Top 350+ Solved Social Media Analytics (SMA) MCQ Questions Answer
Q. _______________ 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
Q. 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
Q. Self-organizing maps are an example of____________
a. Unsupervised learning
b. Supervised learning
c. Reinforcement learning
d. Missing data imputation
Q. 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
Q. The________ centrality measure does not allow for centrality values tobe compared across networks
a. Eigenvector
b. Katz
c. degree
d. None
Q. Eigenvector centrality takes eigen vector of ____________
a. adjacency matrix
b. Neighbouring matrix
c. polling matrix
d. All of Above
Q. 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
Q. __________algorithm is more effective for betweenness centrality.
a. adjacency matrix
b. Dijkstra\s
c. Neighbouring matrix
d. Brandes\
Q. In____________centrality, the intuition is that the more central nodes are, themore quickly they can reach other nodes.
a. Eigenvector
b. Katz
c. Closeness
d. degree
Q. __________provides solution for directed graph problems.
a. Eigenvector
b. Katz
c. PageRank
d. none
Q. ________centrality considers how important nodes are in connecting other nodes.
a. Eigenvector
b. Betweenness
c. degree
d. Katz
Q. Which centrality can not be generalized for group of nodes.
a. Closeness
b. degree
c. betweenness
d. Katz
Q. Transitivity and reciprocity are used in ____________networks.
a. Directed
b. Undirected
c. weighted
d. None
Q. 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