MGTS2202 Lecture MCQs

Benefits of the latest visual analytics tools, such as SAS Visual Analytics, include all of the following EXCEPT
A) there is less demand on IT departments for reports.
B) mobile platforms such as the iPhone are supported by these products.
C) they explore massive amounts of data in hours, not days.
D) it is easier to spot useful patterns and trends in the data.
What is the management feature of a dashboard?
A) operational data that identify what actions to take to resolve a problem
B) summarized dimensional data to analyze the root cause of problems
C) * graphical, abstracted data to monitor key performance metrics
D) summarized dimensional data to monitor key performance metrics
What is the fundamental challenge of dashboard design?
A) ensuring that the organization has access to the latest web browsers
B) ensuring that the organization has the appropriate hardware onsite to support it
C) * ensuring that the required information is shown clearly on a single screen
D) ensuring that users across the organization have access to it
Which type of visualization tool can be very helpful when the intention is to show relative proportions of dollars per department allocated by a university administration?
A) heat map
B) bubble chart
C) *pie chart
D) bullet
The Internet emerged as a new medium for visualization and brought all the following EXCEPT
A) immersive environments for consuming data.
B) * new forms of computation of business logic.
C) worldwide digital distribution of visualization.
D) new graphics displays through PC displays
Who are automated decision systems (ADS) primarily designed for ?
A) strategic level managers making long-term, wide-ranging decisions
B) mid-level managers making tactical decision
C) frontline workers who must make decisions rapidly *
D) operational managers who make shop floor decisions
Revenue management systems modify the prices of products and services dynamically based on (no intuition)
A) intuition, demand, and supply.
B) intuition, competition, and supply.
C) business rules, demand, and supply. *
D) business rules, supply, and intuition.
The MYCIN Expert System was used to diagnose bacterial infections using
A) a simulation model that tried out many options.
B) a set of 500 rules on the subject.*
C) an optimization model.
D) an expert system whose performance was inferior to human experts.
Which of the following is NOT a stage of knowledge engineering?
A) knowledge consolidation*
B) knowledge representation
C) knowledge acquisition
D) knowledge validation
It is difficult to acquire knowledge from experts for all the following reasons EXCEPT
A) experts may not be able to put into words how they conduct their work.
B) testing and refining of knowledge is complex and difficult.
C) many business areas have no identifiable experts.*
D) experts often change their behavior when observed
Which of these are applications of Artificial Intelligence (they all are)
Expert Systems
Game Playing
Computer Vision
Automatic Programming
Speech Understanding
Autonomous Robots
Intelligent Tutoring
Intelligent Agents
Natural Language Processing
Voice Recognition
The development of expert systems is often described as a tedious process. What activities does it typically include? (they all are)
Identifying proper experts
Acquiring knowledge
Selecting the building tools
Coding the system
Evaluating the system
A relatively new approach to supporting decision making is called automated decision systems (ADS), sometimes also known as decision automation systems (DAS). Give a definition of an ADS/DAS in simple terms?
Answer: In simple terms, An ADS is a rule-based system that provides a solution, usually in one functional area, to a specific repetitive managerial problem, usually in one industry.
Give examples of situations in which cluster analysis would be an appropriate data
mining technique.
Cluster algorithms are used when the data records do not have predefined class identifiers (i.e., it is not known to what class a particular record belongs).
Give examples of situations in which association would be an appropriate data mining
technique.
Association rule mining is appropriate to use when the objective is to discover two or more items (or events or concepts) that go together. Students’ answers will differ.
Give examples of situations in which classification would be an appropriate data mining
technique. Give examples of situations in which regression would be an
appropriate data mining technique.
Classification is for prediction that can be based on historical data and relationships,
such as predicting the weather, product demand, or a student’s success in a university.
If what is being predicted is a class label (e.g., “sunny,” “rainy,” or “cloudy”) the
prediction problem is called a classification, whereas if it is a numeric value (e.g.,
temperature such as 68°F), the prediction problem is called a regression.
What are some major data mining methods and algorithms
What is the major difference between cluster analysis and classification
Classification methods learn from previous examples containing inputs and the resulting class labels, and once properly trained they are able to classify future cases.
Clustering partitions pattern records into natural segments or clusters.
What are some major data mining methods and algorith
Generally speaking, data mining tasks can be classified into three main categories: prediction, association, and clustering. Based on the way in which the patterns are extracted from the historical data, the learning algorithms of data mining methods can be classified as either supervised or unsupervised. In contrast, with unsupervised learning the training data includes only the descriptive attributes.
Supverised
ith supervised learning algorithms, the training data includes both the descriptive attributes (i.e.,
independent variables or decision variables) as well as the class attribute (i.e., output variable or result variable).
Un-supervised
In contrast, with unsupervised learning the training data includes only the descriptive attributes.
Which of these SOA definitions do you prefer?
1. A deployed SOA-based architecture will provide a loosely-integrated suite of services that can be used within multiple business domains.
**2. SOA, is an architecture approach that packages functionality as interoperable, loosely-coupled units, or services, made accessible over a network and communicating by passing data independent of operating system or programming language.
3. SOA is an approach to organizing and utilizing distributed data resources operated by independent organizations. The SOA establishes standardized procedures for interactions (services) between these resources. …
Which definition describes Enterprise Application Integration
1. A technology that provides a vehicle for pushing data from source systems into a data warehouse
2. An evolving tool space that promises real-time data integration from a variety of sources
3.A new way of integrating information systems
Which definition describes Enterprise Information Integration
1. A technology that provides a vehicle for pushing data from source systems into a data warehouse
2. An evolving tool space that promises real-time data integration from a variety of sources
3. A new way of integrating information systems
Rank these ETL tool purchase criteria
Price
Learning curve
Ability to read from and write to an unlimited number of data sources/architectures
Automatic capturing and delivery of metadata
A history of conforming to open standards
An easy-to-use interface for the developer and the functional user
What is the decision factory?
1.Executive Dashboard
2.Analytics Play Pen
3.A database
What sort of KPIs might measure customer satisfaction? (order your top 3)
1.Number of bookings per year
2.Star rankings
3. Existence of written feedback on site
4. Repeat visit to site
5. Response to survey
Which of these BI technologies might have been used (your top 3 guesses)?
1.Database Integration (various)
2.Online Analytical Processing
3.Real time Data warehouse
4.Visual Analytics
5.Regular Data warehouse
6.Data mining
7.Other