Data Mining & Data Ware House Questions and Answers

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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

81. Paradigm is

  • General class of approaches to a problem.
  • Performing several computations simultaneously.
  • Structures in a database those are statistically relevant.
  • Simple forerunner of modern neural networks, without hidden layers.
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82. Pattern is

  • General class of approaches to a problem
  • Performing several computations simultaneously.
  • Structures in a database those are statistically relevant.
  • Simple forerunner of modern neural networks, without hidden layers.
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83. Parallelelism is

  • General class of approaches to a problem
  • Performing several computations simultaneously
  • Structures in a database those are statistically relevant.
  • Simple forerunner of modern neural networks, without hidden layers.
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84. Perceptron is

  • General class of approaches to a problem
  • Performing several computations simultaneously
  • Structures in a database those are statistically relevant.
  • Simple forerunner of modern neural networks, without hidden layers.
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85. Shallow knowledge

  • The large set of candidate solutions possible for a problem
  • The information stored in a database that can be,l retrieved with a single query
  • Worth of the output of a machine-learning program that makes it under-standable for humans
  • None of these
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86. Statistics

  • The science of collecting, organizing, and applying numerical facts
  • Measure of the probability 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
  • None of these
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87. Subject orientation

  • The science of collecting, organizing and applying numerical facts
  • Measure of the probability that a certain hypthesis is incorrect given certain observations.
  • One of the defining aspects of a data warehouse, which is specially built around all the existing applications of
  • None of these
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88. Search space

  • The large set of candidate solutions possible for a problem
  • The information stored in a database that can be retrieved with a single query.
  • Worth of the output of a machine-learning program that makes it understandable for humans
  • None of these
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89. Transparency

  • The large set of candidate solutions possible for a problem
  • The information stored in a database that can be, retrieved with a single query.
  • Worth of the output of a machine-learning program that makes it under-standable for human
  • None of these
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90. Quantitative attributes are

  • A reference to the speed of an algorithm, which is quadratically dependent on the size of the dat(A)
  • Attributes of a database table that can take only numerical values.
  • Tools designed to query a database.
  • None of these
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