McqMate
These multiple-choice questions (MCQs) are designed to enhance your knowledge and understanding in the following areas: Computer Science Engineering (CSE) , Common Topics in Competitive and Entrance exams .
| 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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