Data Cleaning and Transformation: Master Data Preparation for Analysis

Question 1

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

Accepted Answer

Min-max scaling

Answer

Mean normalization

Answer

Z-score standardization

Answer

Robust scaling

Question 2

Which of the following data cleaning tools is proprietary?

Accepted Answer

RapidMiner

Answer

Pandas

Answer

NumPy

Answer

OpenRefine

Question 3

What is the initial stage of the data cleaning process?

Accepted Answer

Data profiling

Answer

Data transformation

Answer

Data visualization

Answer

Missing value handling

Question 4

Which of the following visualizations is most suitable for identifying missing values?

Accepted Answer

Heatmap

Answer

Line chart

Answer

Bar chart

Answer

Scatter plot

Question 5

Which of the following techniques is most effective for handling missing values when the underlying distribution is known?

Accepted Answer

Imputation with mean or median

Answer

Dropping the rows with missing values

Answer

Replacing missing values with zeros

Answer

Creating a new variable to flag missing values

Question 6

Data normalization is primarily used to:

Accepted Answer

Transform data to a common scale for better comparison

Answer

Remove outliers

Answer

Handle missing values

Answer

Enhance model accuracy

Question 7

Which of the following is a potential drawback of data standardization?

Accepted Answer

Reduced data variability

Answer

Enhanced interpretability

Answer

Improved model performance

Answer

Increased data consistency

Question 8

The process of converting non-numerical data into numerical data for analysis is known as:

Accepted Answer

Encoding

Answer

Labeling

Answer

Binning

Answer

Quantization

Question 9

The process of removing duplicate records from a dataset is referred to as:

Accepted Answer

Deduplication

Answer

Merging

Answer

Joining

Answer

Aggregation

Question 10

How does effective data cleaning enhance the accuracy of data analysis?

Accepted Answer

Improves data quality by removing errors and inconsistencies, resulting in more reliable data for analysis.

Answer

Reduces data volume, making analysis faster but not necessarily more accurate.

Answer

Converts data into a specific format, which may or may not improve accuracy depending on the analysis method.

Question 11

Which factors should be considered when choosing the best data transformation method for a dataset?

Accepted Answer

Data distribution and data types

Answer

Only data distribution

Answer

Only data types

Question 12

Which method replaces missing values with statistical measures like mean or median?

Accepted Answer

Imputation

Answer

Interpolation

Answer

Extrapolation

Answer

Aggregation

Question 13

Which of the following is NOT a method for dealing with outliers?

Accepted Answer

Smoothing

Answer

Winsorization

Answer

Capping

Answer

Deletion

Question 14

How does data normalization differ from data standardization?

Accepted Answer

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

Answer

Normalization converts data to a common scale, while standardization removes outliers

Answer

Normalization handles missing values, while standardization does not

Answer

There is no difference between normalization and standardization

Question 15

What is the purpose of data aggregation?

Accepted Answer

To combine data into summarized statistics

Answer

To remove duplicate data points

Answer

To handle missing values

Answer

To improve data quality

Question 16

Which data quality metric is specifically designed to assess the uniformity and coherence of data values within a dataset?

Accepted Answer

Consistency

Answer

Completeness

Answer

Accuracy

Answer

Validity

Question 17

What is the primary goal of data normalization?

Accepted Answer

To convert data to a common range

Answer

To remove outliers

Answer

To fill in missing values

Answer

To detect data inconsistencies

Question 18

What is the primary purpose of data normalization?

Accepted Answer

To transform data values into a common range

Answer

To reduce data dimensionality

Answer

To remove outliers

Answer

To enhance data visualization

Question 19

Which potential concern arises when imputing missing values using mean or median?

Accepted Answer

Bias introduction

Answer

Increased data variance

Answer

Generation of unrealistic values

Answer

Excessive computational demand

Question 20

Which normalization technique involves subtracting the mean and dividing by the standard deviation?

Accepted Answer

Z-score normalization

Answer

Min-max normalization

Answer

Max-abs normalization

Answer

Decimal scaling

Question 21

What is the primary objective of data normalization?

Accepted Answer

Scale data within a consistent range

Answer

Enhance model performance

Answer

Eliminate outliers

Answer

Simplify data interpretation

Question 22

What is the purpose of a data dictionary?

Accepted Answer

To document the structure and meaning of data elements

Answer

To enhance data accessibility

Answer

To ensure data consistency

Answer

To accelerate data processing

Question 23

Which of the following is NOT a data cleaning tool?

Accepted Answer

Tableau

Answer

OpenRefine

Answer

DataCleanerR

Answer

Pandas

Question 24

What is the primary purpose of data normalization?

Accepted Answer

To scale data to a common range

Answer

To remove outliers

Answer

To handle missing values

Answer

To transform data into a normal distribution

Question 25

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

Accepted Answer

Data cleaning removes errors and inconsistencies, while data transformation prepares data for analysis.

Answer

Data transformation removes errors and inconsistencies, while data cleaning prepares data for analysis.

Answer

Data cleaning and data transformation are the same process.

Answer

Data transformation is a subset of data cleaning.

Question 26

What is the main purpose of data imputation?

Accepted Answer

To fill in missing values with estimated values

Answer

To remove outliers

Answer

To transform data into a normal distribution

Answer

To standardize data

Question 27

What is the primary purpose of data standardization?

Accepted Answer

To scale data variables to a common scale for improved comparability.

Answer

To reduce the dimensionality of the data.

Answer

To handle missing values.

Answer

To remove outliers from the dataset.

Question 28

What is the ultimate goal of data cleaning and transformation?

Accepted Answer

To enhance data quality for accurate analysis and visualization.

Answer

To reduce the size of the dataset.

Answer

To improve the visual appeal of the data.

Question 29

Which of the following is NOT a benefit of data transformation?

Accepted Answer

It can increase data complexity.

Answer

It can enhance data accuracy.

Answer

It can improve data interpretability.

Answer

It can facilitate data integration across systems.

Question 30

In the data cleaning process, what step should be taken first?

Accepted Answer

Data exploration

Answer

Data visualization

Answer

Data imputation

Answer

Data transformation

Question 31

What is the purpose of binning in the context of data transformation?

Accepted Answer

To group numeric data into predefined intervals or bins.

Answer

To remove duplicate values from a dataset.

Answer

To identify outliers in a dataset.

Answer

To convert categorical data into numerical data.

Question 32

Which data cleaning technique involves replacing missing values with the average value of the column?

Accepted Answer

Mean imputation

Answer

Interpolation

Answer

Deletion

Answer

Mode imputation

Question 33

What is the main purpose of data normalization?

Accepted Answer

To scale data values to a common range for better analysis and comparability

Answer

To remove duplicate values

Answer

To identify outliers

Question 34

Why is it important to deal with outliers in data analysis?

Accepted Answer

Because they can potentially skew analysis results and lead to incorrect conclusions

Answer

To improve data visualization

Answer

To increase data accuracy

Answer

To identify data errors

Question 35

Which of the following is NOT a statistical method for detecting outliers?

Accepted Answer

Regression analysis

Answer

Box-plot analysis

Answer

Z-score analysis

Question 36

What is the process of converting categorical data into a numerical format called?

Accepted Answer

Data encoding

Answer

Data normalization

Answer

Data decoding

Answer

Data transformation

Question 37

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

Accepted Answer

Carefully considering the context and impact of any data modifications

Answer

Transforming data without documenting the changes made

Answer

Deleting all missing values without considering the potential impact

Answer

Using complex algorithms even when simpler ones are sufficient

Question 38

Which of the following techniques is used to fill missing values in a dataset, based on logical reasoning or statistical estimation?

Accepted Answer

Imputation

Answer

Aggregation

Answer

Discretization

Answer

Normalization

Question 39

Which of the following is a common method for identifying extreme values or anomalies in a dataset?

Accepted Answer

Z-score

Answer

Correlation analysis

Answer

Regression

Answer

Clustering

Question 40

What is the primary purpose of scaling data in data cleaning and transformation?

Accepted Answer

To ensure data attributes are on a common or comparable scale.

Answer

To handle missing values

Answer

To transform data into a categorical form

Answer

To remove outliers

Question 41

What is the key difference between data standardization and data normalization?

Accepted Answer

Standardization involves subtracting the mean and dividing by the standard deviation, while normalization scales the data to a specific range.

Answer

Standardization scales the data to a specific range, while normalization subtracts the mean and divides by the standard deviation.

Question 42

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

Accepted Answer

Pandas

Answer

Matplotlib

Answer

NumPy

Question 43

What is the primary goal of data cleaning in data analysis?

Accepted Answer

Improve the quality and accuracy of data for effective analysis and decision-making.

Answer

Prepare data for visualization only.

Answer

Reduce data storage requirements.

Question 44

Which type of data transformation involves converting continuous data into distinct and meaningful categories?

Accepted Answer

Discretization

Answer

Aggregation

Answer

Normalization

Answer

Standardization

Question 45

Which technique is used to estimate and replace missing values based on the relationships and patterns within the dataset?

Accepted Answer

Imputation

Answer

Interpolation

Answer

Deletion

Answer

Normalization

Question 46

What is the process of identifying and addressing extreme values that significantly deviate from the rest of the data known as?

Accepted Answer

Outlier detection

Answer

Data transformation

Answer

Data filtering

Answer

Data validation

Question 47

What is the purpose of data standardization?

Accepted Answer

To transform data to have a mean of 0 and standard deviation of 1

Answer

To remove missing values

Answer

To convert data to a binary format

Question 48

What is the first step in the data cleaning process?

Accepted Answer

Data exploration

Answer

Outlier detection

Answer

Missing value handling

Answer

Data transformation

Question 49

Which tool is commonly used for data cleaning and transformation in Python?

Accepted Answer

Pandas

Answer

scikit-learn

Answer

NumPy

Question 50

What is the technique used to convert categorical data into numerical values?

Accepted Answer

Encoding

Answer

Binarization

Answer

Discretization

Answer

Normalization

Question 51

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

Accepted Answer

Improved data quality and accuracy

Answer

Enhanced data security

Answer

Reduced data storage requirements

Answer

Faster data processing