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
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96 questions
61. Machine learning is
- An algorithm that can learn
- A sub-discipline of computer science that deals with the design and implementation of learning algorithms.
- An 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
62. Inductive logic programming is
- A class of learning algorithm that try to derive a prolog program from examples
- A table with n independent attributes can be seen as an n- dimensionlal space
- A prediction made using an extremely simpl method, such as always predicting the same output.
- None of these
63. Multi-dimensional knowledge is
- A class of learning algorithms that try to derive a Prolog program from examples
- A table with n independent attributes can be seen as an n- dimensional space
- A pediction made using an extremely simple method, such as always predicting the same output.
- None of these
64. Naive prediction is
- A class of learning algorithms that try to derive a Prolog program from examples
- A table with n independent attributes can be seen as an n -dimensional space
- A prediction made using an extremely simple method, such asalways predicting the same output
- None of these
65. Knowledge is referred to
- Non-trivial extraction 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 table uniquely
- collection of interesting and useful patterns in a database
- none of these
66. Node is
- A component of network
- In the context of KDD and data mining this refers to random errors in a database table
- One of the defining aspects of a data warehouse
- None of these
67. Node is
- A component of a network
- In the context of KDD and data mining, this refers to random errors in a database table
- One of the defining aspects of a d ata warehouse
- None of these
68. Node is
- A component of a network
- In the context of KDD and data mining, this refers to random errors in a database table
- One of the defining aspects of a data warehouse
- None of these
69. Projection pursuit is
- The result of the application of a theory or a rule in a specific case
- 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.
- Discipline in statistics that studies ways to find the most interesting projections of multi-dimensional spaces
- None of these
70. Statistical significance is
- The science of collecting, organizing, and applying numberical facts
- Measure of the probabiity that a certain hypothesis is incorrect given certain observations.
- One of the defining aspects of a data warehouse, which is specially built around all the existing applications of the operational dat(A)
- None of these