McqMate
201. |
The ___________is a long, single fibre that originates from the cell body. |
A. | axon. |
B. | neuron. |
C. | dendrites. |
D. | strands. |
Answer» A. axon. |
202. |
A single axon makes ___________ of synapses with other neurons. |
A. | ones. |
B. | hundreds. |
C. | thousands. |
D. | millions. |
Answer» C. thousands. |
203. |
_____________ is a complex chemical process in neural networks. |
A. | receiving process. |
B. | sending process. |
C. | transmission process. |
D. | switching process. |
Answer» C. transmission process. |
204. |
_________ is the connectivity of the neuron that give simple devices their real power. a. b. c. d. |
A. | water. |
B. | air. |
C. | power. |
D. | fire. |
Answer» D. fire. |
205. |
__________ are highly simplified models of biological neurons. |
A. | artificial neurons. |
B. | computational neurons. |
C. | biological neurons. |
D. | technological neurons. |
Answer» A. artificial neurons. |
206. |
The biological neuron's _________ is a continuous function rather than a step function. |
A. | read. |
B. | write. |
C. | output. |
D. | input. |
Answer» C. output. |
207. |
The threshold function is replaced by continuous functions called ________ functions. |
A. | activation. |
B. | deactivation. |
C. | dynamic. |
D. | standard. |
Answer» A. activation. |
208. |
The sigmoid function also knows as __________functions. |
A. | regression. |
B. | logistic. |
C. | probability. |
D. | neural. |
Answer» B. logistic. |
209. |
MLP stands for ______________________. |
A. | mono layer perception. |
B. | many layer perception. |
C. | more layer perception. |
D. | multi layer perception. |
Answer» D. multi layer perception. |
210. |
In a feed- forward networks, the conncetions between layers are ___________ from input to output. |
A. | bidirectional. |
B. | unidirectional. |
C. | multidirectional. |
D. | directional. |
Answer» B. unidirectional. |
211. |
The network topology is constrained to be __________________. |
A. | feedforward. |
B. | feedbackward. |
C. | feed free. |
D. | feed busy. |
Answer» A. feedforward. |
212. |
RBF stands for _____________. |
A. | radial basis function. |
B. | radial bio function. |
C. | radial big function. |
D. | radial bi function. |
Answer» A. radial basis function. |
213. |
RBF have only _______________ hidden layer. |
A. | four. |
B. | three. |
C. | two. |
D. | one. |
Answer» D. one. |
214. |
RBF hidden layer units have a receptive field which has a ____________; that is, a particular input value at which they have a maximal output. |
A. | top. |
B. | bottom. |
C. | centre. |
D. | border. |
Answer» C. centre. |
215. |
___________ training may be used when a clear link between input data sets and target output values does not exist. |
A. | competitive. |
B. | perception. |
C. | supervised. |
D. | unsupervised. |
Answer» D. unsupervised. |
216. |
___________ employs the supervised mode of learning. |
A. | rbf. |
B. | mlp. |
C. | mlp & rbf. |
D. | ann. |
Answer» C. mlp & rbf. |
217. |
________________ design involves deciding on their centres and the sharpness of their Gaussians. |
A. | dr. |
B. | and. |
C. | xor. |
D. | rbf. |
Answer» D. rbf. |
218. |
___________ is the most widely applied neural network technique. |
A. | abc. |
B. | plm. |
C. | lmp. |
D. | mlp. |
Answer» D. mlp. |
219. |
SOM is an acronym of _______________. |
A. | self-organizing map. |
B. | self origin map. |
C. | single organizing map. |
D. | simple origin map. |
Answer» A. self-organizing map. |
220. |
____________ is one of the most popular models in the unsupervised framework. |
A. | som. |
B. | sam. |
C. | osm. |
D. | mso. |
Answer» A. som. |
221. |
The actual amount of reduction at each learning step may be guided by _________. |
A. | learning cost. |
B. | learning level. |
C. | learning rate. |
D. | learning time. |
Answer» C. learning rate. |
222. |
The SOM was a neural network model developed by ________. |
A. | simon king. |
B. | teuvokohonen. |
C. | tomoki toda. |
D. | julia. |
Answer» B. teuvokohonen. |
223. |
SOM was developed during ____________. |
A. | 1970-80. |
B. | 1980-90. |
C. | 1990 -60. |
D. | 1979 -82. |
Answer» D. 1979 -82. |
224. |
Investment analysis used in neural networks is to predict the movement of _________ from previous data. |
A. | engines. |
B. | stock. |
C. | patterns. |
D. | models. |
Answer» B. stock. |
225. |
SOMs are used to cluster a specific _____________ dataset containing information about the patient's drugs etc. |
A. | physical. |
B. | logical. |
C. | medical. |
D. | technical. |
Answer» C. medical. |
226. |
GA stands for _______________. |
A. | genetic algorithm |
B. | gene algorithm. |
C. | general algorithm. |
D. | geo algorithm. |
Answer» A. genetic algorithm |
227. |
GA was introduced in the year __________. |
A. | 1955. |
B. | 1965. |
C. | 1975. |
D. | 1985. |
Answer» C. 1975. |
228. |
Genetic algorithms are search algorithms based on the mechanics of natural_______. |
A. | systems. |
B. | genetics. |
C. | logistics. |
D. | statistics. |
Answer» B. genetics. |
229. |
GAs were developed in the early _____________. |
A. | 1970. |
B. | 1960. |
C. | 1950. |
D. | 1940. |
Answer» A. 1970. |
230. |
The RSES system was developed in ___________. |
A. | poland. |
B. | italy. |
C. | england. |
D. | america. |
Answer» A. poland. |
231. |
Crossover is used to _______. |
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» A. recombine the population\s genetic material. |
232. |
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» B. introduce new genetic structures in the population. |
233. |
Which of the following is an operation in genetic algorithm? |
A. | inversion. |
B. | dominance. |
C. | genetic edge recombination. |
D. | all of the above. |
Answer» D. all of the above. |
234. |
. ___________ is a system created for rule induction. |
A. | rbs. |
B. | cbs. |
C. | dbs. |
D. | lers. |
Answer» D. lers. |
235. |
NLP stands for _________. |
A. | non language process. |
B. | nature level program. |
C. | natural language page. |
D. | natural language processing. |
Answer» D. natural language processing. |
236. |
Web content mining describes the discovery of useful information from the _______contents. |
A. | text. |
B. | web. |
C. | page. |
D. | level. |
Answer» B. web. |
237. |
Research on mining multi-types of data is termed as _______ data. |
A. | graphics. |
B. | multimedia. |
C. | meta. |
D. | digital. |
Answer» B. multimedia. |
238. |
_______ mining is concerned with discovering the model underlying the link structures of the web. |
A. | data structure. |
B. | web structure. |
C. | text structure. |
D. | image structure. |
Answer» B. web structure. |
239. |
_________ is the way of studying the web link structure. |
A. | computer network. |
B. | physical network. |
C. | social network. |
D. | logical network. |
Answer» C. social network. |
240. |
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» B. close web. |
241. |
In web mining, _______ is used to find natural groupings of users, pages, etc. |
A. | clustering. |
B. | associations. |
C. | sequential analysis. |
D. | classification. |
Answer» A. clustering. |
242. |
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» C. sequential analysis. |
243. |
In web mining, _________ is used to know which URLs tend to be requested together. |
A. | clustering. |
B. | associations. |
C. | sequential analysis. |
D. | classification. |
Answer» B. associations. |
244. |
__________ describes the discovery of useful information from the web contents. |
A. | web content mining. |
B. | web structure mining. |
C. | web usage mining. |
D. | all of the above. |
Answer» A. web content mining. |
245. |
_______ is concerned with discovering the model underlying the link structures of the web. |
A. | web content mining. |
B. | web structure mining. |
C. | web usage mining. |
D. | all of the above. |
Answer» B. web structure mining. |
246. |
The ___________ engine for a data warehouse supports query-triggered usage of data |
A. | nntp |
B. | smtp |
C. | olap |
D. | pop |
Answer» C. olap |
247. |
________ displays of data such as maps, charts and other graphical representation allow data to be presented compactly to the users. |
A. | hidden |
B. | visual |
C. | obscured |
D. | concealed |
Answer» B. visual |
248. |
__________ is a subject-oriented, integrated, time-variant, nonvolatile collection of data in support of management decisions. |
A. | data mining. |
B. | data warehousing. |
C. | web mining. |
D. | text mining. |
Answer» B. data warehousing. |
249. |
The important aspect of the data warehouse environment is that data found within the data warehouse is___________. |
A. | subject-oriented. |
B. | time-variant. |
C. | integrated. |
D. | all of the above. |
Answer» D. all of the above. |
250. |
_________maps the core warehouse metadata to business concepts, familiar and useful to end users. |
A. | application level metadata. |
B. | user level metadata. |
C. | enduser level metadata. |
D. | core level metadata. |
Answer» A. application level metadata. |
251. |
Data redundancy between the environments results in less than ____________percent. |
A. | one. |
B. | two. |
C. | three. |
D. | four. |
Answer» A. one. |
252. |
Bill Inmon has estimated___________of the time required to build a data warehouse, is consumed in the conversion process. |
A. | 10 percent. |
B. | 20 percent. |
C. | 40 percent |
D. | 80 percent. |
Answer» D. 80 percent. |
253. |
The biggest drawback of the level indicator in the classic star-schema is that it limits_________ |
A. | quantify. |
B. | qualify. |
C. | flexibility. |
D. | ability. |
Answer» C. flexibility. |
254. |
Maintenance of cache consistency is the limitation of __________________. |
A. | numa. |
B. | unam. |
C. | mpp. |
D. | pmp. |
Answer» C. mpp. |
255. |
___________ 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. | dimensionality. |
Answer» C. functional dependency. |
256. |
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. |
257. |
____________ 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. |
258. |
_____________ helps to uncover hidden information about the data.. |
A. | induction. |
B. | compression. |
C. | approximation. |
D. | summarization. |
Answer» C. approximation. |
259. |
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% |
260. |
7 If T consist of 500000 transactions, 20000 transaction contain bread, 30000 transaction contain
|
A. | 33.33% |
B. | 66.66% |
C. | 45% |
D. | 50% |
Answer» D. 50% |
261. |
The _______ step eliminates the extensions of (k-1)-itemsets which are not found to be frequent, from being considered for counting support. |
A. | candidate generation. |
B. | pruning. |
C. | partitioning. |
D. | itemset eliminations. |
Answer» B. pruning. |
262. |
The transformed prefix paths of a node 'a' form a truncated database of pattern which co-occur with a is called _______. |
A. | suffix path. |
B. | fp-tree. |
C. | conditional pattern base. |
D. | prefix path. |
Answer» C. conditional pattern base. |
263. |
__________ clustering techniques starts with all records in one cluster and then try to split that cluster into small pieces. |
A. | agglomerative. |
B. | divisive. |
C. | partition. |
D. | numeric. |
Answer» B. divisive. |
264. |
BIRCH is a ________.. |
A. | agglomerative clustering algorithm. |
B. | hierarchical algorithm. |
C. | hierarchical-agglomerative algorithm. |
D. | divisive. |
Answer» C. hierarchical-agglomerative algorithm. |
265. |
The ________ algorithm is based on the observation that the frequent sets are normally very few in number compared to the set of all itemsets. |
A. | a priori. |
B. | clustering. |
C. | association rule. |
D. | partition. |
Answer» D. partition. |
266. |
The basic idea of the apriori algorithm is to generate________ item sets of a particular size & scans the database. |
A. | candidate. |
B. | primary. |
C. | secondary. |
D. | superkey. |
Answer» A. candidate. |
267. |
________is the most well known association rule algorithm and is used in most commercial products. |
A. | apriori algorithm. |
B. | partition algorithm. |
C. | distributed algorithm. |
D. | pincer-search algorithm. |
Answer» A. apriori algorithm. |
268. |
An algorithm called________is used to generate the candidate item sets for each pass after the first. |
A. | apriori. |
B. | apriori-gen. |
C. | sampling. |
D. | partition. |
Answer» B. apriori-gen. |
269. |
___________can be thought of as classifying an attribute value into one of a set of possible classes. |
A. | estimation. |
B. | prediction. |
C. | identification. |
D. | clarification. |
Answer» B. prediction. |
270. |
____________ are a different paradigm for computing which draws its inspiration from neuroscience. |
A. | computer networks. |
B. | neural networks. |
C. | mobile networks. |
D. | artificial networks. |
Answer» B. neural networks. |
271. |
In a feed- forward networks, the conncetions between layers are ___________ from input to output. |
A. | bidirectional. |
B. | unidirectional. |
C. | multidirectional. |
D. | directional. |
Answer» B. unidirectional. |
272. |
___________ training may be used when a clear link between input data sets and target output values does not exist. |
A. | competitive. |
B. | perception. |
C. | supervised. |
D. | unsupervised. |
Answer» D. unsupervised. |
273. |
Investment analysis used in neural networks is to predict the movement of _________ from previous data. |
A. | engines. |
B. | stock. |
C. | patterns. |
D. | models. |
Answer» B. stock. |
274. |
SOMs are used to cluster a specific _____________ dataset containing information about the patient's drugs etc. |
A. | physical. |
B. | logical. |
C. | medical. |
D. | technical. |
Answer» C. medical. |
275. |
_______ is concerned with discovering the model underlying the link structures of the web.. |
A. | web content mining. |
B. | web structure mining. |
C. | web usage mining. |
D. | all of the above. |
Answer» B. web structure mining. |
276. |
A link is said to be _________ link if it is between pages with different domain names. |
A. | intrinsic. |
B. | transverse. |
C. | direct. |
D. | contrast. |
Answer» B. transverse. |
277. |
A link is said to be _______ link if it is between pages with the same domain name. |
A. | intrinsic. |
B. | transverse. |
C. | direct. |
D. | contrast. |
Answer» A. intrinsic. |
278. |
Patterns that can be discovered from a given database are which type |
A. | more than one type |
B. | multiple types always |
C. | one type only |
D. | no specific type |
Answer» A. more than one type |
279. |
A snowflake schema is which of the following types of tables? |
A. | fact |
B. | dimension |
C. | helper |
D. | all of the above |
Answer» D. all of the above |
280. |
Which one manages both current and historic transactions? |
A. | oltp |
B. | olap |
C. | spread sheet |
D. | xml |
Answer» B. olap |
281. |
Expansion for DSS in DW is__________. |
A. | decision support system |
B. | decision single system |
C. | data storable system |
D. | data support system |
Answer» A. decision support system |
282. |
__________describes the data contained in the data warehouse |
A. | relational data |
B. | operational data |
C. | meta data |
D. | informational data |
Answer» C. meta data |
283. |
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 |
284. |
Data warehousing is used in_______________ |
A. | transaction system |
B. | database management system |
C. | decision support system |
D. | expert system |
Answer» C. decision support system |
285. |
Data warehouse is based on_____________ |
A. | two dimensional model |
B. | three dimensional model |
C. | multi dimensional model |
D. | unidimensional model |
Answer» C. multi dimensional model |
286. |
Multidimensional model of data warehouse called as_____ |
A. | data structure |
B. | table |
C. | tree |
D. | data cube |
Answer» D. data cube |
287. |
In data warehousing what is time-variant data? |
A. | data in the warehouse is only accurate and valid at some point in time or over time interval |
B. | data in the warehouse is always accurate and valid |
C. | data in the warehouse is not accurate |
D. | data in the warehouse is only accurate sometimes |
Answer» A. data in the warehouse is only accurate and valid at some point in time or over time interval |
288. |
What is a Star Schema? |
A. | a star schema consists of a fact table with a single table for each dimension |
B. | a star schema is a type of database system |
C. | a star schema is used when exporting data from the database |
D. | none of these |
Answer» A. a star schema consists of a fact table with a single table for each dimension |
289. |
What does the acronym ETL stands for? |
A. | explain,transfer and lead |
B. | extract,transform and load |
C. | extract,transfer and load |
D. | effect,transfer and load |
Answer» B. extract,transform and load |
290. |
Which small logical units do data warehouses hold large amounts of information? |
A. | data storage |
B. | data marts |
C. | access layers |
D. | data miners |
Answer» B. data marts |
291. |
Which one is correct for data warehousing? |
A. | it can be updated by end users |
B. | it can solve all business questions |
C. | it is designed for focus subject areas |
D. | it contains only current data |
Answer» C. it is designed for focus subject areas |
292. |
A fact table is related to dimensional table as a ___ relationship |
A. | 1:m |
B. | m:n |
C. | m:1 |
D. | 1:1 |
Answer» C. m:1 |
293. |
Minkowski distance is a function used to find the distance between two |
A. | binary vectors |
B. | boolean-valued vectors |
C. | real-valued vectors |
D. | categorical vectors |
Answer» C. real-valued vectors |
294. |
Data set of designation {Professor, Assistant Professor, Associate Professor} is example of__________attribute. |
A. | continuous |
B. | ordinal |
C. | numeric |
D. | nominal |
Answer» D. nominal |
295. |
Identify the correct example of Nominal Attributes. |
A. | weight of person in kg |
B. | income categories - high, medium, low |
C. | mobile number |
D. | all above |
Answer» B. income categories - high, medium, low |
296. |
When objects are represented using single attribute, the proximity value 1 indicates : |
A. | objects are similar |
B. | objects are dissimilar |
C. | not equal |
D. | reflexive |
Answer» A. objects are similar |
297. |
Identity correct equation of Jacard Coefficient: |
A. | j= f11/f01+f10+f11 |
B. | j= f11+f00/f01+f10+f11 |
C. | j= f11+f00/f01+f10 |
D. | none of these |
Answer» A. j= f11/f01+f10+f11 |
298. |
What equation we get when r parameter =2 in Minskowski Distance formula? |
A. | manhattan distance |
B. | euclidean distance |
C. | lmaximum distance |
D. | all |
Answer» B. euclidean distance |
299. |
________is a generalization of Manhattan, Euclidean and Max Distance |
A. | euclidean distance |
B. | minkowski distance |
C. | manhattan distance |
D. | jaccard distance |
Answer» B. minkowski distance |
300. |
_________ distance is based on L1 norm. |
A. | euclidean distance |
B. | minkowski distance |
C. | manhattan distance |
D. | jaccard distance |
Answer» C. manhattan distance |
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