CHAPTER 13: BUSINESS INTELLIGENCE AND DATA
WAREHOUSES
1. Business intelligence is a framework that allows a business to transform data into information, information into
knowledge, and knowledge into wisdom.
a. True
b. False
2. Business intelligence (BI) architecture is composed of data, people, processes, technology, and the
management of such components.
a. True
b. False
3. A data store is used by data analysts to create queries that access the database.
a. True
b. False
4. Master data management’s main goal is to provide a partial and segmented definition of all data within an
organization.
a. True
b. False
a. True
b. False
6. Decision support data are a snapshot of the operational data at a given point in time.
a. True
b. False
7. Queries against operational data typically are broad in scope and high in complexity.
a. True
b. False
Chapter 13: Business Intelligence and Data Warehouses
8. Data warehouse data are organized and summarized by table, such as CUSTOMER and ADDRESS.
a. True
b. False
9. Relational data warehouses use multidimensional data schema support to handle multidimensional data.
a. True
b. False
10. The data warehouse development life cycle differs from classical systems development.
a. True
b. False
11. 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.
a. True
b. False
12. Normalizing fact tables improves data access performance and saves data storage space.
a. True
b. False
13. 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.
a. True
b. False
14. 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.
a. True
b. False
Chapter 13: Business Intelligence and Data Warehouses
15. Business intelligence tools use the data warehouse data as the raw materials for data analytics to generate
business knowledge.
a. True
b. False
16. In the prognosis phase, the data-mining findings are used to predict future behavior and forecast business
outcomes.
a. True
b. False
17. 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.
a. True
b. False
a. True
b. False
19. 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.
a. True
b. False
20. A star schema is designed to optimize data query operations rather than data update operations.
a. True
b. False
21. ROLAP and MOLAP vendors are working toward the integration of their respective solutions within a unified
decision support framework.
a. True
b. False
Chapter 13: Business Intelligence and Data Warehouses
22. The ROLLUP extension is used with the GROUP BY clause to generate aggregates by the listed columns,
including the last one.
a. True
b. False
23. 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
24. 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
25. _____ provide a unified, single point of entry for information distribution.
a. Decision support systems b. Portals
c. Data warehouses d. Dashboards
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
27. tools focus on the strategic and tactical use of information.
a. Business b. Relational database management
c. Business intelligence d. Networking
28. Which of the following is a personal analytics vendor for BI applications?
a. IBM
b. Kognitio
c. Netezza
d. MicroStrategy
Chapter 13: Business Intelligence and Data Warehouses
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
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
31. The schema must support complex (non-normalized) data representations.
a. snowflake b. online analytical processing
c. decision support database d. multidimensional database
32. 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
33. 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
34. 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
35. The basic star schema has four components: facts, , attributes, and attribute hierarchies.
a. keys b. relationships
c. cubes d. dimensions
36. Computed or derived facts, at run time, are sometimes called to differentiate them from stored facts.
a. schemas b. attributes
c. metrics d. dimensions
Chapter 13: Business Intelligence and Data Warehouses
37. In a star schema, attributes are often used to search, filter, or classify .
a. tables b. sales
c. facts d. dimensions
38. 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)
39. 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
40. Fact and dimension tables are related by keys.
a. shared b. primary
c. foreign d. linked
41. In a typical star schema, each dimension record is related to thousands of records.
a. attribute b. fact
c. key d. primary
42. 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
43. _____ 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
44. 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
Chapter 13: Business Intelligence and Data Warehouses
45. Decision support data tend to be non-normalized, , and pre-aggregated.
a. unique b. duplicated
c. optimized d. sorted
46. 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
47. A index is based on 0 and 1 bits to represent a given condition.
a. logical b. multidimensional
c. normal d. bitmapped
48. 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
49. 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
50. 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
51. 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.
52. functionality ranges from simple data gathering and transformation to very complex data analysis and
presentation.
Chapter 13: Business Intelligence and Data Warehouses
53. is a collection of concepts, techniques, and processes for the proper identification, definition, and
management of data elements within an organization.
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.
55. Data _____ tools are tools that provide advanced statistical analysis to uncover problems and opportunities
hidden within business data.
56. _____ are quantifiable measurements (numeric or scale based) that assess a company’s effectiveness or success
in reaching its strategic and operational goals.
57. 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).
58. is a read-only database optimized for data analysis and query processing.
59. A data _____ is a centralized, consolidated database that integrates data derived from the entire organization
and from multiple sources with diverse formats.
60. A data _____ is a small, single-subject data warehouse subset that provides decision support to a small group
of people.
61. _____ are numeric measurements (values) that represent a specific business aspect or activity.
62. _____ are qualifying characteristics that provide additional perspectives to a given fact.
Chapter 13: Business Intelligence and Data Warehouses
63. In multidimensional terms, the ability to focus on slices of the cube to perform a more detailed analysis is
known as _____.
64. The hierarchy provides the capability to perform drill-down and roll-up searches in a data warehouse.
65. _____ makes a copy of a table and places it in a different location to improve access time.
66. The most distinctive characteristic of modern OLAP tools is their capacity for _____ analysis.
67. To deliver efficient decision support, OLAP tools must have advanced data features.
68. OLAP systems are designed to use both operational and data _____ data.
69. _____ online analytical processing provides OLAP functionality by using relational databases and familiar
relational query tools to store and analyze multidimensional data.
70. _____ 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.
71. What is data visualization? Name different techniques of data visualization.
Chapter 13: Business Intelligence and Data Warehouses
72. What is the difference between decision support data and operational data from the point of view of data
analyst?
73. Explain the concept of data analytics. What are the various tools of data analytics?
74. Describe the use of SQL in relation to ROLAP.
Chapter 13: Business Intelligence and Data Warehouses
75. What is the ROLLUP extension to the GROUP BY clause? Provide the syntax for this extension.