tools are tools that provide advanced statistical analysis to uncover problems and opportunities hidden within business data.
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45
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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46
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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47
_____ 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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48
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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49
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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50
Decision support data tend 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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51
functionality ranges from simple data gathering and transformation to very complex data analysis and presentation.
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52
_____ 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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53
A data _____ is a small, single-subject data warehouse subset that provides decision support to a small group of people.
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54
_____ 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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55
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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56
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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57
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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58
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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59
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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60
is a read-only database optimized for data analysis and query processing.
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61
_____ are numeric measurements (values) that represent a specific business aspect or activity.
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62
_____ online analytical processing provides OLAP functionality by using relational databases and familiar relational query tools to store and analyze multidimensional data.
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63
The most distinctive characteristic of modern OLAP tools is their capacity for _____ analysis.
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64
_____ are qualifying characteristics that provide additional perspectives to a given fact.
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65
The hierarchy provides the capability to perform drill-down and roll-up searches in a data warehouse.
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66
What is the difference between decision support data and operational data from the point of view of data analyst?
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67
OLAP systems are designed to use both operational and data _____ data.
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68
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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69
To deliver efficient decision support, OLAP tools must have advanced data features.
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70
What is data visualization? Name different techniques of data visualization.
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71
Describe the use of SQL in relation to ROLAP.
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72
_____ 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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73
What is the ROLLUP extension to the GROUP BY clause? Provide the syntax for this extension.
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74
Explain the concept of data analytics. What are the various tools of data analytics?
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75
_____ makes a copy of a table and places it in a different location to improve access time.
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