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400+ Data Mining and Data Warehouse Solved MCQs

These multiple-choice questions (MCQs) are designed to enhance your knowledge and understanding in the following areas: Computer Science Engineering (CSE) , Common Topics in Competitive and Entrance exams .

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101.

A fact is said to be partially additive if ___________.

A. it is additive over every dimension of its dimensionality.
B. additive over atleast one but not all of the dimensions.
C. not additive over any dimension.
D. none of the above.
Answer» B. additive over atleast one but not all of the dimensions.
discuss
102.

A fact is said to be non-additive if ___________.

A. it is additive over every dimension of its dimensionality.
B. additive over atleast one but not all of the dimensions.
C. not additive over any dimension.
D. none of the above.
Answer» C. not additive over any dimension.
discuss
103.

Non-additive measures can often combined with additive measures to create new _________.

A. additive measures.
B. non-additive measures.
C. partially additive.
D. all of the above.
Answer» A. additive measures.
discuss
104.

A fact representing cumulative sales units over a day at a store for a product is a _________.

A. additive fact.
B. fully additive fact.
C. partially additive fact.
D. non-additive fact.
Answer» B. fully additive fact.
discuss
105.

____________ of data means that the attributes within a given entity are fully dependent on the entire primary key of the entity.

A. additivity.
B. granularity.
C. functional dependency.
D. dependency.
Answer» C. functional dependency.
discuss
106.

Which of the following is the other name of Data mining?

A. exploratory data analysis.
B. data driven discovery.
C. deductive learning.
D. all of the above.
Answer» D. all of the above.
discuss
107.

Which of the following is a predictive model?

A. clustering.
B. regression.
C. summarization.
D. association rules.
Answer» B. regression.
discuss
108.

Which of the following is a descriptive model?

A. classification.
B. regression.
C. sequence discovery.
D. association rules.
Answer» C. sequence discovery.
discuss
109.

A ___________ model identifies patterns or relationships.

A. descriptive.
B. predictive.
C. regression.
D. time series analysis.
Answer» A. descriptive.
discuss
110.

A predictive model makes use of ________.

A. current data.
B. historical data.
C. both current and historical data.
D. assumptions.
Answer» B. historical data.
discuss
111.

____________ maps data into predefined groups.

A. regression.
B. time series analysis
C. prediction.
D. classification.
Answer» D. classification.
discuss
112.

__________ is used to map a data item to a real valued prediction variable.

A. regression.
B. time series analysis.
C. prediction.
D. classification.
Answer» B. time series analysis.
discuss
113.

In ____________, the value of an attribute is examined as it varies over time.

A. regression.
B. time series analysis.
C. sequence discovery.
D. prediction.
Answer» B. time series analysis.
discuss
114.

In ________ the groups are not predefined.

A. association rules.
B. summarization.
C. clustering.
D. prediction.
Answer» C. clustering.
discuss
115.

Link Analysis is otherwise called as ___________.

A. affinity analysis.
B. association rules.
C. both a & b.
D. prediction.
Answer» C. both a & b.
discuss
116.

_________ is a the input to KDD.

A. data.
B. information.
C. query.
D. process.
Answer» A. data.
discuss
117.

The output of KDD is __________.

A. data.
B. information.
C. query.
D. useful information.
Answer» D. useful information.
discuss
118.

The KDD process consists of ________ steps.

A. three.
B. four.
C. five.
D. six.
Answer» C. five.
discuss
119.

Treating incorrect or missing data is called as ___________.

A. selection.
B. preprocessing.
C. transformation.
D. interpretation.
Answer» B. preprocessing.
discuss
120.

Converting data from different sources into a common format for processing is called as ________.

A. selection.
B. preprocessing.
C. transformation.
D. interpretation.
Answer» C. transformation.
discuss
121.

Various visualization techniques are used in ___________ step of KDD.

A. selection.
B. transformaion.
C. data mining.
D. interpretation.
Answer» D. interpretation.
discuss
122.

Extreme values that occur infrequently are called as _________.

A. outliers.
B. rare values.
C. dimensionality reduction.
D. all of the above.
Answer» A. outliers.
discuss
123.

Box plot and scatter diagram techniques are _______.

A. graphical.
B. geometric.
C. icon-based.
D. pixel-based.
Answer» B. geometric.
discuss
124.

__________ is used to proceed from very specific knowledge to more general information.

A. induction.
B. compression.
C. approximation.
D. substitution.
Answer» A. induction.
discuss
125.

Describing some characteristics of a set of data by a general model is viewed as ____________

A. induction.
B. compression.
C. approximation.
D. summarization.
Answer» B. compression.
discuss
126.

_____________ helps to uncover hidden information about the data.

A. induction.
B. compression.
C. approximation.
D. summarization.
Answer» C. approximation.
discuss
127.

_______ are needed to identify training data and desired results.

A. programmers.
B. designers.
C. users.
D. administrators.
Answer» C. users.
discuss
128.

Overfitting occurs when a model _________.

A. does fit in future states.
B. does not fit in future states.
C. does fit in current state.
D. does not fit in current state.
Answer» B. does not fit in future states.
discuss
129.

The problem of dimensionality curse involves ___________.

A. the use of some attributes may interfere with the correct completion of a data mining task.
B. the use of some attributes may simply increase the overall complexity.
C. some may decrease the efficiency of the algorithm.
D. all of the above.
Answer» D. all of the above.
discuss
130.

Incorrect or invalid data is known as _________.

A. changing data.
B. noisy data.
C. outliers.
D. missing data.
Answer» B. noisy data.
discuss
131.

ROI is an acronym of ________.

A. return on investment.
B. return on information.
C. repetition of information.
D. runtime of instruction
Answer» A. return on investment.
discuss
132.

The ____________ of data could result in the disclosure of information that is deemed to be confidential.

A. authorized use.
B. unauthorized use.
C. authenticated use.
D. unauthenticated use.
Answer» B. unauthorized use.
discuss
133.

___________ data are noisy and have many missing attribute values.

A. preprocessed.
B. cleaned.
C. real-world.
D. transformed.
Answer» C. real-world.
discuss
134.

The rise of DBMS occurred in early ___________.

A. 1950\s.
B. 1960\s
C. 1970\s
D. 1980\s.
Answer» C. 1970\s
discuss
135.

SQL stand for _________.

A. standard query language.
B. structured query language.
C. standard quick list.
D. structured query list.
Answer» B. structured query language.
discuss
136.

Which of the following is not a data mining metric?

A. space complexity.
B. time complexity.
C. roi.
D. all of the above.
Answer» D. all of the above.
discuss
137.

Reducing the number of attributes to solve the high dimensionality problem is called as ________.

A. dimensionality curse.
B. dimensionality reduction.
C. cleaning.
D. overfitting.
Answer» B. dimensionality reduction.
discuss
138.

Data that are not of interest to the data mining task is called as ______.

A. missing data.
B. changing data.
C. irrelevant data.
D. noisy data.
Answer» C. irrelevant data.
discuss (1)
139.

______ are effective tools to attack the scalability problem.

A. sampling.
B. parallelization
C. both a & b.
D. none of the above.
Answer» C. both a & b.
discuss
140.

Market-basket problem was formulated by __________.

A. agrawal et al.
B. steve et al.
C. toda et al.
D. simon et al.
Answer» A. agrawal et al.
discuss
141.

Data mining helps in __________.

A. inventory management.
B. sales promotion strategies.
C. marketing strategies.
D. all of the above.
Answer» D. all of the above.
discuss
142.

The proportion of transaction supporting X in T is called _________.

A. confidence.
B. support.
C. support count.
D. all of the above.
Answer» B. support.
discuss
143.

The absolute number of transactions supporting X in T is called ___________.

A. confidence.
B. support.
C. support count.
D. none of the above.
Answer» C. support count.
discuss
144.

The value that says that transactions in D that support X also support Y is called ______________.

A. confidence.
B. support.
C. support count.
D. none of the above.
Answer» A. confidence.
discuss
145.

If T consist of 500000 transactions, 20000 transaction contain bread, 30000 transaction contain jam, 10000 transaction contain both bread and jam. Then the support of bread and jam is _______.

A. 2%
B. 20%
C. 3%
D. 30%
Answer» A. 2%
discuss
146.

7 If T consist of 500000 transactions, 20000 transaction contain bread, 30000 transaction contain jam, 10000 transaction contain both bread and jam. Then the confidence of buying bread with jam is _______.

A. 33.33%
B. 66.66%
C. 45%
D. 50%
Answer» D. 50%
discuss
147.

The left hand side of an association rule is called __________.

A. consequent.
B. onset.
C. antecedent.
D. precedent.
Answer» C. antecedent.
discuss
148.

The right hand side of an association rule is called _____.

A. consequent.
B. onset.
C. antecedent.
D. precedent.
Answer» A. consequent.
discuss
149.

Which of the following is not a desirable feature of any efficient algorithm?

A. to reduce number of input operations.
B. to reduce number of output operations.
C. to be efficient in computing.
D. to have maximal code length.
Answer» D. to have maximal code length.
discuss
150.

All set of items whose support is greater than the user-specified minimum support are called as _____________.

A. border set.
B. frequent set.
C. maximal frequent set.
D. lattice.
Answer» B. frequent set.
discuss

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