Data mining and warehousing mcq unit 1

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Data mining and warehousing mcq unit 1

Data mining and warehousing mcq

1. Which of the following applied on warehouse?

  1. write only
  2. read only
  3. both
  4. a & b none
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read only

2. Data can be store , retrive and updated in

  1. SMTOP
  2. OLTP
  3. FTP
  4. OLAP
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OLTP

3. Data mining is Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from data in large databases

  1. TRUE
  2. FALSE
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TRUE

4. Data in the real world is

  1. incomplete
  2. inconsitent
  3. noisy
  4. all of above
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all of above

5. What are Measure of Data Quality

  1. Accuracy
  2. Completeness
  3. Consistency
  4. all
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all

6. Data cleaning is fill in missing values, smooth noisy data, identify or remove outliers, and resolve inconsistencies

  1. TRUE
  2. FALSE
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TRUE

7. Data integration is Integration of multiple databases, data cubes, or files

  1. TRUE
  2. FALSE
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TRUE

8. Data transformation is Normalization and aggregation

  1. TRUE
  2. FALSE

TRUE

9. Data reduction Obtains reduced representation in volume but produces the same or similar analytical results

  1. TRUE
  2. FALSE
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TRUE

10. Data discretization is Part of data reduction but with particular importance, especially for numerical data

  1. TRUE
  2. FALSE

TRUE

Data mining and warehousing mcq sppu

11. Missing data may be due to

  1. equipment malfunction
  2. inconsistent with other recorded data and thus deleted data not entered due to misunderstanding
  3. certain data may not be considered important at the time of entry
  4. all
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all

12. Incorrect attribute values may due to

  1. faulty data collection instruments
  2. data entry problems
  3. data transmission problems
  4. all

all

13. data cleaning is not required for duplicate records

  1. TRUE
  2. FALSE
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FALSE

14. Binning method first sort data and partition into (equi-depth) bins

  1. TRUE
  2. FALSE

TRUE

15. Data can be smoothed by fitting the data to a function, such as with regression.

  1. TRUE
  2. FALSE
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TRUE

16. Linear regression – involves finding the________ line to fit two attributes (or variables)

  1. best
  2. average
  3. worst

best

17. Data cleaning is fill in __ values

  1. existing
  2. missing
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missing

18. Data transformation is ________________and aggregation

  1. Normalization
  2. Denormalization

Normalization

19. Data reduction Obtains reduced representation in volume but produces the_________ or similar analytical results

  1. same
  2. different

same

data mining and warehousing mcq sppu

20. Data discretization is Part of data reduction but with particular importance, especially for _____________data

  1. Character
  2. numerical
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numerical

21. Redundant data occur often when integration of multiple databases

  1. TRUE
  2. FALSE

TRUE

22. The same attribute may have different names in different databases

  1. TRUE
  2. FALSE

TRUE

23. Careful integration of the data from multiple sources may help reduce/avoid redundancies and inconsistencies

  1. TRUE
  2. FALSE
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TRUE

24. Correlation coefficient is also called Pearson’s product moment coefficient

  1. TRUE
  2. FALSE

TRUE

25. Min-max normalization performs a linear transformation on the original data.

  1. TRUE
  2. FALSE

TRUE

26. The values for an attribute, A, are normalized based on the mean and standard deviation of A

  1. Min-max normalization
  2. z-score normalization
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z-score normalization

27. The values for an attribute, A, are normalized based on the mean and standard deviation of A in z-score normalization

  1. TRUE
  2. FALSE

TRUE

28. z-score normalization is useful when the actual minimum and maximum of attribute A are unknown

  1. TRUE
  2. FALSE

TRUE

29. Normalization by decimal scaling normalizes by moving the decimal point of values of attribute A.

  1. TRUE
  2. FALSE

TRUE

30. Data reduction obtains a reduced representation of the data set that is much smaller in volume but yet produces the same (or almost the same) analytical results

  1. TRUE
  2. FALSE

TRUE

data mining and warehousing mcq questions

31. Run Length Encoding is lossless

  1. TRUE
  2. FALSE
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TRUE

32. Jpeg compression is

  1. lossy
  2. lossless

lossy

33. Wavelet Transform Decomposes a signal into different frequency subbands

  1. TRUE
  2. FALSE
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TRUE

34. Principal Component Analysis (PCA) is used for dimensionality reduction

  1. TRUE
  2. FALSE

TRUE

35. Normalization by______________ scaling normalizes by moving the decimal point of values of attribute A

  1. binary
  2. octal
  3. decimal
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decimal

36. Data cube aggregation is normalization

  1. TRUE
  2. FALSE

TRUE

37. ordinal attribute have values from an ___________set

  1. ordered
  2. unordered
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ordered

38. Nominal attribute have values from an ___________set

  1. TRUE
  2. FALSE

FALSE

39. Run Length Encoding is

  1. lossy
  2. lossless
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lossless

Data Analytics sppu mcq

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