MCQs

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Overall MCQs for Reference

1. Which of the following best defines a data attribute in data science?
   a. A random sequence of numbers
   b. A specific characteristic or property measured for each observation in a dataset
   c. An unsorted group of data entries
   d. The process of cleaning data
   Answer: bstatistics+1​
2. Which of the following is an example of a nominal attribute?
   a. Age
   b. Height
   c. Gender
   d. Weight
   Answer: cgeeksforgeeks+1​
3. In data science, which type of attribute represents measurable, numerical values?
   a. Ordinal
   b. Nominal
   c. Quantitative
   d. Binary
   Answer: cgeeksforgeeks+1​
4. Which attribute type is also referred to as categorical and has no inherent ordering?
   a. Ordinal
   b. Nominal
   c. Ratio
   d. Interval
   Answer: bgeeksforgeeks+1​
5. What is meant by an ordinal attribute?
   a. Values represent categories without order
   b. Values are always binary
   c. Values with meaningful order but undefined intervals
   d. Values with defined intervals and true zero
   Answer: cgeeksforgeeks+1​
6. Which is a binary attribute?
   a. Age group
   b. Gender (“Male” or “Female”)
   c. House color
   d. Country name
   Answer: bgeeksforgeeks+1​
7. The collection of all attributes that describe an object is referred to as:
   a. Attribute matrix
   b. Attribute vector
   c. Feature map
   d. Data cluster
   Answer: bgeeksforgeeks​
8. A data attribute that has only two states such as “on”/”off” is classified as:
   a. Nominal
   b. Interval
   c. Binary
   d. Ratio
   Answer: cgeeksforgeeks+1​
9. Which attribute allows measurement and has a true zero point?
   a. Interval
   b. Ratio
   c. Ordinal
   d. Nominal
   Answer: bstudyglance​
10. Which describes interval-scaled attributes?
    a. Only two possible values
    b. Categories with meaningful order
    c. Numeric values with equal intervals but no true zero
    d. No numeric interpretation
    Answer: cstudyglance​
11. In a dataset with customer ages, the ‘age’ attribute is:
    a. Ordinal
    b. Nominal
    c. Ratio
    d. Binary
    Answer: cstudyglance+1​
12. “Education level” categorized as “Undergraduate”, “Graduate”, “Postgraduate” is an example of:
    a. Nominal attribute
    b. Binary attribute
    c. Ordinal attribute
    d. Ratio attribute
    Answer: cgeeksforgeeks​
13. Which of the following attributes cannot be reasonably averaged?
    a. Weight
    b. Age
    c. Country
    d. Height
    Answer: cgeeksforgeeks+1​
14. Which attribute is best analyzed using mode rather than mean or median?
    a. Height
    b. Gender
    c. Temperature
    d. Salary
    Answer: bgeeksforgeeks+1​
15. What makes binary attributes symmetric?
    a. One value is more significant
    b. Both values are equally important
    c. Neither value is used
    d. Only one state is possible
    Answer: bgeeksforgeeks​
16. What describes an asymmetric binary attribute?
    a. Values are ordered
    b. Both values are equally important
    c. One value is more important
    d. Attribute cannot be used for analysis
    Answer: cgeeksforgeeks​
17. What is an example of an ordinal (ordered category) attribute?
    a. Phone number
    b. Medal type: Gold, Silver, Bronze
    c. Temperature in °C
    d. Email address
    Answer: bgeeksforgeeks​
18. “Salary” measured in dollars is an example of which attribute type?
    a. Nominal
    b. Interval
    c. Ratio
    d. Ordinal
    Answer: cstudyglance​
19. Which attribute type would a “yes”/”no” question represent?
    a. Interval
    b. Nominal
    c. Binary
    d. Ordinal
    Answer: cgeeksforgeeks+1​
20. Color of a car (red, blue, green) in a dataset is a:
    a. Nominal attribute
    b. Ratio attribute
    c. Interval attribute
    d. Binary attribute
    Answer: ageeksforgeeks​
21. What is NOT characteristic of nominal data?
    a. Lacks natural ordering
    b. Values are categories
    c. Allows calculation of mean
    d. Identified by mode
    Answer: cgeeksforgeeks+1​
22. If an attribute can be grouped into “employed,” “unemployed,” and “student,” this is:
    a. Interval
    b. Binary
    c. Nominal
    d. Ratio
    Answer: cgeeksforgeeks+1​
23. The “zipcode” field in a dataset is an example of which attribute?
    a. Nominal
    b. Interval
    c. Binary
    d. Ratio
    Answer: ageeksforgeeks​
24. “Temperature in Celsius” is:
    a. Binary
    b. Ordinal
    c. Interval
    d. Nominal
    Answer: cstudyglance​
25. What attribute type is best suited for a Likert scale (Strongly Disagree to Strongly Agree)?
    a. Interval
    b. Ratio
    c. Ordinal
    d. Nominal
    Answer: cstudyglance​
26. The term “attribute domain” refers to:
    a. All attribute names in a table
    b. Set of possible values for an attribute
    c. Attribute types in the dataset
    d. Data entry format
    Answer: blearninglabb+1​
27. Which of the following is an example of quantitative data?
    a. Eye color
    b. City name
    c. Number of employees
    d. Country
    Answer: cgeeksforgeeks+1​
28. Which attribute can have values like “married”, “single”, “divorced”?
    a. Ordinal
    b. Binary
    c. Nominal
    d. Ratio
    Answer: cgeeksforgeeks+1​
29. Which type allows arithmetic operations like addition and multiplication?
    a. Nominal
    b. Ordinal
    c. Ratio
    d. Binary
    Answer: cstudyglance​
30. Why is understanding attribute types important in data analysis?
    a. Helps with database indexing
    b. Aids in variable selection and statistical method choice
    c. Reduces size of data
    d. None of the above
    Answer: bgeeksforgeeks+1​
31. Which attribute is most suitable for one-hot encoding?
    a. Ratio
    b. Nominal
    c. Interval
    d. All of these
    Answer: bgeeksforgeeks+1​
32. Attributes with meaningful zero and equal interval, but relative comparison (e.g., twice as much) do not make sense, are:
    a. Interval
    b. Ratio
    c. Ordinal
    d. Nominal
    Answer: astudyglance​
33. Which is a valid attribute for an individual’s record in a dataset?
    a. Transaction ID
    b. Date of birth
    c. Both a and b
    d. None of these
    Answer: cstatistics+1​
34. Attributes that have unlimited possible values within a range are:
    a. Binary
    b. Discrete
    c. Continuous
    d. Nominal
    Answer: cgeeksforgeeks+1​
35. The “status” field (active/inactive) is an example of which attribute?
    a. Nominal
    b. Ratio
    c. Binary
    d. Ordinal
    Answer: cgeeksforgeeks+1​
36. Which attribute is often ignored by algorithms due to uniqueness?
    a. Gender
    b. ID number
    c. Age group
    d. Salary range
    Answer: bgeeksforgeeks+1​
37. Which of the following would NOT be a valid attribute?
    a. Color of a shirt
    b. Eye shape
    c. Random number with no meaning
    d. Student name
    Answer: cgeeksforgeeks​
38. True or False: Attributes must always be numeric for effective data analysis.
    a. True
    b. False
    c. Only for regression
    d. Only for classification
    Answer: bgeeksforgeeks+1​
39. “Speed in km/h” is an example of which attribute type?
    a. Ordinal
    b. Ratio
    c. Binary
    d. Nominal
    Answer: bstudyglance​
40. Which attribute type requires the use of non-parametric statistical tests?
    a. Nominal
    b. Ratio
    c. Interval
    d. None of these
    Answer: ageeksforgeeks+1​
41. The attribute “marital status” having “Yes”/”No” is:
    a. Ordinal
    b. Binary
    c. Ratio
    d. Interval
    Answer: bgeeksforgeeks+1​
42. When is it appropriate to use label encoding in data preprocessing?
    a. For ordinal attributes
    b. For binary attributes only
    c. For nominal attributes only
    d. For numeric attributes only
    Answer: ageeksforgeeks+1​
43. Which attribute best represents a phone area code?
    a. Ratio
    b. Interval
    c. Nominal
    d. Ordinal
    Answer: cgeeksforgeeks​
44. “Exam grade” (A, B, C, D, F) is an example of:
    a. Ratio attribute
    b. Ordinal attribute
    c. Binary attribute
    d. Interval attribute
    Answer: bstudyglance​
45. An attribute that can only take integer values is:
    a. Continuous
    b. Discrete
    c. Nominal
    d. Interval
    Answer: bgeeksforgeeks+1​
46. Which attribute is best for performing group by operations in SQL?
    a. Ratio
    b. Nominal
    c. Ordinal
    d. Binary
    Answer: bgeeksforgeeks+1​
47. The sum of which type of attribute is meaningful?
    a. Nominal
    b. Ordinal
    c. Ratio
    d. Binary
    Answer: cstudyglance​
48. Which attribute is usually used as the target variable in classification tasks?
    a. Nominal
    b. Interval
    c. Ratio
    d. Ordinal
    Answer: ageeksforgeeks+1​
49. If an attribute represents an amount, such as monthly income, it is:
    a. nominal
    b. interval
    c. ratio
    d. ordinal
    Answer: cstudyglance​
50. The measurement of “humidity in percentage” falls under which attribute type?
    a. Ratio
    b. Nominal
    c. Binary
    d. Ordinal
    Answer: astudyglance​

1. https://www.statistics.com/glossary/attribute/
2. https://www.geeksforgeeks.org/data-science/attributes-and-its-types-in-data-analytics/
3. https://www.geeksforgeeks.org/data-science/understanding-data-attribute-types-qualitative-and-quantitative/
4. https://studyglance.in/dm/display.php?tno=2&topic=Attribute-Types
5. https://learninglabb.com/data-objects-and-attribute-types-in-data-mining/
6. https://leanscape.io/what-is-attribute-data-the-attribute-data-world-explained/
7. https://www.kaggle.com/code/rabianaz22/data-types-attributes-in-data-analysis
8. https://gyansanchay.csjmu.ac.in/wp-content/uploads/2022/04/Data-Objects-and-Attribute-Types-converted.pdf
9. https://developer.mozilla.org/en-US/docs/Web/HTML/How_to/Use_data_attributes
10. https://www.w3schools.com/tags/att_data-.asp
11. https://www.slideshare.net/slideshow/data-types-and-attributes1-1pptx/258908855
12. https://www.digitalfluency.guide/data/attributes-of-data
13. https://www.ibm.com/docs/en/hla-and-tf/1.6.0?topic=instructions-data-attributes
14. https://www.youtube.com/watch?v=rWLX8QCeEXU
15. https://www.geeksforgeeks.org/dbms/data-mining-data-attributes-and-quality/
16. https://www.sprinkledata.com/blogs/what-is-a-dataset-types-examples-and-the-techniques-involved
17. https://www.sciencedirect.com/topics/computer-science/attribute-data
18. https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/23/dmprg/about-attributes.html
19. https://developers.google.com/machine-learning/crash-course/overfitting/data-characteristics
20. https://www.ibm.com/docs/en/epfz/5.3.0?topic=elements-data-types-attributes