Data Pre Processing Quiz Questions

1. What are the different types of attributes?

A. Nominal

B. Ordinal

C. Spacial

D. All of the Above

2. Examples of Nominal can be:

A. ID Numbers, eye color, zip codes

B. Rankings, taste of potato chips, grades, height

C. Calendar dates, temperatures in celsius or Fahrenheit, phone numbers

D. The temperature in Kelvin, length, time, counts

3. Examples of Ordinal can be:

A. ID Numbers, eye color, zip codes

B. Rankings, taste of potato chips, grades, height

C. Calendar dates, temperatures in Celsius or Fahrenheit, phone numbers

D. Temperature in Kelvin, length, time, counts

4. Examples of Interval can be:

A. ID Numbers, eye color, zip codes

B. Rankings, taste of potato chips, grades, height

C. Calendar dates, temperatures in celsius or Fahrenheit

D. Temperature in Kelvin, length, time, counts

5. The type of a Nominal attribute depends on which of the following properties:

A. Distinctness & order

B. Distinctness, order & addition

C. Distinctness

D. All 4 properties

6. The type of an Interval attribute depends on which of the following properties:

A. Distinctness & order

B. Distinctness, order & addition

C. Distinctness

D. All 4 properties

7. The type of an Ordinal attribute depends on which of the following properties:

A. Distinctness & order

B. Distinctness, order & addition

C. Distinctness

D. All 4 properties

8. Important Characteristics of Structured Data are:

A. Generality

B. Dimensionality

C. Resolution

D. All of the Above

9. What are some examples of data quality problems:

A. Noise and outliers

B. Duplicate data

C. Missing values

D. All of the Above

10. Which Method is used for encoding the categorical variables?

A. LabelEncoder

B. OneHotEncoder

C. CategoryEncoder

D. All of the Above

11. Under fitting happens due to -

A. A fewer number of features

B. Data has a high variance

C. No use of regularization

D. All of the Above

12. Over fitting happens due to -

A. Imbalance in data

B. Noise in data

C. Data has a high variance

D. All of the Above

13. Why do we need feature transformation?

A. Converting non-numeric features into numeric

B. Resizing inputs to a fixed size

C. Both A and B

D. None

14. Which of the following is true about outliers -

A. Data points that deviate a lot from normal observations

B. Can reduce the accuracy of the model

C. Both A and B

D. None

15. Some of the Imputation methods are -

A. Imputation with mean/median

B. Imputing with random numbers

C. Imputing with one

D. All of the above

16. Which algorithm does not require feature scaling?

A. Naive Bayes

B. Decision Tree

C. B. Decision Tree

D. None

17. The purpose of feature scaling is to -

A. Accelerating the training time

B. Getting better accuracy

C. Both A and B

D. None

18. In standardization, the features will be rescaled with -

A. Mean 0 and Variance 0

B. Mean 0 and Variance 1

C. Mean 1 and Variance 0

D. Mean 1 and Variance 1

19. What is a Dummy Variable Trap?

A. Multicollinearity among the dummy variables

B. One variable predicts the value of other

C. Both A and B

D. None of the Above

20. Which of the following(s) is/are features scaling techniques?

A. Standardization

B. Normalization

C. Min-Max Scaling

D. All of the Above

21. The characteristic of a good dataset is-

A. Sufficiently large for getting meaningful predictions

B. The bias is lower

C. Both A and B

D. None

22. How to handle the missing values in the dataset?

A. Dropping the missing rows or columns

B. Imputation with mean/median/mode value

C. Taking missing values into a new row or column

D. All of the above

23. The correct way of pre processing the data should be-

A. Imputation ->feature scaling-> training

B. Feature scaling->imputation->training

C. Feature scaling->label encoding->training

D. None

24. Which one is a feature extraction example?

A. Constructing a bag of words model

B. Imputation of missing values

C. Principal component analysis

D. All of the Above

25. Which of these techniques is used for normalization in text mining?

A. Stemming

B. Stop words removal

C. Lemmatization

D. All of the above

26. What stemming refers to in text mining?

A. Reducing a word to its root

B. Defining the parts of speech of a word

C. Converting sentences to words

D. None

27. Which is the correct order for pre processing in Natural Language Processing?

A. tokenization ->stemming ->lemmatization

B. lemmatization ->tokenization ->stemming

C. stemming ->tokenization ->lemmatization

D. None

28. Bag of Words in text pre processing is a-

A. Feature scaling technique

B. Feature extraction technique

C. Feature selection technique

D. None

29. In text mining, how the words ‘lovely’ is converted to ‘love’-

A. By stemming

B. By tokenization

C. By lemmatization

D. None

30. Stop words are-

A. Words frequently found in a document

B. Words not important for text mining

C. Words having no use in prediction

D. All of the Above

Quiz Category

Machine Learning

Data Pre Processing Quiz Questions