MS-DS Master of Data science FREE MS-DS Master of Data science Data Wrangling and Preprocessing Questions and Answers 2

Question 1

Which pandas method is most appropriate for combining two DataFrames that share a common key column?

Accepted Answer

merge()

Answer

append()

Answer

concat()

Answer

stack()

Question 2

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

Accepted Answer

Converting categorical variables into binary columns

Answer

Normalizing numerical features to a 0-1 range

Answer

Removing duplicate rows from a dataset

Answer

Imputing missing values with the mode

Question 3

When dealing with a heavily right-skewed numerical feature, which transformation is commonly applied to reduce skewness?

Accepted Answer

Log transformation

Answer

Min-max scaling

Answer

Mean imputation

Answer

Label encoding

Question 4

What does the pandas melt() function accomplish in data wrangling?

Accepted Answer

Converts wide-format data to long-format

Answer

Removes rows with missing values

Answer

Merges multiple DataFrames vertically

Answer

Applies aggregate functions to grouped data

Question 5

Which strategy for handling missing data is most likely to introduce bias when data is not missing completely at random?

Accepted Answer

Listwise deletion

Answer

Multiple imputation

Answer

K-nearest neighbors imputation

Answer

Regression imputation

Question 6

In feature engineering, what is the purpose of creating interaction terms between two variables?

Accepted Answer

To capture the combined effect of two features that may not be represented by either alone

Answer

To reduce the dimensionality of the dataset

Answer

To normalize the distribution of both features

Answer

To remove multicollinearity between the features