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.
| 251. |
What equation we get when r parameter =2 in Minskowski Distance formula? |
| A. | manhattan distance |
| B. | euclidean distance |
| C. | lmaximum distance |
| D. | all |
| Answer» C. lmaximum distance | |
| 252. |
A definition of a concept is——if it recognizes all the instances of that concept. |
| A. | Complete |
| B. | Consistent |
| C. | Constant |
| D. | None of these |
| Answer» B. Consistent | |
| 253. |
______________ is data about data. |
| A. | metadata. |
| B. | microdata. |
| C. | minidata. |
| D. | multidata. |
| Answer» B. microdata. | |
| 254. |
The load and index is ______________. |
| A. | a process to reject data from the data warehouse and to create the necessary indexes. |
| B. | a process to load the data in the data warehouse and to create the necessary indexes. |
| C. | a process to upgrade the quality of data after it is moved into a data warehouse. |
| D. | a process to upgrade the quality of data before it is moved into a data warehouse. |
| Answer» C. a process to upgrade the quality of data after it is moved into a data warehouse. | |
| 255. |
Data 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 extract |
| Answer» D. | |
| 256. |
The number of iterations in a priori ___________. |
| A. | increases with the size of the maximum frequent set. |
| B. | decreases with increase in size of the maximum frequent set. |
| C. | increases with the size of the data. |
| D. | decreases with the increase in size of the data. |
| Answer» B. decreases with increase in size of the maximum frequent set. | |
| 257. |
The absolute number of transactions supporting X in T is called ___________. |
| A. | confidence. |
| B. | support. |
| C. | support count. |
| D. | none of the above. |
| Answer» D. none of the above. | |
| 258. |
The transformed prefix paths of a node 'a' form a truncated database of pattern which co-occurwith a is called _______. |
| A. | suffix path. |
| B. | fp-tree. |
| C. | conditional pattern base. |
| D. | prefix path. |
| Answer» D. prefix path. | |
| 259. |
___________ is the most widely applied neural network technique. |
| A. | abc. |
| B. | plm. |
| C. | lmp. |
| D. | mlp. |
| Answer» E. | |
| 260. |
The rise of DBMS occurred in early ___________. |
| A. | 1950\s. |
| B. | 1960\s |
| C. | 1970\s |
| D. | 1980\s. |
| Answer» D. 1980\s. | |
| 261. |
The basic partition algorithm reduces the number of database scans to ________ & divides it intopartitions. |
| A. | one. |
| B. | two. |
| C. | three. |
| D. | four. |
| Answer» C. three. | |
| 262. |
If an itemset is not a frequent set and no superset of this is a frequent set, then it is _______. |
| A. | maximal frequent set |
| B. | border set. |
| C. | upward closure property. |
| D. | downward closure property. |
| Answer» C. upward closure property. | |
| 263. |
Cache is |
| A. | It is a memory buffer that is used to store data that is needed frequently by an algorithm in order to minimize input/ output traffic |
| B. | The number of different values that a given attribute can take |
| C. | A mathematical conception of space where the location of a point is given by reference to its distance from two or three axes intersecting at right angles |
| D. | None of these |
| Answer» B. The number of different values that a given attribute can take | |
| 264. |
The human brain consists of a network of ___________. |
| A. | neurons. |
| B. | cells. |
| C. | tissue. |
| D. | muscles. |
| Answer» B. cells. | |
| 265. |
________ displays of data such as maps, charts and other graphical representation allow data to bepresented compactly to the users. |
| A. | hidden |
| B. | visual |
| C. | obscured |
| D. | concealed |
| Answer» C. obscured | |
| 266. |
The full form of KDD is _________. |
| A. | knowledge database. |
| B. | knowledge discovery in database. |
| C. | knowledge data house. |
| D. | knowledge data definition. |
| Answer» C. knowledge data house. | |
| 267. |
Certain itemsets enter afresh into the system and get into the _______, which are essentially thesupersets of the itemsets that move from the dashed circle to the dashed box. |
| A. | dashed box. |
| B. | solid circle. |
| C. | solid box. |
| D. | dashed circle. |
| Answer» E. | |
| 268. |
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» C. mobile number | |
| 269. |
Expert systems |
| A. | Combining different types of method or information |
| B. | Approach to the design of learning algorithms that is structured along the lines of the theory of evolution. |
| C. | Decision support systems that contain an Information base filled with the knowledge of an expert formulated in terms of if-then rules |
| D. | None of these |
| Answer» D. None of these | |
| 270. |
The right hand side of an association rule is called _____. |
| A. | consequent. |
| B. | onset. |
| C. | antecedent. |
| D. | precedent. |
| Answer» B. onset. | |
| 271. |
Quantitative attributes are |
| A. | A reference to the speed of an algorithm, which is quadratically dependent on the size of the dat(A) |
| B. | Attributes of a database table that can take only numerical values. |
| C. | Tools designed to query a database. |
| D. | None of these |
| Answer» C. Tools designed to query a database. | |
| 272. |
___________ is a good alternative to the star schema. |
| A. | star schema. |
| B. | snowflake schema. |
| C. | fact constellation. |
| D. | star-snowflake schema. |
| Answer» D. star-snowflake schema. | |
| 273. |
Investment analysis used in neural networks is to predict the movement of _________ from previousdata. |
| A. | engines. |
| B. | stock. |
| C. | patterns. |
| D. | models. |
| Answer» C. patterns. | |
| 274. |
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» D. it contains only current data | |
| 275. |
____________ contains information that gives users an easy-to-understand perspective of theinformation stored in the data warehouse. |
| A. | business metadata. |
| B. | technical metadata. |
| C. | operational metadata. |
| D. | financial metadata. |
| Answer» B. technical metadata. | |
| 276. |
____________ is called a multifield transformation. |
| A. | converting data from one field into multiple fields. |
| B. | converting data from fields into field. |
| C. | converting data from double fields into multiple fields. |
| D. | converting data from one field to one field. |
| Answer» B. converting data from fields into field. | |
| 277. |
Integrated is |
| A. | The amount of information with in data as opposed to the amount of redundancy or noise |
| B. | One of the defining aspects of a data warehouse |
| C. | Restriction that requires data in one column of a database table to the a sub- set of another-column. |
| D. | None of these |
| Answer» C. Restriction that requires data in one column of a database table to the a sub- set of another-column. | |
| 278. |
__________ are designed to overcome any limitations placed on the warehouse by the nature of therelational data model. |
| A. | operational database. |
| B. | relational database. |
| C. | multidimensional database. |
| D. | data repository. |
| Answer» D. data repository. | |
| 279. |
Classification task referred to |
| A. | A subdivision of a set of examples into a number of classes |
| B. | A measure of the accuracy, of the classification of a concept that is given by a certain theory |
| C. | The task of assigning a classification to a set of examples |
| D. | None of these |
| Answer» D. None of these | |
| 280. |
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» B. non-additive measures. | |
| 281. |
Back propagation networks is |
| A. | Additional acquaintance used by a learning algorithm to facilitate the learning process |
| B. | A neural network that makes use of a hidden layer |
| C. | It is a form of automatic learning. |
| D. | None of these |
| Answer» C. It is a form of automatic learning. | |
| 282. |
___________ employs the supervised mode of learning. |
| A. | rbf. |
| B. | mlp. |
| C. | mlp & rbf. |
| D. | ann. |
| Answer» D. ann. | |
| 283. |
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» D. ability. | |
| 284. |
Data cleaning is |
| A. | Large collection of data mostly stored in a computer system |
| B. | The removal of noise errors and incorrect input from a database |
| C. | The systematic description of the syntactic structure of a specific database. It describes the structure of the attributes the tables and foreign key relationships. |
| D. | None of these |
| Answer» C. The systematic description of the syntactic structure of a specific database. It describes the structure of the attributes the tables and foreign key relationships. | |
| 285. |
A fact is said to be partially 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» C. not additive over any dimension. | |
| 286. |
Paradigm is |
| A. | General class of approaches to a problem. |
| B. | Performing several computations simultaneously. |
| C. | Structures in a database those are statistically relevant. |
| D. | Simple forerunner of modern neural networks, without hidden layers. |
| Answer» B. Performing several computations simultaneously. | |
| 287. |
Cartesian space is |
| A. | It is a memory buffer that is used to store data that is needed frequently by an algorithm in order to minimize input/ output traffic |
| B. | The number of different values that a given attribute can take |
| C. | A mathematical conception of space where the location of a point is given by reference to its distance from two or three axes intersecting at right angles |
| D. | None of these |
| Answer» B. The number of different values that a given attribute can take | |
| 288. |
Decision trees is |
| A. | A family of relational database management systems marketed by IBM |
| B. | Interactive systems that enable decision makers to use databases and models on a computer in order to solve ill- structured problems |
| C. | It consists of nodes and branches starting from a single root node. Each node represents a test, or decision. |
| D. | None of these |
| Answer» D. None of these | |
| 289. |
MFCS is the acronym of _____. |
| A. | maximum frequency control set. |
| B. | minimal frequency control set. |
| C. | maximal frequent candidate set. |
| D. | minimal frequent candidate set. |
| Answer» D. minimal frequent candidate set. | |
| 290. |
Visualization techniques are |
| 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» B. The division of a certain space into various areas based on guide points. | |
| 291. |
The type of relationship in star schema is __________________. |
| A. | many-to-many. |
| B. | one-to-one. |
| C. | one-to-many. |
| D. | many-to-one. |
| Answer» D. many-to-one. | |
| 292. |
7 If T consist of 500000 transactions, 20000 transaction contain bread, 30000 transaction containjam, 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. | |
| 293. |
Extendible architecture 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» B. Showing a universal law or rule to be invalid by providing a counter example | |
| 294. |
The SOM was a neural network model developed by ________. |
| A. | simon king. |
| B. | teuvokohonen. |
| C. | tomoki toda. |
| D. | julia. |
| Answer» C. tomoki toda. | |
| 295. |
In ___________ each cluster is represented by one of the objects of the cluster located near thecenter. |
| A. | k-medoid. |
| B. | k-means. |
| C. | stirr. |
| D. | rock. |
| Answer» B. k-means. | |
| 296. |
____________ is one of the most popular models in the unsupervised framework. |
| A. | som. |
| B. | sam. |
| C. | osm. |
| D. | mso. |
| Answer» B. sam. | |
| 297. |
___________can be thought of as classifying an attribute value into one of a set of possibleclasses. |
| A. | estimation. |
| B. | prediction. |
| C. | identification. |
| D. | clarification. |
| Answer» C. identification. | |
| 298. |
The threshold function is replaced by continuous functions called ________ functions. |
| A. | activation. |
| B. | deactivation. |
| C. | dynamic. |
| D. | standard. |
| Answer» B. deactivation. | |
| 299. |
____________predicts future trends & behaviors, allowing business managers to make proactive,knowledge-driven decisions. |
| A. | data warehouse. |
| B. | data mining. |
| C. | datamarts. |
| D. | metadata. |
| Answer» C. datamarts. | |
| 300. |
Business Intelligence and data warehousing is used for ________. |
| A. | forecasting. |
| B. | data mining. |
| C. | analysis of large volumes of product sales data. |
| D. | all of the above. |
| Answer» E. | |