MCQOPTIONS
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MCQOPTIONS
This section includes 313 Mcqs, each offering curated multiple-choice questions to sharpen your Information Technology Engineering (IT) knowledge and support exam preparation. Choose a topic below to get started.
| 101. |
To determine individuals joining ________and______algorithms are used. |
| A. | classification, feature selection |
| B. | clustering, feature selection |
| C. | classification, clustering |
| D. | mining, clustering |
| Answer» B. clustering, feature selection | |
| 102. |
Decision tree learning is a __________algorithm |
| A. | classification |
| B. | clustering |
| C. | feature selection |
| D. | mining |
| Answer» B. clustering | |
| 103. |
Posting a photo is an example of __________behaviour. |
| A. | User-User |
| B. | User-Community |
| C. | User-Entity |
| D. | none |
| Answer» D. none | |
| 104. |
Number of friends are ___________proportional to probability of joining community. |
| A. | Directly |
| B. | Indirectly |
| C. | inversely |
| D. | not |
| Answer» B. Indirectly | |
| 105. |
Number of friends of an individual in a community considered as |
| A. | Class variable |
| B. | Class attribute |
| C. | Class vector |
| D. | none |
| Answer» C. Class vector | |
| 106. |
Individuals are inclined toward an activity when their_____are engaged in the same activity. |
| A. | friends |
| B. | foes |
| C. | relatives |
| D. | none |
| Answer» B. foes | |
| 107. |
Communities are mostly______________ |
| A. | explicit |
| B. | implicit |
| C. | compact |
| D. | none |
| Answer» C. compact | |
| 108. |
Befriending is an example of ___________behaviour. |
| A. | User-User |
| B. | User-Community |
| C. | User-Entity |
| D. | none |
| Answer» B. User-Community | |
| 109. |
User-_______ behaviour is content generation |
| A. | User |
| B. | Community |
| C. | Entity |
| D. | none |
| Answer» D. none | |
| 110. |
___________behavior emerges when a population of individuals behave in a similar way |
| A. | Collective |
| B. | individual |
| C. | group |
| D. | none |
| Answer» B. individual | |
| 111. |
Following are types of individual behaviour. |
| A. | User-User |
| B. | User-Community |
| C. | User-Entity |
| D. | All above |
| Answer» E. | |
| 112. |
When discussing individual behavior, Our focus is on _______ individuals |
| A. | two |
| B. | one |
| C. | more than one |
| D. | more |
| Answer» C. more than one | |
| 113. |
Similarity between two nodes can be computed by measuring their_____equivalence |
| A. | Nodal |
| B. | global |
| C. | structural |
| D. | central |
| Answer» D. central | |
| 114. |
__________ centrality assumes that the node with the maximum degree is the most central individual. |
| A. | Eigenvector |
| B. | Katz |
| C. | degree |
| D. | None |
| Answer» D. None | |
| 115. |
Social status theory measures how consistent individuals are in assigning status to their neighbors. |
| A. | true |
| B. | false |
| Answer» B. false | |
| 116. |
Social Balance Theory also known as ______________ |
| A. | Nodal balance theory |
| B. | structural balance theory |
| C. | Network balance theory |
| D. | none |
| Answer» C. Network balance theory | |
| 117. |
Social balance theory says friend/foe relationships are _________ |
| A. | consistent |
| B. | determinant |
| C. | conjugate |
| D. | adjacent |
| Answer» B. determinant | |
| 118. |
__________Clustering estimates how strongly neighbors of a node are themselves connected |
| A. | global |
| B. | local |
| C. | central |
| D. | average |
| Answer» C. central | |
| 119. |
The clustering coeficient analyzes transitivity in an ____________ graph |
| A. | directed |
| B. | undirected |
| C. | both |
| D. | none |
| Answer» C. both | |
| 120. |
___________clustering is computed for the network |
| A. | global |
| B. | local |
| C. | central |
| D. | average |
| Answer» B. local | |
| 121. |
A transitive behavior needs at least___________edges. |
| A. | two |
| B. | three |
| C. | more than one |
| D. | five |
| Answer» C. more than one | |
| 122. |
Reciprocity is a simplified version of ____________ |
| A. | centrality |
| B. | clustering |
| C. | Transitivity |
| D. | classification |
| Answer» D. classification | |
| 123. |
Which centrality can not be generalized for group of nodes. |
| A. | Closeness |
| B. | degree |
| C. | betweenness |
| D. | Katz |
| Answer» E. | |
| 124. |
Transitivity and reciprocity are used in ____________networks. |
| A. | Directed |
| B. | Undirected |
| C. | weighted |
| D. | None |
| Answer» B. Undirected | |
| 125. |
When edges (v1; v2) and (v2; v3) are formed,if (v3; v1) is also formed, it is ____________ |
| A. | reciprocity |
| B. | Transitivity |
| C. | centrality |
| D. | None |
| Answer» C. centrality | |
| 126. |
________centrality considers how important nodes are in connecting other nodes. |
| A. | Eigenvector |
| B. | Betweenness |
| C. | degree |
| D. | Katz |
| Answer» C. degree | |
| 127. |
Eigenvector centrality defined for ____________ graphs |
| A. | directed |
| B. | undirected |
| C. | both |
| D. | none |
| Answer» D. none | |
| 128. |
__________provides solution for directed graph problems. |
| A. | Eigenvector |
| B. | Katz |
| C. | PageRank |
| D. | none |
| Answer» D. none | |
| 129. |
In____________centrality, the intuition is that the more central nodes are, the more quickly they can reach other nodes. |
| A. | Eigenvector |
| B. | Katz |
| C. | Closeness |
| D. | degree |
| Answer» D. degree | |
| 130. |
__________algorithm is more effective for betweenness centrality. |
| A. | adjacency matrix |
| B. | Dijkstra\s |
| C. | Neighbouring matrix |
| D. | Brandes\ |
| Answer» E. | |
| 131. |
When bias term is added to the centrality values for all nodes no matter how they are situated in the network it is called_______ |
| A. | Eigenvector |
| B. | Katz |
| C. | degree |
| D. | None |
| Answer» C. degree | |
| 132. |
Eigenvector centrality takes eigen vector of ____________ |
| A. | adjacency matrix |
| B. | Neighbouring matrix |
| C. | polling matrix |
| D. | All of Above |
| Answer» B. Neighbouring matrix | |
| 133. |
The________ centrality measure does not allow for centrality values to be compared across networks |
| A. | Eigenvector |
| B. | Katz |
| C. | degree |
| D. | None |
| Answer» D. None | |
| 134. |
Some telecommunication company wants to segment their customers into distinct groups in order to send appropriate subscription offers, this is an example of_______ |
| A. | Supervised learning |
| B. | Data extraction |
| C. | Serration |
| D. | Unsupervised learning |
| Answer» E. | |
| 135. |
Self-organizing maps are an example of____________ |
| A. | Unsupervised learning |
| B. | Supervised learning |
| C. | Reinforcement learning |
| D. | Missing data imputation |
| Answer» B. Supervised learning | |
| 136. |
_______________ is a summarization of the general characteristics or features of a target class of data. |
| A. | Data Classification |
| B. | Data discrimination |
| C. | Data selection |
| D. | Data Characterization |
| Answer» E. | |
| 137. |
Strategic value of data mining is____________ |
| A. | cost-sensitive |
| B. | work-sensitive |
| C. | time-sensitive |
| D. | technique-sensitive |
| Answer» D. technique-sensitive | |
| 138. |
Bayesian classifiers is____________ |
| A. | A class of learning algorithm that tries to find an optimum classification of a set of examples using the probabilistic theory. |
| B. | Any mechanism employed by a learning system to constrain the search space of a hypothesis |
| C. | An approach to the design of learning algorithms that is inspired by the fact that when people encounter new situations, they often explain them by reference to familiar experiences, adapting the explanations to fit the new situation. |
| D. | None of these |
| Answer» B. Any mechanism employed by a learning system to constrain the search space of a hypothesis | |
| 139. |
________________ is the process of finding a model that describes and distinguishes data classes or concepts. |
| A. | Data Characterization |
| B. | Data Classification |
| C. | Data discrimination |
| D. | Data selection |
| Answer» C. Data discrimination | |
| 140. |
The out put of KDD is____________ |
| A. | Data |
| B. | Information |
| C. | Query |
| D. | Useful information |
| Answer» E. | |
| 141. |
Following is not a mining technique. |
| A. | Bayesian classification |
| B. | rule-based classifier |
| C. | support vector machines, |
| D. | ObjectRanking |
| Answer» E. | |
| 142. |
The primary idea in___________ is that data mining problems have varying levels of diffculty in different domains |
| A. | clustering |
| B. | classification |
| C. | transfer learning |
| D. | keyword search |
| Answer» D. keyword search | |
| 143. |
Major challenge which arises in the context of social networks is that many such networks are______________ |
| A. | homogeneous |
| B. | heterogeneous |
| C. | unstructured |
| D. | semistructured |
| Answer» C. unstructured | |
| 144. |
Supervised approaches depend on some a-priori knowledge of the data which are___________ |
| A. | Class ids |
| B. | Class labels |
| C. | Classifiers |
| D. | None |
| Answer» C. Classifiers | |
| 145. |
The problem of network clustering is closely related to the traditional problem of ___________ |
| A. | edge partitioning |
| B. | node partitioning |
| C. | graph partitioning |
| D. | vector partitioning |
| Answer» D. vector partitioning | |
| 146. |
A common tool kit used for classification is__________ |
| A. | Bridges |
| B. | Rainbow |
| C. | Naive Bayes |
| D. | TFIDF |
| Answer» C. Naive Bayes | |
| 147. |
Following is not classification algorithm |
| A. | Naive Bayes |
| B. | TFIDF |
| C. | Probabilistic Indexing |
| D. | Indexbased |
| Answer» E. | |
| 148. |
Keyword search on XML data is a simpler problem because_______ |
| A. | XML data is mostly not structured |
| B. | XML data is mostly tree structured |
| C. | XML data is mostly semi structured |
| D. | XML data is mostly fully structured |
| Answer» C. XML data is mostly semi structured | |
| 149. |
____________predicts future trends & behaviors, allowing business managers to make proactive,knowledge-driven decisions. |
| A. | Data warehouse. |
| B. | Datamarts |
| C. | Data mining. |
| D. | Metadata |
| Answer» D. Metadata | |
| 150. |
___________is open source and free visualization tool |
| A. | NodeXL |
| B. | Ruby |
| C. | Pajek |
| D. | Gephi |
| Answer» E. | |