Quiz Topic - Data Pre Processing

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
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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