McqMate
| 301. |
Which is not the type of attribute used in distance measure? |
| A. | ordinal |
| B. | nominal |
| C. | binay |
| D. | rank |
| Answer» D. rank | |
| 302. |
_____ method is used to find the distance between two objects represented by numerical attributes. |
| A. | euclidean distance |
| B. | minkowski distance |
| C. | manhattan distance |
| D. | all of these |
| Answer» D. all of these | |
| 303. |
Contingency table is prepared for _______ attribute data. |
| A. | ordinal |
| B. | nominal |
| C. | binay |
| D. | integer |
| Answer» C. binay | |
| 304. |
Which are the applications of proximity measures? |
| A. | classification |
| B. | clustering |
| C. | knn classifier |
| D. | all of these |
| Answer» D. all of these | |
| 305. |
_________ matrix represents the distance between all objects in the dataset |
| A. | confusion |
| B. | dissimilarity |
| C. | similarity |
| D. | square |
| Answer» B. dissimilarity | |
| 306. |
If o1 and o2 are two objects and distance between these objects is zero then it means_____ |
| A. | o1 and o2 are totally similar |
| B. | o1 and o2 are totally dissimilar |
| C. | o1 and o2 are similar |
| D. | o1 and o2 are partially dissimilar |
| Answer» A. o1 and o2 are totally similar | |
| 307. |
Identify the correct subtype of Binary attribute. |
| A. | ordinal |
| B. | asymmetric |
| C. | symmetric |
| D. | both b and c |
| Answer» D. both b and c | |
| 308. |
_____ Lower when objects are more alike. |
| A. | dissimilarity |
| B. | recall |
| C. | similarity |
| D. | accuracy |
| Answer» A. dissimilarity | |
| 309. |
Adaptive system management is |
| A. | It uses machine-learning techniques. Here program can learn from past experience and adapt themselves to new situations |
| B. | Computational procedure that takes some value as input and produces some value as output. |
| C. | Science of making machines performs tasks that would require intelligence when performed by humans |
| D. | None of these |
| Answer» A. It uses machine-learning techniques. Here program can learn from past experience and adapt themselves to new situations | |
| 310. |
Algorithm is |
| A. | It uses machine-learning techniques. Here program can learn from past experience and adapt themselves to new situations |
| B. | Computational procedure that takes some value as input and produces some value as output |
| C. | Science of making machines performs tasks that would require intelligence when performed by humans |
| D. | None of these |
| Answer» B. Computational procedure that takes some value as input and produces some value as output | |
| 311. |
Background knowledge referred to |
| 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» A. Additional acquaintance used by a learning algorithm to facilitate the learning process | |
| 312. |
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» B. A neural network that makes use of a hidden layer | |
| 313. |
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 |
| Answer» A. A class of learning algorithm that tries to find an optimum classification of a set of examples using the probabilistic theory. | |
| 314. |
Bias 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 |
| Answer» B. Any mechanism employed by a learning system to constrain the search space of a hypothesis. | |
| 315. |
Case-based learning 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 |
| Answer» 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. | |
| 316. |
Binary attribute are |
| A. | This takes only two values. In general, these values will be 0 and 1 and they can be coded as one bit |
| B. | The natural environment of a certain species |
| C. | Systems that can be used without knowledge of internal operations |
| D. | None of these |
| Answer» A. This takes only two values. In general, these values will be 0 and 1 and they can be coded as one bit | |
| 317. |
Biotope are |
| A. | This takes only two values. In general, these values will be 0 and 1 and they can be coded as one bit. |
| B. | The natural environment of a certain species |
| C. | Systems that can be used without knowledge of internal operations |
| D. | None of these |
| Answer» B. The natural environment of a certain species | |
| 318. |
Black boxes |
| A. | This takes only two values. In general, these values will be 0 and 1 and they can be coded as one bit. |
| B. | The natural environment of a certain species |
| C. | Systems that can be used without knowledge of internal operations |
| D. | None of these |
| Answer» C. Systems that can be used without knowledge of internal operations | |
| 319. |
Artificial intelligence is |
| A. | It uses machine-learning techniques. Here program can learn from past experience and adapt themselves to new situations |
| B. | Computational procedure that takes some value as input and produces some value as output. |
| C. | Science of making machines performs tasks that would require intelligence when performed by humans |
| D. | None of these |
| Answer» C. Science of making machines performs tasks that would require intelligence when performed by humans | |
| 320. |
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» 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 | |
| 321. |
Cardinality of an attribute 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 | |
| 322. |
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» 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 | |
| 323. |
Classification is |
| 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» A. A subdivision of a set of examples into a number of classes | |
| 324. |
Classification accuracy is |
| A. | A subdivision of a set of examples into a number of classes |
| B. | 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» B. Measure of the accuracy, of the classification of a concept that is given by a certain theory | |
| 325. |
Cluster 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» A. Group of similar objects that differ significantly from other objects | |
| 326. |
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» C. Symbolic representation of facts or ideas from which information can potentially be extract | |
| 327. |
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» A. Complete | |
| 328. |
A definition or a concept is ———————if it does not classify any examples as coming within the concept |
| A. | Complete |
| B. | Consistent |
| C. | Constant |
| D. | None of these |
| Answer» B. Consistent | |
| 329. |
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» C. The task of assigning a classification to a set of examples | |
| 330. |
Database 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» A. Large collection of data mostly stored in a computer system | |
| 331. |
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» B. The removal of noise errors and incorrect input from a database | |
| 332. |
Data dictionary 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. | |
| 333. |
Data mining is |
| A. | The actual discovery phase of a knowledge discovery process |
| B. | The stage of selecting the right data for a KDD process |
| C. | A subject-oriented integrated time- variant non-volatile collection of data in support of management |
| D. | None of these |
| Answer» A. The actual discovery phase of a knowledge discovery process | |
| 334. |
Data selection is |
| A. | The actual discovery phase of a knowledge discovery process |
| B. | The stage of selecting the right data for a KDD process |
| C. | A subject-oriented integrated time- variant non-volatile collection of data in support of management |
| D. | None of these |
| Answer» B. The stage of selecting the right data for a KDD process | |
| 335. |
Data warehouse is |
| A. | The actual discovery phase of a knowledge discovery process |
| B. | The stage of selecting the right data for a KDD process |
| C. | A subject-oriented integrated time- variant non-volatile collection of data in support of management |
| D. | None of these |
| Answer» C. A subject-oriented integrated time- variant non-volatile collection of data in support of management | |
| 336. |
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» B. Operations on a database to transform or simplify data in order to prepare it for a machine-learning algorithm | |
| 337. |
DB/2 is |
| A. | A family of relational database manage- ment 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» A. A family of relational database manage- ment systems marketed by IBM | |
| 338. |
Decision support systems (DSS) 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» B. Interactive systems that enable decision makers to use databases and models on a computer in order to solve ill- structured problems | |
| 339. |
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» C. It consists of nodes and branches starting from a single root node. Each node represents a test, or decision. | |
| 340. |
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» A. It is hidden within a database and can only be recovered if one is given certain clues (an example IS encrypted information) | |
| 341. |
Discovery is |
| 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) | |
| 342. |
DNA (Deoxyribonucleic acid) |
| 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» C. An extremely complex molecule that occurs in human chromosomes and that carries genetic information in the form of genes. | |
| 343. |
Enrichment is |
| A. | A stage of the KDD process in which new data is added to the existing selection |
| B. | The process of finding a solution for a problem simply by enumerating all possible solutions according to some pre-defined order and then testing them. |
| C. | The distance between two points as calculated using the Pythagoras theorem. |
| D. | None of these |
| Answer» A. A stage of the KDD process in which new data is added to the existing selection | |
| 344. |
Enumeration is referred to |
| A. | A stage of the KDD process in which new data is added to the existing selection. |
| B. | The process of finding a solution for a problem simply by enumerating all possible solutions according to some pre-defined order and then testing them |
| C. | The distance between two points as calculated using the Pythagoras theorem. |
| D. | None of these |
| Answer» B. The process of finding a solution for a problem simply by enumerating all possible solutions according to some pre-defined order and then testing them | |
| 345. |
Euclidean distance measure is |
| A. | A stage of the KDD process in which new data is added to the existing selection. |
| B. | The process of finding a solution for a problem simply by enumerating all possible solutions according to some pre-defined order and then testing them. |
| C. | The distance between two points as calculated using the Pythagoras theo- rem |
| D. | None of these |
| Answer» C. The distance between two points as calculated using the Pythagoras theo- rem | |
| 346. |
Heuristic is |
| A. | A set of databases from different vendors, possibly using different database paradigms |
| B. | An approach to a problem that is not guaranteed to work but performs well in most cases. |
| C. | Information that is hidden in a database and that cannot be recovered by a simple SQL query. |
| D. | None of these |
| Answer» B. An approach to a problem that is not guaranteed to work but performs well in most cases. | |
| 347. |
Heterogeneous databases referred to |
| A. | A set of databases from different vendors, possibly using different database paradigms |
| B. | An approach to a problem that is not guaranteed to work but performs well in most cases. |
| C. | Information that is hidden in a database and that cannot be recovered by a simple SQL query. |
| D. | None of these |
| Answer» A. A set of databases from different vendors, possibly using different database paradigms | |
| 348. |
Hidden knowledge referred to |
| A. | A set of databases from different vendors, possibly using different database paradigms |
| B. | An approach to a problem that is not guaranteed to work but performs well in most cases. |
| C. | Information that is hidden in a database and that cannot be recovered by a simple SQL query. |
| D. | None of these |
| Answer» C. Information that is hidden in a database and that cannot be recovered by a simple SQL query. | |
| 349. |
Hybrid is |
| 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» A. Combining different types of method or information | |
| 350. |
Evolutionary computation is |
| 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» B. Approach to the design of learning algorithms that is structured along the lines of the theory of evolution. | |
| 351. |
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» C. Decision support systems that contain an Information base filled with the knowledge of an expert formulated in terms of if-then rules | |
| 352. |
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» A. Modular design of a software application that facilitates the integration of new modules | |
| 353. |
Falsification 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 | |
| 354. |
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» C. A set of attributes in a database table that refers to data in another table | |
| 355. |
Hybrid learning is |
| A. | Machine-learning involving different techniques |
| B. | The learning algorithmic analyzes the examples on a systematic basis and makes incremental adjustments to the theory that is learned |
| C. | Learning by generalizing from examples |
| D. | None of these |
| Answer» A. Machine-learning involving different techniques | |
| 356. |
Incremental learning referred to |
| A. | Machine-learning involving different techniques |
| B. | The learning algorithmic analyzes the examples on a systematic basis and makes incremental adjustments to the theory that is learned |
| C. | Learning by generalizing from examples |
| D. | None of these |
| Answer» B. The learning algorithmic analyzes the examples on a systematic basis and makes incremental adjustments to the theory that is learned | |
| 357. |
Information content 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» A. The amount of information with in data as opposed to the amount of redundancy or noise | |
| 358. |
Inclusion dependencies |
| 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 | |
| 359. |
KDD (Knowledge Discovery in Databases) is referred to |
| A. | Non-trivial extraction of implicit previously unknown and potentially useful information from dat(A) |
| B. | Set of columns in a database table that can be used to identify each record within this table uniquely. |
| C. | collection of interesting and useful patterns in a database |
| D. | none of these |
| Answer» A. Non-trivial extraction of implicit previously unknown and potentially useful information from dat(A) | |
| 360. |
Key is referred to |
| A. | Non-trivial extraction of implicit previously unknown and potentially useful information from dat(A) |
| B. | Set of columns in a database table that can be used to identify each record within this table uniquely |
| C. | collection of interesting and useful patterns in a database |
| D. | none of these |
| Answer» B. Set of columns in a database table that can be used to identify each record within this table uniquely | |
| 361. |
Inductive learning is |
| A. | Machine-learning involving different techniques |
| B. | The learning algorithmic analyzes the examples on a systematic basis and makes incremental adjustments to the theory that is learned |
| C. | Learning by generalizing from examples |
| D. | None of these |
| Answer» C. Learning by generalizing from examples | |
| 362. |
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» B. One of the defining aspects of a data warehouse | |
| 363. |
Knowledge engineering is |
| A. | The process of finding the right formal representation of a certain body of knowledge in order to represent it in a knowledge-based system |
| B. | It automatically maps an external signal space into a system’s internal representational space. They are useful in the performance of classification tasks. |
| C. | A process where an individual learns how to carry out a certain task when making a transition from a situation in which the task cannot be carried out to a situation in which the same task under the same circumstances can be carried out. |
| D. | None of these |
| Answer» A. The process of finding the right formal representation of a certain body of knowledge in order to represent it in a knowledge-based system | |
| 364. |
Kohonen self-organizing map referred to |
| A. | The process of finding the right formal representation of a certain body of knowledge in order to represent it in a knowledge-based system |
| B. | It automatically maps an external signal space into a system’s internal representational space. They are useful in the performance of classification tasks |
| C. | A process where an individual learns how to carry out a certain task when making a transition from a situation in which the task cannot be carried out to a situation in which the same task under the same circumstances can be carried out. |
| D. | None of these |
| Answer» B. It automatically maps an external signal space into a system’s internal representational space. They are useful in the performance of classification tasks | |
| 365. |
Learning is |
| A. | The process of finding the right formal representation of a certain body of knowledge in order to represent it in a knowledge-based system |
| B. | It automatically maps an external signal space into a system’s internal representational space. They are useful in the performance of classification tasks. |
| C. | A process where an individual learns how to carry out a certain task when making a transition from a situation in which the task cannot be carried out to a situation in which the same task under the same circumstances can be carried out. |
| D. | None of these |
| Answer» C. A process where an individual learns how to carry out a certain task when making a transition from a situation in which the task cannot be carried out to a situation in which the same task under the same circumstances can be carried out. | |
| 366. |
Learning algorithm referrers to |
| 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» A. An algorithm that can learn | |
| 367. |
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» 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. | |
| 368. |
Machine 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. | An 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» B. A sub-discipline of computer science that deals with the design and implementation of learning algorithms. | |
| 369. |
Inductive logic programming is |
| A. | A class of learning algorithms that try to derive a Prolog program from examples* |
| B. | A table with n independent attributes can be seen as an n- dimensional space. |
| C. | A prediction made using an extremely simple method, such as always predicting the same output. |
| D. | None of these |
| Answer» A. A class of learning algorithms that try to derive a Prolog program from examples* | |
| 370. |
Multi-dimensional knowledge is |
| A. | A class of learning algorithms that try to derive a Prolog program from examples |
| B. | A table with n independent attributes can be seen as an n- dimensional space |
| C. | A prediction made using an extremely simple method, such as always predicting the same output. |
| D. | None of these |
| Answer» B. A table with n independent attributes can be seen as an n- dimensional space | |
| 371. |
Naive prediction is |
| A. | A class of learning algorithms that try to derive a Prolog program from examples |
| B. | A table with n independent attributes can be seen as an n- dimensional space. |
| C. | A prediction made using an extremely simple method, such as always predicting the same output. |
| D. | None of these |
| Answer» C. A prediction made using an extremely simple method, such as always predicting the same output. | |
| 372. |
Knowledge is referred to |
| A. | Non-trivial extraction of implicit previously unknown and potentially useful information from dat(A) |
| B. | Set of columns in a database table that can be used to identify each record within this table uniquely. |
| C. | collection of interesting and useful patterns in a database |
| D. | none of these |
| Answer» C. collection of interesting and useful patterns in a database | |
| 373. |
Node is |
| A. | A component of a network |
| B. | In the context of KDD and data mining, this refers to random errors in a database table. |
| C. | One of the defining aspects of a data warehouse |
| D. | None of these |
| Answer» A. A component of a network | |
| 374. |
Projection pursuit is |
| A. | The result of the application of a theory or a rule in a specific case |
| B. | One of several possible enters within a database table that is chosen by the designer as the primary means of accessing the data in the table. |
| C. | Discipline in statistics that studies ways to find the most interesting projections of multi-dimensional spaces |
| D. | None of these |
| Answer» C. Discipline in statistics that studies ways to find the most interesting projections of multi-dimensional spaces | |
| 375. |
Statistical significance is |
| A. | The science of collecting, organizing, and applying numerical facts |
| B. | Measure of the probability that a certain hypothesis is incorrect given certain observations. |
| C. | One of the defining aspects of a data warehouse, which is specially built around all the existing applications of the operational dat(A) |
| D. | None of these |
| Answer» B. Measure of the probability that a certain hypothesis is incorrect given certain observations. | |
| 376. |
Prediction is |
| A. | The result of the application of a theory or a rule in a specific case |
| B. | One of several possible enters within a database table that is chosen by the designer as the primary means of accessing the data in the table. |
| C. | Discipline in statistics that studies ways to find the most interesting projections of multi-dimensional spaces. |
| D. | None of these |
| Answer» A. The result of the application of a theory or a rule in a specific case | |
| 377. |
Primary key is |
| A. | The result of the application of a theory or a rule in a specific case |
| B. | One of several possible enters within a database table that is chosen by the designer as the primary means of accessing the data in the table |
| C. | Discipline in statistics that studies ways to find the most interesting projections of multi-dimensional spaces. |
| D. | None of these |
| Answer» B. One of several possible enters within a database table that is chosen by the designer as the primary means of accessing the data in the table | |
| 378. |
Noise is |
| A. | A component of a network |
| B. | In the context of KDD and data mining, this refers to random errors in a database table. |
| C. | One of the defining aspects of a data warehouse |
| D. | None of these |
| Answer» B. In the context of KDD and data mining, this refers to random errors in a database table. | |
| 379. |
Quadratic complexity is |
| 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» A. A reference to the speed of an algorithm, which is quadratically dependent on the size of the dat(A) | |
| 380. |
Query tools 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. | |
| 381. |
Prolog is |
| A. | A programming language based on logic |
| B. | A computer where each processor has its own operating system, its own memory, and its own hard disk. |
| C. | Describes the structure of the contents of a database. |
| D. | None of these |
| Answer» A. A programming language based on logic | |
| 382. |
Massively parallel machine is |
| A. | A programming language based on logic |
| B. | A computer where each processor has its own operating system, its own memory, and its own hard disk |
| C. | Describes the structure of the contents of a database. |
| D. | None of these |
| Answer» B. A computer where each processor has its own operating system, its own memory, and its own hard disk | |
| 383. |
Meta-data is |
| A. | A programming language based on logic |
| B. | A computer where each processor has its own operating system, its own memory, and its own hard disk. |
| C. | Describes the structure of the contents of a database |
| D. | None of these |
| Answer» C. Describes the structure of the contents of a database | |
| 384. |
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» A. A measure of the desired maximal complexity of data mining algorithms | |
| 385. |
Operational database is |
| 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 | |
| 386. |
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» C. Relational database management system | |
| 387. |
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» A. General class of approaches to a problem. | |
| 388. |
Patterns 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» C. Structures in a database those are statistically relevant | |
| 389. |
Parallelism 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 | |
| 390. |
Perceptron 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» D. Simple forerunner of modern neural networks, without hidden layers. | |
| 391. |
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» B. The information stored in a database that can be, retrieved with a single query. | |
| 392. |
Statistics |
| A. | The science of collecting, organizing, and applying numerical facts |
| B. | Measure of the probability that a certain hypothesis is incorrect given certain observations. |
| C. | One of the defining aspects of a data warehouse, which is specially built around all the existing applications of the operational dat(A) |
| D. | None of these |
| Answer» A. The science of collecting, organizing, and applying numerical facts | |
| 393. |
Subject orientation |
| A. | The science of collecting, organizing, and applying numerical facts |
| B. | Measure of the probability that a certain hypothesis is incorrect given certain observations. |
| C. | One of the defining aspects of a data warehouse, which is specially built around all the existing applications of the operational dat(A) |
| D. | None of these |
| Answer» C. One of the defining aspects of a data warehouse, which is specially built around all the existing applications of the operational dat(A) | |
| 394. |
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» A. The large set of candidate solutions possible for a problem | |
| 395. |
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» C. Worth of the output of a machine- learning program that makes it under- standable for humans | |
| 396. |
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» B. Attributes of a database table that can take only numerical values. | |
| 397. |
Unsupervised algorithms |
| A. | It do not need the control of the human operator during their execution. |
| B. | An arrow in a multi-dimensional space. It is a quantity usually characterized by an ordered set of scalars. |
| C. | The validation of a theory on the basis of a finite number of examples. |
| D. | None of these |
| Answer» A. It do not need the control of the human operator during their execution. | |
| 398. |
Vector |
| A. | It do not need the control of the human operator during their execution. |
| B. | An arrow in a multi-dimensional space. It is a quantity usually characterized by an ordered set of scalars. |
| C. | The validation of a theory on the basis of a finite number of examples. |
| D. | None of these |
| Answer» B. An arrow in a multi-dimensional space. It is a quantity usually characterized by an ordered set of scalars. | |
| 399. |
Verification |
| A. | It does not need the control of the human operator during their execution. |
| B. | An arrow in a multi-dimensional space. It is a quantity usually characterized by an ordered set of scalars. |
| C. | The validation of a theory on the basis of a finite number of examples |
| D. | None of these |
| Answer» C. The validation of a theory on the basis of a finite number of examples | |
| 400. |
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» A. A class of graphic techniques used to visualize the contents of a database | |
Done Reading?