Data Cleaning and Preparation: A Comprehensive Guide for Data Science

Question 1

What is the primary goal of data normalization?

Accepted Answer

To ensure data consistency and comparability

Answer

To remove duplicate data points

Answer

To identify outliers and missing values

Answer

To reduce the dimensionality of the data

Question 2

How does data cleaning differ from data transformation?

Accepted Answer

Data cleaning rectifies errors and inconsistencies, while data transformation modifies the data structure or format.

Answer

Data transformation rectifies errors and inconsistencies, while data cleaning modifies the data structure or format.

Answer

Data cleaning and data transformation are essentially the same process.

Answer

Data transformation and data modeling are synonymous.

Question 3

What is the primary purpose of data transformation in data preparation?

Accepted Answer

To convert data into a format suitable for analysis

Answer

To remove duplicate records

Answer

To correct errors in the data

Answer

To identify outliers

Question 4

What is the key difference between an outlier and an anomaly in data analysis?

Accepted Answer

Outliers are extreme values within the distribution, while anomalies are unexpected or unusual patterns in the data.

Answer

Anomalies are extreme values within the distribution, while outliers are unexpected or unusual patterns in the data.

Answer

Outliers and anomalies are interchangeable terms.

Answer

There is no difference between outliers and anomalies.

Question 5

Which of the following data visualization techniques is most useful for identifying outliers?

Accepted Answer

Scatter plot

Answer

Bar chart

Answer

Pie chart

Answer

Heat map

Question 6

What is the main objective of data cleansing?

Accepted Answer

To remove inconsistencies and errors from the data

Answer

To transform data into a format suitable for analysis

Answer

To identify outliers

Answer

To reduce the dimensionality of the data

Question 7

What is the key distinction between data cleaning and data preparation?

Accepted Answer

Data cleaning focuses on removing errors and inconsistencies, while data preparation focuses on transforming data into a format suitable for analysis.

Answer

Data preparation focuses on removing errors and inconsistencies, while data cleaning focuses on transforming data into a format suitable for analysis.

Answer

Data cleaning and data preparation are the same process.

Answer

There is no difference between data cleaning and data preparation.

Question 8

What is the crucial role of data cleaning in the data science process?

Accepted Answer

To ensure that the data is accurate, consistent, and complete

Answer

To transform data into a format suitable for analysis

Answer

To identify and remove outliers

Answer

To reduce the dimensionality of the data

Question 9

The process of replacing missing values with a suitable replacement is known as what?

Accepted Answer

Data imputation

Answer

Data transformation

Answer

Data normalization

Answer

Data standardization

Question 10

Which data normalization technique scales data to a range between 0 and 1?

Accepted Answer

Min-max normalization

Answer

Z-score normalization

Answer

Decimal scaling

Answer

Logarithmic normalization

Question 11

What is the main objective of data standardization?

Accepted Answer

Facilitating comparison across different features

Answer

Eliminating outliers

Answer

Reducing data dimensionality

Answer

Enhancing model interpretability

Question 12

Which data type is most appropriate for representing dates and times in a dataset?

Accepted Answer

Date

Answer

Integer

Answer

String

Answer

Boolean

Question 13

Which of the following techniques is most commonly used to handle missing values in a dataset?

Accepted Answer

Imputation, replacing missing values with estimated or representative values

Answer

Deletion, removing rows or columns with missing values

Answer

Binning, grouping missing values into intervals or categories

Answer

Standardization, scaling data to a common range

Question 14

What is the technical term for extreme data points that deviate significantly from the rest of the dataset?

Accepted Answer

Outliers, extreme values that lie outside the normal distribution of the data

Answer

Errors, incorrect or corrupted data points

Answer

Anomalies, unexpected or unusual patterns in the data

Answer

Deviations, departures from the mean or median of the data

Question 15

Which data transformation technique is used to convert categorical variables, such as 'gender' or 'occupation,' into numerical values suitable for analysis?

Accepted Answer

One-hot encoding, creating binary columns for each category

Answer

Dummy coding, assigning numerical values to each category

Answer

Label encoding, assigning ordinal values to each category

Answer

Target encoding, assigning values based on the target variable

Question 16

What is the primary purpose of data normalization?

Accepted Answer

To scale data to a common range, making it easier for analysis

Answer

To remove outliers and extreme values from the data

Answer

To handle missing values by imputing or deleting them

Answer

To convert data to a specific format, such as a particular file type

Question 17

What is the process of combining multiple features into a single, more informative feature called?

Accepted Answer

Feature engineering, designing and creating new features

Answer

Feature selection, choosing the most relevant features for analysis

Answer

Feature extraction, creating new features from existing ones

Answer

Feature transformation, converting features to a different format

Question 18

Which of the following is a widely used software library for data cleaning and preparation in Python?

Accepted Answer

Pandas, a powerful library for data manipulation and analysis

Answer

SQL, a database query language used for data retrieval and management

Answer

Tableau, a data visualization tool used for creating interactive dashboards

Answer

MATLAB, a numerical computing environment primarily used for scientific and engineering applications

Question 19

Which step is not essential in assessing data quality?

Accepted Answer

Transforming the data

Answer

Profiling the data

Answer

Visualizing the data

Answer

Validating the data

Question 20

Which of the following is an uncommon technique for missing value imputation?

Accepted Answer

Random Imputation

Answer

Mean Imputation

Answer

K-Nearest Neighbors Imputation

Answer

Multiple Imputation

Question 21

Which data normalization technique scales data between 0 and 1, making it compatible with many machine learning algorithms?

Accepted Answer

Min-max normalization

Answer

Z-score normalization

Answer

Decimal scaling

Answer

Quantile normalization

Question 22

Which of the following techniques is commonly used to fill in missing values in a dataset?

Accepted Answer

Imputation

Answer

Normalization

Answer

Deletion

Answer

Aggregation

Question 23

What is the primary purpose of conducting an outlier analysis on a dataset?

Accepted Answer

To identify extreme or unusual data points that may warrant further investigation

Answer

To remove duplicate data entries

Answer

To convert continuous data to categorical data

Answer

To find the average and standard deviation of the data

Question 24

Which data transformation technique is commonly used to convert qualitative or categorical data into quantitative or numerical data?

Accepted Answer

One-hot encoding

Answer

Binning

Answer

Normalization

Answer

Standardization

Question 25

What is the primary objective of data preparation?

Accepted Answer

To transform raw data into a format that is suitable for analysis and modeling

Answer

To reduce data size

Answer

To improve data quality

Answer

To visualize data for presentation

Question 26

What is the primary importance of data validation in the data preparation process?

Accepted Answer

To ensure the accuracy, consistency, and integrity of the data

Answer

To make data ready for analysis

Answer

To improve data visualization

Answer

To reduce data size

Question 27

What is the recommended best practice for handling duplicate data entries?

Accepted Answer

Identify and remove duplicates to maintain data integrity

Answer

Impute missing values in the duplicate entries

Answer

Keep all duplicates to increase data accuracy

Question 28

In data cleaning, which technique specifically addresses the issue of missing values?

Accepted Answer

Imputation

Answer

Data transformation

Answer

Normalization

Answer

Outlier detection

Question 29

Outlier detection is utilized in data cleaning primarily for what purpose?

Accepted Answer

Identifying and removing data points that significantly deviate from the rest of the data

Answer

Transforming data into a normal distribution

Answer

Handling missing values

Question 30

Which data transformation technique is commonly employed to convert non-numerical categorical data into numerical representation?

Accepted Answer

One-hot encoding

Answer

Log transformation

Answer

Normalization

Answer

Standardization

Question 31

What is the overarching objective of data preparation?

Accepted Answer

To ensure the quality of data by enhancing its accuracy, completeness, and consistency, making it suitable for analysis

Answer

To prepare data for visualization purposes

Answer

To optimize data storage efficiency

Question 32

Which of the following is NOT included as a step in the data cleaning process?

Accepted Answer

Data visualization

Answer

Data validation

Answer

Data imputation

Answer

Data transformation

Question 33

Which technique is suitable for identifying duplicate records within a dataset?

Accepted Answer

Record linkage

Answer

Clustering

Answer

Dimensionality reduction

Answer

Regression analysis

Question 34

What is the primary benefit of incorporating data validation into data cleaning?

Accepted Answer

Ensures data integrity and consistency by verifying data against predetermined criteria

Answer

Improves data visualization capabilities

Answer

Reduces the need for manual data cleaning

Question 35

Which tool is widely used for data cleaning and preparation tasks?

Accepted Answer

Python's Pandas library

Answer

Power BI

Answer

Microsoft Excel

Answer

Tableau

Question 36

Describe the fundamental distinction between data cleaning and data transformation.

Accepted Answer

Data cleaning focuses on rectifying data quality issues, while data transformation converts data into a format suitable for analysis

Answer

Data cleaning is a subset of data transformation

Question 37

Which of the following techniques can be used to replace missing values in a dataset?

Accepted Answer

Imputation

Answer

Clustering

Answer

Dimensionality reduction

Answer

Deletion

Question 38

Which of the following is NOT a valid technique for handling missing values?

Accepted Answer

Imputation using random values

Answer

Mean imputation

Answer

Median imputation

Answer

K-nearest neighbors imputation

Question 39

Which of the following is a common method for detecting outliers in a dataset?

Accepted Answer

Z-score analysis

Answer

Clustering

Answer

Principal component analysis

Answer

Linear regression

Question 40

What is the primary purpose of data normalization?

Accepted Answer

To transform data values into a consistent range for analysis

Answer

To address missing values

Answer

To remove outliers

Answer

To convert categorical data into numerical data

Question 41

What is the fundamental goal of data cleaning?

Accepted Answer

To rectify errors, inconsistencies, and duplicate records within a dataset

Answer

To transform data into a suitable format for analysis

Answer

To reduce the size of the dataset

Question 42

What is the primary objective of data profiling?

Accepted Answer

To summarize and analyze data characteristics for better understanding

Answer

To cleanse and prepare data for analysis

Answer

To visualize data for presentation purposes

Question 43

Which of the following is NOT a type of missing value?

Accepted Answer

Unknown

Answer

Missing Completely At Random (MCAR)

Answer

Missing Not At Random (MNAR)

Answer

Missing At Random (MAR)

Question 44

What is the main purpose of outlier detection in data preparation?

Accepted Answer

To identify data points that deviate significantly from the rest of the data.

Answer

To remove all extreme values from the dataset.

Answer

To transform the data to a normal distribution.

Question 45

What is the key difference between feature scaling and normalization in data preparation?

Accepted Answer

Feature scaling transforms data to a specific range, while normalization transforms data to have a mean of 0 and a standard deviation of 1.

Answer

Feature scaling is applied to categorical data, while normalization is applied to numerical data.

Question 46

Which of the following is generally considered a best practice for data preparation?

Accepted Answer

Documenting the data cleaning and preparation process.

Answer

Deleting all outliers from the dataset.

Answer

Imputing missing values with the mean of the column.

Question 47

What is the primary purpose of data validation in data preparation?

Accepted Answer

To ensure that data meets specific quality criteria and constraints.

Answer

To identify and remove duplicate data.

Answer

To improve the accuracy of machine learning models.

Question 48

What is the main objective of data profiling in data preparation?

Accepted Answer

To summarize and analyze data to identify patterns, trends, and potential issues.

Answer

To validate data against business rules.

Answer

To clean and transform data.

Question 49

Why is data cleaning and preparation a crucial step in data analysis?

Accepted Answer

It ensures that data is accurate, consistent, and suitable for analysis and modeling.

Answer

It reduces the size of the dataset.

Answer

It improves the speed of data processing.

Question 50

Which of the following is NOT a typical data quality issue found in real-world datasets?

Accepted Answer

Data symmetry

Answer

Inconsistent data entries

Answer

Outliers

Answer

Missing data points

Question 51

What is the term for the process of substituting missing data values in a dataset?

Accepted Answer

Imputation

Answer

Normalization

Answer

Encoding

Answer

Feature scaling

Question 52

Which technique converts categorical data, like 'color' or 'gender', into numerical values for machine learning algorithms?

Accepted Answer

One-hot encoding

Answer

Standardization

Answer

Normalization

Answer

Data aggregation

Question 53

What is the process of identifying and handling extreme values that deviate significantly from other data points in a dataset called?

Accepted Answer

Outlier detection

Answer

Error analysis

Answer

Data validation

Answer

Data cleansing

Question 54

In data preparation, what is the process of creating a subset of data by selecting specific columns or rows from a dataset called?

Accepted Answer

Data filtering

Answer

Data aggregation

Answer

Data merging

Answer

Data sorting

Question 55

What is the term for combining data from two or more datasets into a single, unified dataset?

Accepted Answer

Data merging

Answer

Data aggregation

Answer

Data filtering

Answer

Data transformation

Question 56

Which of the following is NOT typically considered a core step in the data cleaning process?

Accepted Answer

Data modeling

Answer

Data quality assessment

Answer

Data transformation

Answer

Data validation

Question 57

What is the process of identifying and rectifying errors or inconsistencies within a dataset to ensure its accuracy and reliability called?

Accepted Answer

Data validation

Answer

Data analysis

Answer

Data preparation

Answer

Data cleaning