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 .
| 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 | |
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