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Copyright Cengage Learning. Powered by Cognero.
Page 1
1. Which of the following is NOT one of the four dimensions of the framework for understanding Big Data?
a.
Volume
b.
Vastness
c.
Velocity
d.
Variety
e.
All of these are dimensions of the framework.
ANSWER:
b
RATIONALE:
All of these are part of the dimensions of the framework except for vastness. See 6-1: The
Four Vs: Volume, Velocity, and Variety, and Veracity.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Remember
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.01 -
Identify the four Vs of “big data.”
DATE CREATED:
7/26/2017 12:16 AM
DATE MODIFIED:
7/26/2017 12:26 AM
2. The volume dimension of Big Data refers to
a.
the amount of data being collected.
b.
the pace of data flow, both in and out of a firm.
c.
the consistency of the data collection process.
d.
the diversity of types or forms of data.
e.
the capacity of the storage units on which data is stored.
ANSWER:
a
RATIONALE:
The volume dimension of Big Data refers to the amount of data being collected. See 6-1:
The Four Vs: Volume, Velocity, Variety, and Veracity.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Remember
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.01 -
Identify the four Vs of “big data.”
DATE CREATED:
7/26/2017 12:29 AM
DATE MODIFIED:
7/26/2017 12:32 AM
3. The velocity dimension of Big Data refers to
a.
the amount of data being collected.
b.
the pace of data flow, both in and out of a firm.
c.
the consistency of the data collection process.
d.
the diversity of types or forms of data.
e.
the speed at which data is transmitted through the company network.
ANSWER:
b
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Page 2
RATIONALE:
The velocity dimension of Big Data refers to the pace of data flow, both in and out of a
firm. See 6-1: The Four Vs: Volume, Velocity, Variety, and Veracity.
POINTS:
1
DIFFICULTY:
Easy
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.01 -
Identify the four Vs of “big data.”
DATE CREATED:
7/26/2017 12:32 AM
DATE MODIFIED:
7/26/2017 12:35 AM
4. Businesses like banks and airlines have more data than firms in other industries due to the _______ nature of their
businesses.
a.
seasonal
b.
consumer
c.
stagnant
d.
transactional
e.
services
ANSWER:
d
RATIONALE:
The types of firms have more data than other firms in other industries due to the
transactional nature of their businesses. See 6-3: Marketplace Sources of “Big Data”.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Understand
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.02 -
Contrast structured and unstructured data.
DATE CREATED:
7/26/2017 12:37 AM
DATE MODIFIED:
7/26/2017 12:40 AM
5. The variety dimension of Big Data refers to
a.
the amount of data being collected.
b.
the pace of data flow, both in and out of a firm.
c.
the different storage device capacities available for storing Big Data.
d.
the diversity of types or forms of data.
e.
the variety of businesses that utilize Big Data.
ANSWER:
d
RATIONALE:
The variety dimension of Big Data refers to the diversity or types or forms of data. See 6-
1: The Four Vs: Volume, Velocity, Variety, and Veracity.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Remember
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
Copyright Cengage Learning. Powered by Cognero.
Page 3
LEARNING OBJECTIVES:
6.01 -
Identify the four Vs of “big data.”
DATE CREATED:
7/26/2017 12:41 AM
DATE MODIFIED:
7/26/2017 12:43 AM
6. The most challenging of the four dimensions of Big Data is considered to be
a.
volume
b.
veracity
c.
velocity
d.
variety
e.
All dimensions are equally challenging.
ANSWER:
c
RATIONALE:
Velocity is the most challenging of the four dimensions of Big Data. See 6-1: The Four Vs:
Volume, Velocity, Variety, and Veracity.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Remember
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.01 -
Identify the four Vs of “big data.”
DATE CREATED:
7/26/2017 12:44 AM
DATE MODIFIED:
7/26/2017 12:47 AM
7. Which of the following represent valid sources of Big Data?
a.
Survey responses
b.
Transactions details
c.
Social media references
d.
Location data
e.
All of these are valid types of data.
ANSWER:
e
RATIONALE:
All of these are examples of valid types of data. See 6-1: The Four Vs: Volume, Velocity,
Variety, and Veracity.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Remember
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.01 -
Identify the four Vs of “big data.”
DATE CREATED:
7/26/2017 12:47 AM
DATE MODIFIED:
7/26/2017 3:35 AM
8. Big Data is the process of ____________ large and varied data sets.
a.
capturing, understanding, and distributing
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Page 4
b.
detecting, merging, and analysing
c.
capturing, merging, and analysing
d.
securing, validating, and storing
e.
researching, analyzing, and storing
ANSWER:
c
RATIONALE:
Big data is the process of capturing, merging, and analyzing large and varied data sets. See
6-1: The Four Vs: Volume, Velocity, Variety, and Veracity.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Remember
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.01 -
Identify the four Vs of “big data.”
DATE CREATED:
7/26/2017 12:50 AM
DATE MODIFIED:
7/26/2017 12:53 AM
9. The auto insurance company that uses an app to monitor your driving behavior, collecting thousands of data points in
the process, as part of providing you a policy quote that illustrates which dimension of Big Data?
a.
Volume
b.
Veracity
c.
Velocity
d.
Variety
e.
Value
ANSWER:
a
RATIONALE:
This is an example of the volume dimension of Big Data. See 6-1: The Four Vs: Volume,
Velocity, Variety, and Veracity.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Apply
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.01 -
Identify the four Vs of “big data.”
DATE CREATED:
7/26/2017 12:53 AM
DATE MODIFIED:
7/26/2017 12:56 AM
10. A major restaurant chain wishes to understand consumer sentiment about its brand, so it analyzes social media
comments, receipt survey data, call center conversation summaries from its CRM system, and even reviews from
websites like Urban Spoon. This illustrates which dimension of Big Data?
a.
Volume
b.
Veracity
c.
Velocity
d.
Variety
e.
Value
Copyright Cengage Learning. Powered by Cognero.
Page 5
ANSWER:
d
RATIONALE:
This is an example of the variety dimension of Big Data. See 6-1: The Four Vs: Volume,
Velocity, Variety, and Veracity.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Apply
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.01 -
Identify the four Vs of “big data.”
DATE CREATED:
7/26/2017 12:56 AM
DATE MODIFIED:
7/26/2017 12:59 AM
11. Which of the following does NOT represent the purpose of Big Data or what it is all about?
a.
Understanding current business practices better
b.
Generating more data inputs
c.
Seeking new opportunities to enhance future performance
d.
Establishing the processes to yield insightful outcomes
e.
All of these are consistent with the value of Big Data.
ANSWER:
b
RATIONALE:
All of these represent the purpose of Big Data except generating more data inputs. See 6-
1: The Four Vs: Volume, Velocity, Variety, and Veracity.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Understand
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.01 -
Identify the four Vs of “big data.”
DATE CREATED:
7/26/2017 12:59 AM
DATE MODIFIED:
7/26/2017 1:02 AM
12. A study of hundreds of C-level executives reveals that a fifth dimension of Big Data may be
a.
visibility.
b.
variability.
c.
value.
d.
vision.
e.
vigor.
ANSWER:
c
RATIONALE:
A potential fifth dimension of Big Data is value. See 6-2: The Fifth V: Value.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Understand
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
Copyright Cengage Learning. Powered by Cognero.
Page 6
LEARNING OBJECTIVES:
6.01 -
Identify the four Vs of “big data.”
DATE CREATED:
7/26/2017 1:02 AM
DATE MODIFIED:
7/26/2017 1:05 AM
13. Applications of Big Data in the real world might include which of the following?
a.
Improving customer retention rates
b.
Dealing with negative word of mouth
c.
Creating personalized promotions
d.
All of these are valid applications of Big Data.
e.
Only b and c are valid examples of Big Data.
ANSWER:
d
RATIONALE:
All of the above are valid applications of Big Data. See 6-2: The Fifth V: Value.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Understand
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.01 -
Identify the four Vs of “big data.”
DATE CREATED:
7/26/2017 1:05 AM
DATE MODIFIED:
7/26/2017 1:07 AM
14. Data such as transactional data collected by banks, airlines, and retailers is known as _______ data.
a.
structured
b.
random
c.
collected
d.
unstructured
e.
big
ANSWER:
a
RATIONALE:
Transactional data is known as structured data. See 6-3: Marketplace Sources of “Big
Data”.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Understand
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.02 -
Contrast structured and unstructured data.
DATE CREATED:
7/26/2017 1:08 AM
DATE MODIFIED:
7/26/2017 1:10 AM
15. Data such as blogger reviews or social media comments is known as _____ data.
a.
structured
b.
random
Copyright Cengage Learning. Powered by Cognero.
Page 7
c.
collected
d.
unstructured
e.
big
ANSWER:
d
RATIONALE:
This type of data is known as unstructured data. See 6-3: Marketplace Sources of “Big
Data”.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Understand
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.02 -
Contrast structured and unstructured data.
DATE CREATED:
7/26/2017 1:10 AM
DATE MODIFIED:
7/26/2017 1:13 AM
16. A great source of "Voice of the Customer" (VOC) data is
a.
structured.
b.
mobile.
c.
omni-transactional.
d.
unstructured.
e.
social.
ANSWER:
e
RATIONALE:
VOC data is an example of social data. See 6-3: Marketplace Sources of “Big Data”.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Remember
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.03 - Describe the three sources of “big data” for marketers.
DATE CREATED:
7/26/2017 1:13 AM
DATE MODIFIED:
7/26/2017 1:17 AM
17. A Big Data application of using location-based mobile data from call records is
a.
voice-of-the-customer insights.
b.
a 360-degree view of purchasing patterns.
c.
location-based marketing in real time.
d.
optimized website design.
e.
All of these are enabled by location-based mobile data.
ANSWER:
c
RATIONALE:
An example of using location-based mobile data is location-based marketing in real time.
See 6-3: Marketplace Sources of “Big Data”.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Remember
Copyright Cengage Learning. Powered by Cognero.
Page 8
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.03 - Describe the three sources of “big data” for marketers.
DATE CREATED:
7/26/2017 1:17 AM
DATE MODIFIED:
7/26/2017 1:20 AM
18. Omni-channel retailing recognizes that sources of transaction data that provide purchase insights are available from
a.
brick-and-mortar.
b.
e-commerce.
c.
mobile.
d.
in-store pickup.
e.
Omni-channel retailing could include transaction data from all of the above.
ANSWER:
e
RATIONALE:
All of these represent potential sources of transaction data. See 6-3: Marketplace Sources
of “Big Data”.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Remember
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.03 - Describe the three sources of “big data” for marketers.
DATE CREATED:
7/26/2017 1:20 AM
DATE MODIFIED:
7/26/2017 1:23 AM
19. The value of linking data from several contexts (e.g., omni-channel transactional data) is that it can provide
a.
big data sets for analysis.
b.
a complete view of in-store purchasing behavior.
c.
a complete view of online purchasing behavior.
d.
location mapping data.
e.
a 360-degree view of purchasing patterns.
ANSWER:
e
RATIONALE:
Linking data from several contexts can provide a 360-degree view of purchasing patterns.
See 6-3: Marketplace Sources of “Big Data”.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Remember
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.03 - Describe the three sources of “big data” for marketers.
DATE CREATED:
7/26/2017 1:24 AM
DATE MODIFIED:
7/26/2017 1:27 AM
20. Analytical techniques, when applied to large sets of data, can
a.
describe consumer behavior.
b.
predict future consumption actions.
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Page 9
c.
prescribe courses of action for a firm and its management.
d.
All of these are correct.
e.
None of these are correct.
ANSWER:
d
RATIONALE:
All of these represent potential outcomes of various analytical techniques applied to large
sets of data. See 6-4: Big Data Analysis.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Understand
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.04 -
Compare descriptive, predictive, and prescriptive analytical approaches.
DATE CREATED:
7/26/2017 1:28 AM
DATE MODIFIED:
7/26/2017 1:31 AM
21. Which of the following is NOT considered a descriptive analysis technique?
a.
Data harmonization
b.
Data mining
c.
Data fusion
d.
Neural networks
e.
Visualization
ANSWER:
a
RATIONALE:
All of the above represent a descriptive analysis technique except data harmonization.
See 6-4: Big Data Analysis.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Understand
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.04 -
Compare descriptive, predictive, and prescriptive analytical approaches.
DATE CREATED:
7/26/2017 1:32 AM
DATE MODIFIED:
7/26/2017 1:35 AM
22. The descriptive analysis technique whose goal is to integrate and analyze data from various sources as opposed to
relying on only a single source is known as
a.
data harmonization.
b.
data mining.
c.
data fusion.
d.
neural networks.
e.
visualization.
ANSWER:
c
RATIONALE:
This technique is known as data fusion. See 6-4: Big Data Analysis.
POINTS:
1
Copyright Cengage Learning. Powered by Cognero.
Page 10
DIFFICULTY:
Easy
REFERENCES:
Remember
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.04 -
Compare descriptive, predictive, and prescriptive analytical approaches.
DATE CREATED:
7/26/2017 1:35 AM
DATE MODIFIED:
7/26/2017 1:38 AM
23. The analysis that discovers interesting relationships between items purchased on a single ticket (e.g. in the same
shopping cart) of consumers is
a.
data harmonization.
b.
data mining.
c.
data fusion.
d.
neural networks.
e.
visualization.
ANSWER:
b
RATIONALE:
This type of analysis is known as data mining. See 6-4: Big Data Analysis.
POINTS:
1
DIFFICULTY:
Easy
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.04 -
Compare descriptive, predictive, and prescriptive analytical approaches.
DATE CREATED:
7/26/2017 1:39 AM
DATE MODIFIED:
7/26/2017 1:42 AM
24. Combining real-time sales data with real-time social media mentions in order to better understand consumer
sentiment toward an advertising campaign is an example of which data analysis technique?
a.
Neural networks
b.
Affinity
c.
Data mining
d.
Data fusion
e.
Harmonization
ANSWER:
d
RATIONALE:
This is an example of the data fusion data analysis technique. See 6-4: Big Data Analysis.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Remember
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.04 -
Compare descriptive, predictive, and prescriptive analytical approaches.
DATE CREATED:
7/26/2017 1:42 AM
DATE MODIFIED:
7/26/2017 1:45 AM
Copyright Cengage Learning. Powered by Cognero.
Page 11
25. The analysis technique to consider when searching for nonlinear patterns in data is
a.
visualization.
b.
data fusion.
c.
data modeling.
d.
data mining.
e.
neural networks.
ANSWER:
e
RATIONALE:
This type of analysis technique is known as neural networks. See 6-4: Big Data Analysis.
POINTS:
1
DIFFICULTY:
Easy
REFERENCES:
Remember
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.04 -
Compare descriptive, predictive, and prescriptive analytical approaches.
DATE CREATED:
7/26/2017 1:46 AM
DATE MODIFIED:
7/26/2017 1:49 AM
26. Recognizing patterns, such as identifying fraudulent insurance claims made by otherwise consistent policyholders, is
an example of which kind of descriptive analysis technique?
a.
Data screening
b.
Visualization
c.
Neural networks
d.
Data fusion
e.
Data mining
ANSWER:
c
RATIONALE:
This is an example of the neural networks descriptive analysis technique. See 6-4: Big Data
Analysis.
POINTS:
1
DIFFICULTY:
Easy
QUESTION TYPE:
Multiple Choice
HAS VARIABLES:
False
LEARNING OBJECTIVES:
6.04 -
Compare descriptive, predictive, and prescriptive analytical approaches.
DATE CREATED:
7/26/2017 1:50 AM
DATE MODIFIED:
7/26/2017 1:53 AM
27. Creating charts, graphs, images, diagrams. and even word clouds that allow for better communication of data is an
example of
a.
data fusion.
b.
visualization.
c.
neural networks.
d.
harmonization
e.
data mining.
