Computer Science Capstone Exam Review
Course Introduction
The Computer Science Capstone course serves as a culminating academic experience for students, integrating knowledge and skills acquired throughout their studies. In this course, students work individually or in teams to design, implement, and present a significant software or research project that addresses a real-world problem or explores an advanced computing concept. Emphasizing project management, teamwork, communication, and critical thinking, the course guides students through all phases of project development, including proposal, planning, execution, testing, and presentation. By the end of the course, students are expected to demonstrate proficiency in applying computer science principles and methodologies, and to showcase their projects through formal written reports and oral presentations.
Recommended Textbook
Data Mining A Tutorial Based Primer 1st Edition by Richard Roiger
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14 Chapters
167 Verified Questions
167 Flashcards
Source URL: https://quizplus.com/study-set/3934
Page 2
Chapter 1: Data Mining: a First View
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22 Verified Questions
22 Flashcards
Source URL: https://quizplus.com/quiz/78433
Sample Questions
Q1) A statement to be tested.
A) theory
B) procedure
C) principle
D) hypothesis
Answer: D
Q2) Do meaningful attribute relationships exist in a database containing information about credit card customers?
A)supervised learning
B)unsupervised clustering
C)data query
Answer: B
Q3) Computers are best at learning
A) facts.
B) Concepts.
C) Procedures.
D) Principles.
Answer: B
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3
Chapter 2: Data Mining: a Closer Look
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16 Verified Questions
16 Flashcards
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Sample Questions
Q1) Classification problems are distinguished from estimation problems in that
A) classification problems require the output attribute to be numeric.
B) classification problems require the output attribute to be categorical.
C) classification problems do not allow an output attribute.
D) classification problems are designed to predict future outcome.
Answer: B
Q2) Assume that we have a dataset containing information about 200 individuals. One hundred of these individuals have purchased life insurance. A supervised data mining session has discovered the following rule:
IF age < 30 & credit card insurance = yes
THEN life insurance = yes
Rule Accuracy: 70%
Rule Coverage: 63%
How many individuals in the class life insurance= no have credit card insurance and are less than 30 years old?
A) 63
B) 70
C) 30
D) 27
Answer: D
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Page 4
Chapter 3: Basic Data Mining Techniques
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13 Verified Questions
13 Flashcards
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Sample Questions
Q1) Given a rule of the form IF X THEN Y, rule confidence is defined as the conditional probability that
A) Y is true when X is known to be true.
B) X is true when Y is known to be true.
C) Y is false when X is known to be false.
D) X is false when Y is known to be false.
Answer: A
Q2) A data mining algorithm is unstable if
A) test set accuracy depends on the ordering of test set instances.
B) the algorithm builds models unable to classify outliers.
C) the algorithm is highly sensitive to small changes in the training data.
D) test set accuracy depends on the choice of input attributes.
Answer: C
Q3) The computational complexity as well as the explanation offered by a genetic algorithm is largely determined by the A) fitness function
B) techniques used for crossover and mutation
C) training data
D) population of elements
Answer: A
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Chapter 4: An Excel-Based Data Mining Tool
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12 Verified Questions
12 Flashcards
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Sample Questions
Q1) Which relationship is likely to be seen with an interesting clustering of data instances?
A) The domain resemblance score is greater than the resemblance scores for the individual clusters.
B) The domain resemblance score is equal to the average of the resemblance scores for the individual clusters.
C) The resemblance scores for all formed clusters are greater than zero.
D) The domain resemblance score is less than the resemblance scores for the individual clusters.
Q2) A particular categorical attribute value has a predictiveness score of 0.3 and a predictability score of 0.3. The attribute value is
A) necessary but not sufficient for class membership.
B) sufficient but not necessary for class membership.
C) necessary and sufficient for class membership.
D) neither necessary nor sufficient for class membership.
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Chapter 5: Knowledge Discovery in Databases
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10 Verified Questions
10 Flashcards
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Sample Questions
Q1) The choice of a data mining tool is made at this step of the KDD process.
A) goal identification
B) creating a target dataset
C) data preprocessing
D) data mining
Q2) KDD has been described as the application of ___ to data mining.
A) the waterfall model
B) object-oriented programming
C) the scientific method
D) procedural intuition
Q3) A data normalization technique for real-valued attributes that divides each numerical value by the same power of 10.
A) min-max normalization
B) z-score normalization
C) decimal scaling
D) decimal smoothing
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Chapter 6: The Data Warehouse
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13 Verified Questions
13 Flashcards
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Sample Questions
Q1) A variation of the star schema that allows more than one central fact table.
A) snowflake schema
B) linked strar schema
C) distributed star schema
D) constellation schema
Q2) A mapping that allows the attributes of an OLAP cube to be viewed from varying levels of detail.
A) reflection
B) concept hierarchy
C) rotation
D) semantic network
Q3) Consider the OLAP cube shown above. The vertical arrow points to:
A) region four, travel
B) region two, travel
C) Q2, travel
D) Q1, travel
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Chapter 7: Formal Evaluation Techniques
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13 Verified Questions
13 Flashcards
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Sample Questions
Q1) The hypothesis of no significant difference.
A) nil
B) invalid
C) null
D) void
Q2) The correlation coefficient for two real-valued attributes is -0.85. What does this value tell you?
A) The attributes are not linearly related.
B) As the value of one attribute increases the value of the second attribute also increases.
C) As the value of one attribute decreases the value of the second attribute increases.
D) The attributes show a curvilinear relationship.
Q3) The average squared difference between classifier predicted output and actual output.
A) mean squared error
B) root mean squared error
C) mean absolute error
D) mean relative error
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9
Chapter 8: Neural Networks
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10 Verified Questions
10 Flashcards
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Sample Questions
Q1) The values input into a feed-forward neural network
A) may be categorical or numeric.
B) must be either all categorical or all numeric but not both.
C) must be numeric.
D) must be categorical.
Q2) A two-layered neural network used for unsupervised clustering.
A) backpropagation network
B) Kohonen network
C) perceptron network
D) aggomerative network
Q3) Neural network training is accomplished by repeatedly passing the training data through the network while
A) individual network weights are modified.
B) training instance attribute values are modified.
C) the ordering of the training instances is modified.
D) individual network nodes have the coefficients on their corresponding functional parameters modified.
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10
Chapter 9: Building Neural Networks With Ida
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4 Verified Questions
4 Flashcards
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Sample Questions
Q1) The test set accuracy of a backpropagation neural network can often be improved by
A) increasing the number of epochs used to train the network.
B) decreasing the number of hidden layer nodes.
C) increasing the learning rate.
D) decreasing the number of hidden layers.
Q2) This type of supervised network architecture does not contain a hidden layer.
A) backpropagation
B) perceptron
C) self-organizing map
D) genetic
Q3) The total delta measures the total absolute change in network connection weights for each pass of the training data through a neural network. This value is most often used to determine the convergence of a
A) perceptron network.
B) feed-forward network.
C) backpropagation network.
D) self-organizing network.
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11
Chapter 10: Statistical Techniques
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13 Verified Questions
13 Flashcards
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Sample Questions
Q1) Machine learning techniques differ from statistical techniques in that machine learning methods
A) typically assume an underlying distribution for the data.
B) are better able to deal with missing and noisy data.
C) are not able to explain their behavior.
D) have trouble with large-sized datasets.
Q2) This unsupervised clustering algorithm terminates when mean values computed for the current iteration of the algorithm are identical to the computed mean values for the previous iteration.
A) agglomerative clustering
B) conceptual clustering
C) K-Means clustering
D) expectation maximization
Q3) The leaf nodes of a model tree are
A) averages of numeric output attribute values.
B) nonlinear regression equations.
C) linear regression equations.
D) sums of numeric output attribute values.
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Chapter 11: Specialized Techniques
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10 Verified Questions
10 Flashcards
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Sample Questions
Q1) Which of the following is a fundamental difference between bagging and boosting?
A) Bagging is used for supervised learning. Boosting is used with unsupervised clustering.
B) Bagging gives varying weights to training instances. Boosting gives equal weight to all training instances.
C) Bagging does not take the performance of previously built models into account when building a new model. With boosting each new model is built based upon the results of previous models.
D) With boosting, each model has an equal weight in the classification of new instances. With bagging, individual models are given varying weights.
Q2) The training phase of a textual data mining process involves
A) removing common words from a dictionary.
B) creating an attribute dictionary.
C) determining whether a document is about the topic under investigation.
D) modifying an initially created attribute dictionary.
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13
Chapter 12: Rule-Based Systems
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15 Verified Questions
15 Flashcards
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Sample Questions
Q1) Which of the following problems is not a good candidate for an expert systems solution?
A) bankruptcy prediction
B) determining company advertising strategies
C) solving the national debt crisis
D) providing investment strategies to avoid paying taxes
Q2) The knowledge contained in a knowledge-based system
A) contains the knowledge of one or more human experts.
B) contains the knowledge of one or more intelligent agents.
C) contains the knowledge of exactly one human expert.
D) contains knowledge extracted from one or several sources.
Q3) An expert system with an empty knowledge base.
A) expert system shell
B) inference shell
C) state space
D) goal tree
Q4) Construct a goal tree using the following production rules. Assume the goal is g.
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14
Chapter 13: Managing Uncertainty in Rule-Based Systems
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10 Verified Questions
10 Flashcards
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Sample Questions
Q1) Computing the probability of picking a heart from a deck of 52 cards can be determined using ______ probability technique.
A) an objective
B) an experimental
C) a subjective
D) an inexact
Q2) The probability that a person owns a sports car given that they subscribe to at least one automotive magazine is 40%. We also know that 3% of the adult population subscribes to at least one automotive magazine. Finally, the probability of a person owning a sports car given that they don't subscribe to at least one automotive magazine is 30%. Use this information together with Bayes theorem to compute the probability that a person subscribes to at least one automotive magazine given that they own a sports car.
Q3) Probabilities based on frequency distributions obtained through sampling.
A) objective
B) experimental
C) subjective
D) inexact
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15
Chapter 14: Intelligent Agents
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6 Verified Questions
6 Flashcards
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Sample Questions
Q1) An expert system contains _________ knowledge whereas the knowledge processed by an intelligent agent is _____________
A) personal, general
B) general, personal
C) direct, indirect
D) indirect, direct
Q2) Autonomy is an agent's ability to
A) react to a changing environment.
B) act without direct intervention from others.
C) confer with other agents.
D) react to sensory information received from the environment.
Q3) Data mining techniques and intelligent agents are similar in that they
A) are both used for hypotheses testing.
B) both have the ability to learn.
C) are goal directed.
D) both build models from data.
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Page 16