Data Mining & Data Ware House Questions and Answers
Data Mining & Data Warehouse Questions with Answers are vital for understanding large-scale data analysis and storage techniques in database management systems (DBMS). Featured often in programming questions and answers for competitive exams, this topic covers key concepts like OLAP, ETL processes, and data modeling. Companies like Infosys, CTS, and IBM frequently test candidates on these concepts during placements. Practicing these MCQs with explanations helps students enhance their analytical and SQL-based problem-solving skills. Download free Data Mining & Data Warehouse aptitude questions with solutions PDF or take our online tests for quick practice.
Data Mining & Data Ware House
Showing 10 of
96 questions
51. KDD (Knowledge Discovery in Database) is referred to
- Non-trivial extrction of implicit previusly unknown and potentially useful information from dat (A)
- Set of columns in a database table that can be used to identify each record within this table uniquely.
- collection of interesting and useful patterns in a database
- None of these
52. Key is referred to
- Non-trival extrction of implicit previously unknown and potentially useful information from dat(A)
- Set of columns in a database table that can be used to identify each record within this tabel uniquely
- Collection of interesting and useful patterns in database
- None of these
53. Inductive learning is
- Machine-learning involving different techniques
- The learning algorithmic analyzes the examples on a systematic basis and makes incremental adjustments to the theory that is learned
- Learning by generalizing from examples
- None of these
54. Integrated is
- The amount of information with in data as opposed to the amount of redundancy or noise
- One of the defining aspects of a data warehouse
- Restriction that requires data in one column of a database table to the sub-set of another-column
- None of these
55. Knowledge engineering is
- The process of finding the right formal representation of a certain body of knowledge in order to represent it in a knowledge-based system
- It automatically maps an external signal space into a system's internal representational space. They are useful in the performance of classification tasks
- A process where an individual learns how to carry out a certain task when 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.
- None of these
56. Kohonen self-organizing map referred to
- The process of finding the right formal representation of a certain body of knowledge in order to represent it in a knowledge-based system
- It automatically maps an external signal space into a system's internal representational space. they are useful in the performance of classification tasks
- A process where an individual learns how a 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 circumstaces can be carried out.
- None of these
57. Learning is
- The process of finding the right formal representation of a certain body of knowledge in order to represent it in a knowledge-based system
- It automatically maps an external signal space into a system's internal representational space. they are useful in the performance of classification tasks.
- A process where an individual learns how to carry out a certain task when making a transition from a situation in a situation in which the same task under the same circumstances can be carried out.
- None of these
58. Learning algorithm referrers to
- An algorithm that can learn
- A sub-discipline of computer science that deals with the design and imp-lementation of learning algorithms.
- 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.
- None of these
59. Meta-learning is
- An algorithm that can learn
- A sub-discipline of computerscience that deals with the design and implementation of learning algorithms.
- A machine-learning approach that abstracts from the actual strategy of an invdividual algorithm and can therefore be applied to any other form of machine learning.
- None of these
60. Meta-learning is
- An algorithm that can learn
- A sub-discipline of computer science that deals with the design and implementation of learning algorithms.
- A machine-learning approach that abstracts from the actual strategy of an individual algorithm and can therefore be applied to any other form of machiner learning
- None of these