MCQOPTIONS
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MCQOPTIONS
This section includes 347 Mcqs, each offering curated multiple-choice questions to sharpen your Computer Science Engineering (CSE) knowledge and support exam preparation. Choose a topic below to get started.
| 151. |
__________describes the data contained in the data warehouse |
| A. | relational data |
| B. | operational data |
| C. | meta data |
| D. | informational data |
| Answer» D. informational data | |
| 152. |
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» C. maximal frequent set. | |
| 153. |
Coding is |
| A. | Group of similar objects that differ significantly from other objects |
| B. | Operations on a database to transform or simplify data in order to prepare it for a machine-learning algorithm |
| C. | Symbolic representation of facts or ideas from which information can potentially be extracted |
| D. | None of these |
| Answer» C. Symbolic representation of facts or ideas from which information can potentially be extracted | |
| 154. |
Deep knowledge referred to |
| A. | It is hidden within a database and can only be recovered if one is given certain clues (an example IS encrypted information) |
| B. | The process of executing implicit previously unknown and potentially useful information from dat(A) |
| C. | An extremely complex molecule that occurs in human chromosomes and that carries genetic information in the form of genes. |
| D. | None of these |
| Answer» B. The process of executing implicit previously unknown and potentially useful information from dat(A) | |
| 155. |
Any subset of a frequent set is a frequent set. This is ___________. |
| A. | upward closure property. |
| B. | downward closure property. |
| C. | maximal frequent set. |
| D. | border set. |
| Answer» C. maximal frequent set. | |
| 156. |
The mutation operator ______. |
| A. | recombine the population\s genetic material. |
| B. | introduce new genetic structures in the population. |
| C. | to modify the population\s genetic material. |
| D. | all of the above. |
| Answer» C. to modify the population\s genetic material. | |
| 157. |
n(log n) is referred to |
| A. | A measure of the desired maximal complexity of data mining algorithms |
| B. | A database containing volatile data used for the daily operation of an organization |
| C. | Relational database management system |
| D. | None of these |
| Answer» B. A database containing volatile data used for the daily operation of an organization | |
| 158. |
NLP stands for _________. |
| A. | non language process. |
| B. | nature level program. |
| C. | natural language page. |
| D. | natural language processing. |
| Answer» E. | |
| 159. |
Voronoi diagram |
| A. | A class of graphic techniques used to visualize the contents of a database |
| B. | The division of a certain space into various areas based on guide points. |
| C. | A branch that connects one node to another |
| D. | None of these |
| Answer» C. A branch that connects one node to another | |
| 160. |
The power of self-learning system lies in __________. |
| A. | cost. |
| B. | speed. |
| C. | accuracy. |
| D. | simplicity. |
| Answer» D. simplicity. | |
| 161. |
_______________ is the goal of data mining. |
| A. | to explain some observed event or condition. |
| B. | to confirm that data exists. |
| C. | to analyze data for expected relationships. |
| D. | to create a new data warehouse. |
| Answer» B. to confirm that data exists. | |
| 162. |
Multidimensional model of data warehouse called as_____ |
| A. | data structure |
| B. | table |
| C. | tree |
| D. | data cube |
| Answer» E. | |
| 163. |
A fact table is related to dimensional table as a ___ relationship |
| A. | 1:m |
| B. | m:n |
| C. | m:1 |
| D. | 1:1 |
| Answer» D. 1:1 | |
| 164. |
The left hand side of an association rule is called __________. |
| A. | consequent. |
| B. | onset. |
| C. | antecedent. |
| D. | precedent. |
| Answer» D. precedent. | |
| 165. |
In web mining, _________ is used to know the order in which URLs tend to be accessed. |
| A. | clustering. |
| B. | associations. |
| C. | sequential analysis. |
| D. | classification. |
| Answer» D. classification. | |
| 166. |
_______ clustering technique start with as many clusters as there are records, with each cluster havingonly one record. |
| A. | agglomerative. |
| B. | divisive. |
| C. | partition. |
| D. | numeric. |
| Answer» B. divisive. | |
| 167. |
_________ is the way of studying the web link structure. |
| A. | computer network. |
| B. | physical network. |
| C. | social network. |
| D. | logical network. |
| Answer» D. logical network. | |
| 168. |
Removing duplicate records is a process called _____________. |
| A. | recovery. |
| B. | data cleaning. |
| C. | data cleansing. |
| D. | data pruning. |
| Answer» C. data cleansing. | |
| 169. |
EIS stands for ______________. |
| A. | extended interface system. |
| B. | executive interface system. |
| C. | executive information system. |
| D. | extendable information system. |
| Answer» D. extendable information system. | |
| 170. |
Shallow knowledge |
| A. | The large set of candidate solutions possible for a problem |
| B. | The information stored in a database that can be, retrieved with a single query. |
| C. | Worth of the output of a machine- learning program that makes it under- standable for humans |
| D. | None of these |
| Answer» C. Worth of the output of a machine- learning program that makes it under- standable for humans | |
| 171. |
_________ distance is based on L1 norm. |
| A. | euclidean distance |
| B. | minkowski distance |
| C. | manhattan distance |
| D. | jaccard distance |
| Answer» D. jaccard distance | |
| 172. |
Incorrect or invalid data is known as _________. |
| A. | changing data. |
| B. | noisy data. |
| C. | outliers. |
| D. | missing data. |
| Answer» C. outliers. | |
| 173. |
Market-basket problem was formulated by __________. |
| A. | agrawal et al. |
| B. | steve et al. |
| C. | toda et al. |
| D. | simon et al. |
| Answer» B. steve et al. | |
| 174. |
__________ is used to map a data item to a real valued prediction variable. |
| A. | regression. |
| B. | time series analysis. |
| C. | prediction. |
| D. | classification. |
| Answer» C. prediction. | |
| 175. |
Transparency |
| A. | The large set of candidate solutions possible for a problem |
| B. | The information stored in a database that can be, retrieved with a single query. |
| C. | Worth of the output of a machine- learning program that makes it under- standable for humans |
| D. | None of these |
| Answer» D. None of these | |
| 176. |
__________ are highly simplified models of biological neurons. |
| A. | artificial neurons. |
| B. | computational neurons. |
| C. | biological neurons. |
| D. | technological neurons. |
| Answer» B. computational neurons. | |
| 177. |
Capability of data mining is to build ___________ models. |
| A. | retrospective. |
| B. | interrogative. |
| C. | predictive. |
| D. | imperative. |
| Answer» D. imperative. | |
| 178. |
A data warehouse is _____________. |
| A. | updated by end users. |
| B. | contains numerous naming conventions and formats |
| C. | organized around important subject areas. |
| D. | contains only current data. |
| Answer» D. contains only current data. | |
| 179. |
____________ can generate programs itself, enabling it to carry out new tasks. |
| A. | automated system. |
| B. | decision making system. |
| C. | self-learning system. |
| D. | productivity system. |
| Answer» E. | |
| 180. |
Oracle is referred to |
| A. | A measure of the desired maximal complexity of data mining algorithms |
| B. | A database containing volatile data used for the daily operation of an organization |
| C. | Relational database management system |
| D. | None of these |
| Answer» D. None of these | |
| 181. |
The A Priori algorithm is a ___________. |
| A. | top-down search. |
| B. | breadth first search. |
| C. | depth first search. |
| D. | bottom-up search. |
| Answer» E. | |
| 182. |
The network topology is constrained to be __________________. |
| A. | feedforward. |
| B. | feedbackward. |
| C. | feed free. |
| D. | feed busy. |
| Answer» B. feedbackward. | |
| 183. |
Foreign key is |
| A. | Modular design of a software application that facilitates the integration of new modules |
| B. | Showing a universal law or rule to be invalid by providing a counter example |
| C. | A set of attributes in a database table that refers to data in another table |
| D. | None of these |
| Answer» D. None of these | |
| 184. |
___________and prediction may be viewed as types of classification. |
| A. | decision. |
| B. | verification. |
| C. | estimation. |
| D. | illustration. |
| Answer» D. illustration. | |
| 185. |
Which is not the type of attribute used in distance measure? |
| A. | ordinal |
| B. | nominal |
| C. | binay |
| D. | rank |
| Answer» E. | |
| 186. |
A fact is said to be fully 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. | |
| 187. |
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» E. | |
| 188. |
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» C. partially additive fact. | |
| 189. |
Meta-learning is |
| A. | An algorithm that can learn |
| B. | A sub-discipline of computer science that deals with the design and implementation of learning algorithms. |
| C. | A machine-learning approach that abstracts from the actual strategy of an individual algorithm and can therefore be applied to any other form of machine learning. |
| D. | None of these |
| Answer» D. None of these | |
| 190. |
Data marts that incorporate data mining tools to extract sets of data are called ______. |
| A. | independent data mart. |
| B. | dependent data marts. |
| C. | intra-entry data mart. |
| D. | inter-entry data mart. |
| Answer» C. intra-entry data mart. | |
| 191. |
A priori algorithm is otherwise called as __________. |
| A. | width-wise algorithm. |
| B. | level-wise algorithm. |
| C. | pincer-search algorithm. |
| D. | fp growth algorithm. |
| Answer» C. pincer-search algorithm. | |
| 192. |
_______ is concerned with discovering the model underlying the link structures of the web.http://grdmcqonline/printqp.php?heading=II M.Sc(IT) [2012-2014], Se...33 of 34 8/20/2013 2:47 PM |
| A. | web content mining. |
| B. | web structure mining. |
| C. | web usage mining. |
| D. | all of the above. |
| Answer» C. web usage mining. | |
| 193. |
_____________ is a process of determining the preference of customer's majority. |
| A. | association. |
| B. | preferencing. |
| C. | segmentation. |
| D. | classification. |
| Answer» C. segmentation. | |
| 194. |
The ________ propose a measure of standing a node based on path counting. |
| A. | open web. |
| B. | close web. |
| C. | link web. |
| D. | hidden web. |
| Answer» C. link web. | |
| 195. |
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» D. none of the above. | |
| 196. |
________ are responsible for running queries and reports against data warehouse tables. |
| A. | hardware. |
| B. | software. |
| C. | end users. |
| D. | middle ware. |
| Answer» D. middle ware. | |
| 197. |
SMP stands for _______________. |
| A. | symmetric multiprocessor. |
| B. | symmetric multiprogramming. |
| C. | symmetric metaprogramming. |
| D. | symmetric microprogramming. |
| Answer» B. symmetric multiprogramming. | |
| 198. |
Search space |
| A. | The large set of candidate solutions possible for a problem |
| B. | The information stored in a database that can be, retrieved with a single query. |
| C. | Worth of the output of a machine- learning program that makes it understandable for humans |
| D. | None of these |
| Answer» B. The information stored in a database that can be, retrieved with a single query. | |
| 199. |
The generic two-level data warehouse architecture includes __________. |
| A. | at least one data mart. |
| B. | data that can extracted from numerous internal and external sources. |
| C. | near real-time updates. |
| D. | far real-time updates. |
| Answer» D. far real-time updates. | |
| 200. |
Building the informational database is done with the help of _______. |
| A. | transformation or propagation tools. |
| B. | transformation tools only. |
| C. | propagation tools only. |
| D. | extraction tools. |
| Answer» B. transformation tools only. | |