Advanced Database Management Test Questions
Course Introduction
Advanced Database Management delves into sophisticated database concepts and technologies beyond foundational principles. The course explores advanced topics such as database system architecture, query optimization, transaction management, concurrency control, distributed and NoSQL databases, and data warehousing. Students will analyze and solve complex problems related to large-scale data storage, retrieval, and security, while gaining hands-on experience with contemporary database tools and utilizing advanced SQL techniques. Emphasis is placed on the theoretical underpinnings as well as the practical implementation of scalable, reliable, and efficient database systems in real-world scenarios.
Recommended Textbook
Data Mining A Tutorial Based Primer 1st Edition by Richard Roiger
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14 Chapters
167 Verified Questions
167 Flashcards
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Chapter 1: Data Mining: a First View
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22 Verified Questions
22 Flashcards
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Sample Questions
Q1) Develop a profile for credit card customers likely to carry an average monthly balance of more than $1000.00.
A)supervised learning
B)unsupervised clustering
C)data query
Answer: A
Q2) What attribute similarities group customers holding one or several insurance policies?
A)supervised learning
B)unsupervised clustering
C)data query
Answer: A
Q3) Determine the characteristics of a successful used car salesperson.
A)supervised learning
B)unsupervised clustering
C)data query
Answer: A
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Chapter 2: Data Mining: a Closer Look
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16 Flashcards
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Sample Questions
Q1) Which of the following is a common use of unsupervised clustering?
A) detect outliers
B) determine a best set of input attributes for supervised learning
C) evaluate the likely performance of a supervised learner model
D) determine if meaningful relationships can be found in a dataset
E) All of a,b,c, and d are common uses of unsupervised clustering.
Answer: E
Q2) Which statement is true about prediction problems?
A) The output attribute must be categorical.
B) The output attribute must be numeric.
C) The resultant model is designed to determine future outcomes.
D) The resultant model is designed to classify current behavior.
Answer: C
Q3) The average positive difference between computed and desired outcome values.
A) root mean squared error
B) mean squared error
C) mean absolute error
D) mean positive error
Answer: C
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Page 4
Chapter 3: Basic Data Mining Techniques
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Sample Questions
Q1) This approach is best when we are interested in finding all possible interactions among a set of attributes.
A) decision tree
B) association rules
C) K-Means algorithm
D) genetic learning Computational Questions
Answer: B
Q2) Which statement is true about the decision tree attribute selection process described in your book?
A) A categorical attribute may appear in a tree node several times but a numeric attribute may appear at most once.
B) A numeric attribute may appear in several tree nodes but a categorical attribute may appear at most once.
C) Both numeric and categorical attributes may appear in several tree nodes.
D) Numeric and categorical attributes may appear in at most one tree node.
Answer: B
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Chapter 4: An Excel-Based Data Mining Tool
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Sample Questions
Q1) What is the predictability score for the attribute value medium risk?
A) 0.10
B) 0.20
C) 0.25
D) 0.50
Q2) A dataset of 1000 instances contains one attribute specifying the color of an object. Suppose that 800 of the instances contain the value red for the color attribute. The remaining 200 instances hold green as the value of the color attribute. What is the domain predictability score for color = green?
A) 0.80
B) 0.20
C) 0.60
D) 0.40
Q3) A particular categorical attribute value has a predictiveness score of 1.0 and a predictability score of 0.50. 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
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Sample Questions
Q1) The price of a 12 ounce box of cereal decreases from $3.50 to $3.00. What fraction is used to compute the percent decrease in the price of the cereal?
A) 1/3
B) 1/5
C) 1/6
D) 1/7
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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Sample Questions
Q1) 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
Q2) Which of the following is not a primary function of a decision support system?
A) knowledge discovery
B) reporting data
C) processing data
D) analyzing data
Q3) The purpose of an intersection entity is to replace
A) two one-to-one relationships with a one-to-many relationship
B) two one-to-many relationships with one many-to-many relationship
C) a many-to-many relationship with two one-to-many relationships
D) a one-to-many relationship with two one-to-one relationships
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Chapter 7: Formal Evaluation Techniques
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Sample Questions
Q1) Bootstrapping allows us to
A) choose the same training instance several times.
B) choose the same test set instance several times.
C) build models with alternative subsets of the training data several times.
D) test a model with alternative subsets of the test data several times.
Q2) Unsupervised evaluation can be internal or external. Which of the following is an internal method for evaluating alternative clusterings produced by the K-Means algorithm?
A) Use a production rule generator to compare the rule sets generated for each clustering.
B) Compute and compare class resemblance scores for the clusters formed by each clustering.
C) Compare the sum of squared error differences between instances and their corresponding cluster centers for each alternative clustering.
D) Create and compare the decision trees determined by each alternative clustering.
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Chapter 8: Neural Networks
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Sample Questions
Q1) With a Kohonen network, the output layer node that wins an input instance is rewarded by having
A) a higher probability of winning the next training instance to be presented.
B) its connect weights modified to more closely match those of the input instance.
C) its connection weights modified to more closey match those of its neighbors.
D) neighoring connection weights modified to become less similar to its own connection weights.
Q2) Epochs represent the total number of
A) input layer nodes.
B) passes of the training data through the network.
C) network nodes.
D) passes of the test data through the network.
Q3) A two-layered neural network used for unsupervised clustering.
A) backpropagation network
B) Kohonen network
C) perceptron network
D) aggomerative network
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Chapter 9: Building Neural Networks With Ida
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Sample Questions
Q1) Two classes each of which is represented by the same pair of numeric attributes are linearly separable if
A) at least one of the pairs of attributes shows a curvilinear relationship between the classes.
B) at least one of the pairs of attributes shows a high positive correlation between the classes.
C) at least one of the pairs of attributes shows a high positive correlation between the classes
D) a straight line partitions the instances of the two classes
Q2) 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.
Q3) This type of supervised network architecture does not contain a hidden layer.
A) backpropagation
B) perceptron
C) self-organizing map
D) genetic
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Chapter 10: Statistical Techniques
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Sample Questions
Q1) The probability of a hypothesis before the presentation of evidence.
A) a priori
B) subjective
C) posterior
D) conditional
Q2) 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.
Q3) Regression trees are often used to model _______ data.
A) linear
B) nonlinear
C) categorical
D) symmetrical
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Chapter 11: Specialized Techniques
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10 Flashcards
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Sample Questions
Q1) These can be used to help select a best subset of training data.
A) domain resemblance scores
B) class resemblance scores
C) instance typicality scores
D) standard deviation scores
Q2) The automation of Web site adaptation involves creating and deleting A) index pages
B) cookies
C) pageviews
D) clickstreams
Q3) 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.
Q4) Usage profiles for Web-based personalization contain several A) pageviews
B) clickstreams
C) cookies
D) session files
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Chapter 12: Rule-Based Systems
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15 Flashcards
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Sample Questions
Q1) An internal test of an expert system whose purpose is to determine if the system uses the same reasoning process as the experts) used to build the system.
A) validation
B) verification
C) reliability
D) suitability
Q2) Knowledge acquisition involves one or more experts and a A) systems analyst.
B) knowledge engineer.
C) knowledge designer.
D) systems engineer.
Q3) Developing a rough version of a system that is suitable for testing.
A) validating
B) field reporting
C) verifying
D) prototyping
Q4) Construct a goal tree using the following production rules. Assume the goal is g.
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Chapter 13: Managing Uncertainty in Rule-Based Systems
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Sample Questions
Q1) A fuzzy set is associated with a
A) linguistic variable.
B) certainty factor.
C) hypothesis to be tested.
D) linguistic value.
Q2) This technique is used to determine the height of a rule consequent membership function as determined by the truth of the rule's antecedent condition.
A) fuzzy set union
B) fuzzy set intersection
C) center of gravity
D) clipping
Q3) This process converts a crisp numeric value to its corresponding degree of membership within a fuzzy set.
A) composition
B) inference
C) fuzzification
D) defuzzification
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Chapter 14: Intelligent Agents
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Sample Questions
Q1) An agent's ability to choose its actions in the context of other agents.
A) autonomy
B) cooperation
C) adaptivity
D) coordination
Q2) A fundamental difference between a data mining approach to problem solving and an expert systems approach is
A) the output of an expert system is a set of rules and the output of a data mining technique is a decision tree.
B) a data mining technique builds a model without the aid of a human expert.
C) a model built using a data mining technique can explain how decisions are made but an expert system cannot.
D) an expert system is built using inductive learning whereas a data mining model is built using one or several deductive techniques
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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