Data Science Data Wrangling and Preprocessing 2

Question 1

A column of customer ages contains the value 999 for records where age was unknown. What is this an example of?

Accepted Answer

A sentinel value encoding missing data

Answer

A valid outlier

Answer

A categorical variable

Answer

A normalized feature

Question 2

In pandas, which method removes rows containing any missing values from a DataFrame?

Accepted Answer

df.dropna()

Answer

df.fillna()

Answer

df.isnull()

Answer

df.drop_duplicates()

Question 3

You merge two tables on a customer ID, but some IDs exist only in the left table. Which join keeps all left-table rows?

Accepted Answer

Left join

Answer

Inner join

Answer

Right join

Answer

Cross join

Question 4

A date column is stored as strings like '2026-06-30'. What preprocessing step lets you extract the month easily?

Accepted Answer

Convert the column to a datetime type

Answer

One-hot encode the strings

Answer

Standardize the column

Answer

Drop the column

Question 5

Which technique replaces missing numeric values with the column's median?

Accepted Answer

Median imputation

Answer

Min-max scaling

Answer

Label encoding

Answer

Binning

Question 6

Why is the median often preferred over the mean for imputing a skewed income column?

Accepted Answer

It is less affected by extreme high values

Answer

It is always larger than the mean

Answer

It removes the need for scaling

Answer

It converts the column to categorical

Question 7

What does 'tidy data' mean in the context of data wrangling?

Accepted Answer

Each variable is a column and each observation is a row

Answer

All values are numeric

Answer

There are no missing values

Answer

The data is sorted alphabetically