Data Pre Processing Quiz Questions

1. What are the different types of attributes?

view answer: D. All of the Above
2. Examples of Nominal can be:

view answer: A. ID Numbers, eye color, zip codes
Explanation: Therefore, the correct answer is A. Examples of rankings, grades, height, calendar dates, temperatures in Celsius or Fahrenheit, phone numbers, temperature in Kelvin, length, time, and counts are not nominal attributes, but instead fall under other types of attributes like ordinal, interval, or ratio.
3. Examples of Ordinal can be:

view answer: B. Rankings, taste of potato chips, grades, height
4. Examples of Interval can be:

view answer: D. Temperature in Kelvin, length, time, counts
5. The type of a Nominal attribute depends on which of the following properties:

view answer: C. Distinctness
6. The type of an Interval attribute depends on which of the following properties:

view answer: D. All 4 properties
7. The type of an Ordinal attribute depends on which of the following properties:

view answer: A. Distinctness & order
8. Important Characteristics of Structured Data are:

view answer: D. All of the Above
9. What are some examples of data quality problems:

view answer: D. All of the Above
10. Which Method is used for encoding the categorical variables?

view answer: A. LabelEncoder
11. Under fitting happens due to -

view answer: A. A fewer number of features
12. Over fitting happens due to -

view answer: D. All of the Above
13. Why do we need feature transformation?

view answer: C. Both A and B
14. Which of the following is true about outliers -

view answer: C. Both A and B
15. Some of the Imputation methods are -

view answer: A. Imputation with mean/median
16. Which algorithm does not require feature scaling?

view answer: D. None
17. The purpose of feature scaling is to -

view answer: C. Both A and B
18. In standardization, the features will be rescaled with -

view answer: B. Mean 0 and Variance 1
19. What is a Dummy Variable Trap?

view answer: C. Both A and B
20. Which of the following(s) is/are features scaling techniques?

view answer: D. All of the Above
21. The characteristic of a good dataset is-

view answer: C. Both A and B
22. How to handle the missing values in the dataset?

view answer: B. Imputation with mean/median/mode value
23. The correct way of pre processing the data should be-

view answer: A. Imputation ->feature scaling-> training
24. Which one is a feature extraction example?

view answer: C. Principal component analysis
25. Which of these techniques is used for normalization in text mining?

view answer: D. All of the above
26. What stemming refers to in text mining?

view answer: A. Reducing a word to its root
27. Which is the correct order for pre processing in Natural Language Processing?

view answer: A. tokenization ->stemming ->lemmatization
28. Bag of Words in text pre processing is a-

view answer: B. Feature extraction technique
29. In text mining, how the words ‘lovely’ is converted to ‘love’-

view answer: A. By stemming
30. Stop words are-

view answer: D. All of the Above

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