DA Data Cleaning and Preparation

Question 1

What is the most common method for handling missing numeric values in a dataset?

Accepted Answer

Replace with the column mean, median, or mode

Answer

Delete the entire column

Answer

Replace with zero always

Answer

Flag the dataset as unusable

Question 2

What is data normalization?

Accepted Answer

Scaling numeric values to a common range such as 0 to 1

Answer

Removing duplicate records

Answer

Sorting data alphabetically

Answer

Converting strings to uppercase

Question 3

Which of the following best describes an outlier?

Accepted Answer

A value significantly different from the rest of the dataset

Answer

A data point that is exactly equal to the mean

Answer

A missing value in the dataset

Answer

A duplicated record

Question 4

What does data deduplication mean?

Accepted Answer

Identifying and removing duplicate records

Answer

Adding new records to a dataset

Answer

Splitting a dataset into training and test sets

Answer

Converting data types

Question 5

What is a data type mismatch issue?

Accepted Answer

When a column stores values in an incorrect format for its intended data type

Answer

When two datasets have different numbers of rows

Answer

When column names differ between tables

Answer

When a dataset has no primary key

Question 6

What is one-hot encoding used for in data preparation?

Accepted Answer

Converting categorical variables into binary numeric columns

Answer

Normalizing numeric columns

Answer

Filling missing values

Answer

Reducing dataset dimensions