McqMate
These multiple-choice questions (MCQs) are designed to enhance your knowledge and understanding in the following areas: Computer Science Engineering (CSE) , Information Technology Engineering (IT) .
| 101. |
________of data means that the attributes within a given entity are fully dependent on the entire primary key of the entity. |
| A. | Additively |
| B. | Granularity |
| C. | Functional dependency |
| D. | Dimensionality |
| Answer» C. Functional dependency | |
| 102. |
A fact is said to be fully additive if_________. |
| A. | It is additive over every dimension of its dimensionality |
| B. | Additive over at least one but not all of the dimensions |
| C. | Not additive over any dimension |
| D. | None of the above |
| Answer» A. It is additive over every dimension of its dimensionality | |
| 103. |
A fact is said to be partially additive if_______. |
| A. | It is additive over every dimension of its dimensionality |
| B. | Additive over at least one but not all of the dimensions |
| C. | Not additive over any dimension |
| D. | None of the above |
| Answer» B. Additive over at least one but not all of the dimensions | |
| 104. |
A fact is said to be non-additive if_______. |
| A. | It is additive over every dimension of its dimensionality |
| B. | Additive over at least 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 | |
| 105. |
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 | |
| 106. |
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 | |
| 107. |
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 | |
| 108. |
Which of the following is a predictive model? |
| A. | Clustering |
| B. | Regression |
| C. | Summarization |
| D. | Association rules |
| Answer» B. Regression | |
| 109. |
Which of the following is a descriptive model? |
| A. | Classification |
| B. | Regression |
| C. | Sequence discovery |
| D. | Association rules |
| Answer» C. Sequence discovery | |
| 110. |
A_________model identifies patterns or relationships. |
| A. | Descriptive |
| B. | Predictive |
| C. | Regression |
| D. | Time series analysis |
| Answer» A. Descriptive | |
| 111. |
A predictive model makes use of______. |
| A. | Current data. |
| B. | Historical data. |
| C. | Both current and historical data. |
| D. | Assumptions |
| Answer» B. Historical data. | |
| 112. |
______ maps data into predefined groups. |
| A. | Regression |
| B. | Time series analysis |
| C. | Prediction |
| D. | Classification |
| Answer» D. Classification | |
| 113. |
_____ 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 | |
| 114. |
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 | |
| 115. |
In ______ the groups are not predefined. |
| A. | Association rules |
| B. | Summarization |
| C. | Clustering |
| D. | Prediction |
| Answer» C. Clustering | |
| 116. |
Link Analysis is otherwise called as ____. |
| A. | Affinity analysis |
| B. | Association rules |
| C. | Both A & B |
| D. | Prediction |
| Answer» C. Both A & B | |
| 117. |
______ is a the input to KDD. |
| A. | Data |
| B. | Information |
| C. | Query |
| D. | Process |
| Answer» A. Data | |
| 118. |
The output of KDD is ______. |
| A. | Data |
| B. | Information |
| C. | Query |
| D. | Useful information |
| Answer» D. Useful information | |
| 119. |
The KDD process consists of ____steps. |
| A. | Three |
| B. | Four |
| C. | Five |
| D. | Six |
| Answer» C. Five | |
| 120. |
Treating incorrect or missing data is called as________. |
| A. | Selection |
| B. | Preprocessing |
| C. | Transformation |
| D. | Interpretation |
| Answer» B. Preprocessing | |
| 121. |
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 | |
| 122. |
Various visualization techniques are used in_________step of KDD. |
| A. | Selection |
| B. | Transformation |
| C. | Data mining |
| D. | Interpretation |
| Answer» D. Interpretation | |
| 123. |
Extreme values that occur infrequently are called as___________. |
| A. | Outliers |
| B. | Rare values |
| C. | Dimensionality reduction |
| D. | All of the above |
| Answer» A. Outliers | |
| 124. |
Box plot and scatter diagram techniques are_________. |
| A. | Graphical |
| B. | Geometri |
| C. | C Icon-base |
| D. | D Pixel-based |
| Answer» B. Geometri | |
| 125. |
_____ is used to proceed from very specific knowledge to more general information. |
| A. | Induction |
| B. | Compression |
| C. | Approximation |
| D. | Substitution |
| Answer» A. Induction | |
| 126. |
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 | |
| 127. |
______ helps to uncover hidden information about the data. |
| A. | Induction |
| B. | Compression |
| C. | Approximation |
| D. | Summarization |
| Answer» C. Approximation | |
| 128. |
______ are needed to identify training data and desired results. |
| A. | Programmers |
| B. | Designers |
| C. | Users |
| D. | Administrators |
| Answer» C. Users | |
| 129. |
Over fitting 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 | |
| 130. |
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 | |
| 131. |
Incorrect or invalid data is known as _______. |
| A. | Changing data |
| B. | Noisy data |
| C. | Outliers |
| D. | Missing data |
| Answer» B. Noisy data | |
| 132. |
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 | |
| 133. |
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 | |
| 134. |
_________data are noisy and have many missing attribute values. |
| A. | Preprocessed |
| B. | Cleaned |
| C. | Real-worl |
| D. | D Tr |
| Answer» D. D Tr | |
| 135. |
The rise of DBMS occurred in early _______. |
| A. | 1950's |
| B. | 1960's |
| C. | 1970's |
| D. | 1980's |
| Answer» C. 1970's | |
| 136. |
SQL stand for_________. |
| A. | Standard Query Language |
| B. | Structured Query Language |
| C. | Standard Quick List. |
| D. | Structured Query list |
| Answer» B. Structured Query Language | |
| 137. |
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 | |
| 138. |
Reducing the number of attributes to solve the high dimensionality problem is called as_____________. |
| A. | Dimensionality curse |
| B. | Dimensionality reduction |
| C. | Cleaning |
| D. | Over fitting |
| Answer» B. Dimensionality reduction | |
| 139. |
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 | |
| 140. |
_________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 | |
| 141. |
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 | |
| 142. |
Data mining helps in________. |
| A. | Inventory managemen |
| B. | Sales promotion strategies |
| C. | Marketing strategies |
| D. | All of the above |
| Answer» D. All of the above | |
| 143. |
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 | |
| 144. |
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 | |
| 145. |
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 | |
| 146. |
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% | |
| 147. |
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% | |
| 148. |
The left hand side of an association rule is called________. |
| A. | Consequent |
| B. | Onset |
| C. | Antecedent |
| D. | Precedent |
| Answer» C. Antecedent | |
| 149. |
The right hand side of an association rule is called__________. |
| A. | Consequent |
| B. | Onset |
| C. | Antecedent |
| D. | Precedent |
| Answer» A. Consequent | |
| 150. |
Which of the following is not a desirable feature of any efficient algorithm? |
| A. | To reduce number of input operation |
| 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 | |
Done Studing? Take A Test.
Great job completing your study session! Now it's time to put your knowledge to the test. Challenge yourself, see how much you've learned, and identify areas for improvement. Don’t worry, this is all part of the journey to mastery. Ready for the next step? Take a quiz to solidify what you've just studied.