Data Cleaning and Transformation: Master Data Preprocessing for Machine Learning

Question 1

Which technique is NOT specifically used for handling missing data in a dataset?

Accepted Answer

Normalization

Answer

Imputation

Answer

Deletion

Answer

Clustering

Question 2

Which technique for handling missing numerical data involves replacing missing values with the average or middle value of known data?

Accepted Answer

Imputation with mean or median

Answer

Deletion of incomplete rows

Answer

Machine learning model-based imputation

Answer

Retention of missing values

Question 3

Which characteristic distinguishes an outlier in a dataset?

Accepted Answer

Significant deviation from the majority of data

Answer

Frequent occurrence

Answer

Essential for understanding data distribution

Answer

Necessity for removal

Question 4

Explain the distinction between normalization and standardization.

Accepted Answer

Normalization scales data between 0 and 1, while standardization scales data to have a mean of 0 and a standard deviation of 1

Answer

Standardization scales data between 0 and 1, while normalization scales data to have a mean of 0 and a standard deviation of 1

Answer

Normalization and standardization are identical processes

Answer

Normalization is applied to numerical data, whereas standardization is used for categorical data

Question 5

What is the purpose of removing outliers from a dataset?

Accepted Answer

Improved model accuracy and reduced bias

Answer

Normalization of data distribution

Answer

Reduction of dataset size

Answer

Enhanced visual appeal of data

Question 6

Discuss the ethical considerations associated with data cleaning and transformation. How can these techniques impact data privacy, bias mitigation, and the responsible use of data in machine learning applications?

Accepted Answer

Data cleaning and transformation can potentially introduce biases into the data.

Answer

Data cleaning and transformation have no ethical implications.

Answer

Ethical considerations should be addressed only after data cleaning and transformation.

Answer

Data scientists bear no ethical responsibility for the downstream use of cleaned and transformed data.

Question 7

Which of the following methods is NOT commonly used for handling missing values?

Accepted Answer

Ignoring them

Answer

Imputation

Answer

Average imputation

Answer

Deletion

Question 8

Which transformation technique is commonly used to convert categorical variables into numerical values?

Accepted Answer

One-hot encoding

Answer

Binning

Answer

Imputation

Answer

Scaling

Question 9

When choosing a data cleaning or transformation technique, which factor should be a primary consideration?

Accepted Answer

Characteristics of the data

Answer

Time constraints

Answer

Desired outcome

Answer

Available tools

Question 10

For data with a skewed distribution, which normalization technique is most appropriate?

Accepted Answer

Log transformation

Answer

Min-max normalization

Answer

Decimal scaling

Answer

Mean-standardization

Question 11

Which practice is essential for effective data cleaning and transformation?

Accepted Answer

Thorough documentation of all steps and decisions

Answer

Ignoring outliers unless they are extreme

Answer

Applying transformations in a predefined order

Answer

Utilizing the most recent technological tools

Question 12

What is the main purpose of data normalization?

Accepted Answer

To scale different features of data to a common range

Answer

To remove outliers

Answer

To impute missing values

Answer

To reduce the dimensionality of the data

Question 13

Which of the following statistical measures is used to detect outliers?

Accepted Answer

Z-score

Answer

Median

Answer

Mean

Answer

Standard deviation

Question 14

What is the fundamental difference between data cleaning and data transformation?

Accepted Answer

Data cleaning focuses on correcting errors and inconsistencies, while data transformation prepares the data for modeling.

Answer

Data cleaning is only about removing duplicates, while data transformation encompasses all other cleaning tasks.

Question 15

Which data transformation technique is commonly employed to reduce dimensionality?

Accepted Answer

Principal Component Analysis (PCA)

Answer

Normalization

Answer

Binning

Answer

Encoding

Question 16

What is the primary purpose of binning in data cleaning?

Accepted Answer

To categorize continuous data into discrete intervals

Answer

To eliminate outliers

Answer

To impute missing values

Question 17

What do we call the process of converting categorical data into numerical data?

Accepted Answer

Encoding

Answer

Normalization

Answer

Binning

Answer

Scaling

Question 18

Which of the following Python libraries is extensively used for data cleaning and transformation?

Accepted Answer

Pandas

Answer

TensorFlow

Answer

scikit-learn

Answer

NumPy

Question 19

What is the ultimate objective of data validation in data cleaning?

Accepted Answer

To ensure the reliability and accuracy of the data

Answer

To remove potential outliers

Answer

To estimate missing data points

Question 20

Which of the following is a method for handling missing values in data cleaning?

Accepted Answer

Imputation

Answer

Outlier detection

Answer

Feature scaling

Answer

Normalization

Question 21

What is the purpose of outlier detection in data transformation?

Accepted Answer

To identify and remove extreme values that may affect model performance

Answer

To impute missing values

Answer

To normalize the data to a common scale

Question 22

Which of the following is a benefit of data cleaning and transformation?

Accepted Answer

Improved model performance and reduced computation time

Answer

Increased data security

Answer

Reduced data storage requirements

Question 23

What is the purpose of tokenization in text data preprocessing?

Accepted Answer

To break down text data into individual words or tokens

Answer

To remove stop words from the text

Answer

To stem words to their root form

Question 24

Which of the following is a method for detecting and correcting data inconsistencies?

Accepted Answer

Data validation

Answer

Data normalization

Answer

Outlier removal

Answer

Feature engineering

Question 25

Which of the following is NOT a common technique for handling missing values in a dataset?

Accepted Answer

Replacing missing values with a random number within the data range.

Answer

Deleting rows or columns with missing values.

Answer

Imputing missing values with the mean, median, or mode.

Question 26

You are working with a dataset containing income data.  A few values are significantly higher than the rest, skewing the distribution. What is the most appropriate method to handle these outliers?

Accepted Answer

Winsorization.

Answer

Deleting the outliers.

Answer

Replacing outliers with the mean.

Question 27

What is the primary purpose of data normalization?

Accepted Answer

To bring all features to a similar scale.

Answer

To eliminate missing values.

Answer

To remove outliers.

Answer

To transform categorical data into numerical data.

Question 28

Which of the following data transformation techniques is best suited for handling skewed data distributions?

Accepted Answer

Log transformation.

Answer

Standardization.

Answer

Min-Max scaling.

Answer

One-hot encoding.

Question 29

You are building a machine learning model to predict house prices.  The dataset includes features like 'square footage', 'number of bedrooms', and 'location'.  What is the most appropriate data cleaning step to address the 'location' feature?

Accepted Answer

Encoding categorical features using one-hot encoding.

Answer

Imputing missing values with the mean.

Answer

Winsorizing the outliers.

Question 30

What is the primary difference between standardization and normalization?

Accepted Answer

Standardization centers and scales data to a standard normal distribution, while normalization scales data to a specific range.

Answer

Normalization removes outliers, while standardization does not.

Question 31

You are analyzing a dataset containing customer purchase history.  Some customers have made multiple purchases, while others have only made one.  What is an effective data transformation technique to handle this?

Accepted Answer

Creating a new feature to represent the number of purchases per customer.

Answer

Deleting customers with only one purchase.

Answer

Replacing the number of purchases with the average number of purchases.

Question 32

What is the main reason for using data cleaning techniques before building a machine learning model?

Accepted Answer

To improve the accuracy and performance of the model.

Answer

To make the data more visually appealing.

Answer

To reduce the size of the dataset.

Question 33

Which of the following is NOT a common type of data cleaning error?

Accepted Answer

Data redundancy.

Answer

Missing values.

Answer

Inconsistent data.

Answer

Outliers.

Question 34

You are analyzing a dataset containing customer demographics.  The 'age' feature has inconsistent units (some are in years, others in months).  What is the most appropriate data cleaning step?

Accepted Answer

Convert all ages to a consistent unit (e.g., years).

Answer

Replace inconsistent units with the mean age.

Answer

Delete rows with inconsistent units.

Question 35

Which of the following techniques is used to replace missing values with the mean of the column?

Accepted Answer

Imputation with mean

Answer

Smoothing

Answer

Replacing with zeros

Answer

Deleting the entire row

Question 36

Which of the following is NOT a type of outlier detection method?

Accepted Answer

Regression analysis

Answer

Kernel density estimation

Answer

Interquartile range method

Answer

Z-score method

Question 37

What is the purpose of data normalization?

Accepted Answer

To bring data into a consistent format and scale

Answer

To impute missing values

Answer

To remove outliers

Answer

To reduce dimensionality

Question 38

Which of the following is a benefit of using a data cleaning and transformation pipeline?

Accepted Answer

Improved efficiency and reproducibility

Answer

Reduced data accuracy

Answer

Increased data complexity

Answer

Increased model bias

Question 39

What is the difference between scaling and standardization in data transformation?

Accepted Answer

Scaling preserves zero, while standardization does not

Answer

Standardization preserves zero, while scaling does not

Answer

They are the same technique

Question 40

Which of the following is a potential cause of dirty data?

Accepted Answer

Human error

Answer

Cyber attacks

Answer

Data breaches

Answer

Hardware failures

Question 41

What is the role of data governance in data cleaning and transformation?

Accepted Answer

To ensure data quality and compliance

Answer

To develop machine learning models

Answer

To store and manage data

Answer

To perform data analysis

Question 42

Which of the following is a challenge associated with data transformation?

Accepted Answer

Understanding the business context

Answer

Cleaning the data

Answer

Selecting the right tools

Answer

Finding enough data

Question 43

What is the first step in the data cleaning process?

Accepted Answer

Data profiling

Answer

Data imputation

Answer

Data transformation

Question 44

Which of the following is a best practice for data transformation and cleaning?

Accepted Answer

Use a consistent set of rules and standards

Answer

Manually clean the data

Answer

Ignore data inconsistencies

Answer

Transform the data as much as possible