Quiz 5 Flashcards
(25 cards)
Which of the following is NOT one of the examples of problematic data?
Question content area bottom
Part 1
A.
Elastic data
B.
Inconsistent data
C.
Data may have the wrong granularity
D.
Nonintegrated data
E.
Dirtiness due to the nature of the business activity
A.
Elastic data
What are two problems associated with having too much data in your analysis?
Question content area bottom
Part 1
A.
The high cost of storage devices to hold all of that data and the time it takes to process all of that data
B.
The granularity becomes far too fine, and the attributes become too coarse
C.
Too many attributes and too many data points
D.
Too many attributes and not enough data points
E.
Not enough attributes and too many data points
C.
Too many attributes and too many data points
For BI analysis, data need to represent the proper ________, the proper level of detail.
Question content area bottom
Part 1
A.
coarseness
B.
graininess
C.
summarization
D.
granularity
E.
opacity
D.
granularity
A person’s clicking behaviour on the web is termed their __________________.
A.
webstream
B.
link journey
C.
web tracks
D.
mouse trails
E.
clickstream
E.
clickstream
A phone number of 123-456-7890 and an age of 999 are examples of ________ in a database.
A.
OLTP
B.
incorrect data labels
C.
too many attributes
D.
granularity
E.
dirty data
E.
dirty data
An OLAP report has ______________________________.
A.
measures and averages
B.
measures
C.
dimensions
D.
averages
E.
measures and dimensions
E.
measures and dimensions
Making data easier to understand through graphic displays is often referred to as data _________________.
A.
presentation
B.
visualization
C.
validation
D.
explanation
E.
representation
B.
visualization
The distinguishing characteristic of an OLAP report is that ______________________.
A.
you cannot divide the data into more detail
B.
you cannot drill down
C.
OLAP servers perform OLAP analysis
D.
the user can alter the format of the report
E.
the user cannot alter the format of the report
D.
the user can alter the format of the report
While data may be collected in OLTP, the fact that data may not be used to improve decision making refers to (the) ______________________.
A.
data resource challenge
B.
knowledge challenge
C.
data integrity challenge
D.
information overload
E.
data overload challenge
A.
data resource challenge
________ provides the ability to sum, count, average, and perform other simple arithmetic operations on groups of data.
A.
Online transaction processing (OLTP)
B.
Online database processing (OLDP)
C.
Online record processing (OLRP)
D.
Online data processing (OLDP)
E.
Online analytic processing (OLAP)
E.
Online analytic processing (OLAP)
In most cases, data-mining systems are used to make _________________.
A.
predictions
B.
assessments
C.
decisions
D.
solutions
E.
relationships
A.
predictions
________ systems integrate data from a variety of sources, process that data, and produce and deliver formatted reports to users.
A.
Reporting
B.
Data-mining
C.
XML
D.
RFP
E.
Web service
A.
Reporting
Business intelligence tools include all of the following, except:
A.
SharePoint Server.
B.
Clementine.
C.
SAP.
D.
Sisense.
E.
Crystal Reports
C.
SAP.
______________________ allow multiple decision makers to collaborate, often anonymously and at different times and different locations.
A.
Data-mining systems
B.
Expert systems
C.
Knowledge management systems
D.
Group Decision Support Systems
E.
Reporting systems
D.
Group Decision Support Systems
________ is the application of statistical techniques to find patterns and relationships in data and to predict outcomes.
A.
Data warehousing
B.
Data mining
C.
Databasing
D.
Data depositing
E.
Data marting
B.
Data mining
Which of the following is NOT one of the functions of a data warehouse?
Question content area bottom
Part 1
A.
Obtain the data.
B.
Cleanse the data.
C.
Search the data.
D.
Organize and relate data.
E.
Cataloging data.
C.
Search the data.
A facility for managing an organization’s business intelligence (BI) data is known as a ________.
Question content area bottom
Part 1
A.
data market
B.
data repository
C.
data warehouse
D.
database
E.
data store
C.
data warehouse
A collection of data, smaller than a data warehouse, which addresses the needs of a particular department or functional business area is called a ________.
Question content area bottom
Part 1
A.
data store
B.
data mart
C.
data server
D.
database
E.
data repository
B.
data mart
The application of statistical techniques to find patterns and relationships among data for the purposes of classification and prediction is known as ________.
Question content area bottom
Part 1
A.
data reporting
B.
data mining
C.
data processing
D.
data analysis
E.
data analytics
B.
data mining
________ is the type of data mining in which analysts do not create a model or hypothesis before running the analysis.
Question content area bottom
Part 1
A.
Unsupervised
B.
Exploratory
C.
Supervised
D.
Heuristic
E.
Predictive
A.
Unsupervised
________ is the type of data mining in which analysts develop a model or hypothesis before running the analysis and then apply statistical techniques to data to estimate model parameters.
Question content area bottom
Part 1
A.
Exploratory
B.
Unsupervised
C.
Supervised
D.
Predictive
E.
Heuristic
C.
Supervised
A company that acquires and purchases consumer and other data from public records, retailers, Internet cookie vendors, social media trackers, and other sources and uses it to create business intelligence that it sells to companies and to the government is called a ________.
Question content area bottom
Part 1
A.
data analyst
B.
data broker
C.
data wholesaler
D.
data retailer
E.
data collector
B.
data broker
_____________________ is criticized because it adds to excessive data collection, is expensive, and occasionally results in predictions that do not stand the test of time.
A.
Custer analysis
B.
Regression analysis
C.
Big Data
D.
Unsupervised data mining
E.
Market-basket analysis
C.
Big Data
Most _____________________ techniques are sophisticated, and many are difficult to use. These techniques are valuable to organizations, and some business professionals, especially those in finance and marketing, have become experts in their use.
A.
supervised data mining
B.
data mining
C.
data analysis
D.
data warehousing
E.
unsupervised data mining
B.
data mining
