Data Science FREE Data Science Data Wrangling and Preprocessing Questions and Answers 2

Question 1

Which technique is most appropriate for handling missing numerical data when the dataset contains significant outliers?

Accepted Answer

Median imputation

Answer

Mean imputation

Answer

Mode imputation

Answer

Listwise deletion

Question 2

What is the primary purpose of one-hot encoding in data preprocessing?

Accepted Answer

To convert categorical variables into binary vector representations

Answer

To normalize numerical features to a standard scale

Answer

To reduce the dimensionality of the dataset

Answer

To remove duplicate records from the dataset

Question 3

When performing a log transformation on a feature that contains zero values, what is the standard approach?

Accepted Answer

Apply log1p (log of 1 plus the value) instead of a standard log

Answer

Replace zeros with the mean of the column

Answer

Remove all rows containing zero values

Answer

Use square root transformation exclusively

Question 4

In the context of data wrangling, what does the term 'tidy data' refer to?

Accepted Answer

Data where each variable is a column, each observation is a row, and each value is a cell

Answer

Data that has been cleaned of all missing values

Answer

Data that has been normalized to a 0-1 range

Answer

Data stored in a compressed file format for efficiency

Question 5

Which method is most effective for detecting multicollinearity among features during preprocessing?

Accepted Answer

Calculating the Variance Inflation Factor (VIF)

Answer

Checking for missing value patterns

Answer

Performing a chi-square test

Answer

Applying principal component analysis

Question 6

What is the key difference between label encoding and ordinal encoding for categorical variables?

Accepted Answer

Ordinal encoding respects a meaningful rank order while label encoding assigns arbitrary integers

Answer

Label encoding preserves order while ordinal encoding does not

Answer

Label encoding creates binary columns while ordinal encoding creates a single column

Answer

There is no difference; the terms are interchangeable