Data Wrangling MCQs With Answers

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Data Wrangling Online Quiz

By presenting 3 options to choose from, Data Wrangling Quiz which cover a wide range of topics and levels of difficulty, making them adaptable to various learning objectives and preferences. You will have to read all the given answers of Data Wrangling Questions and Answers and click over the correct answer.

• Test Name: Data Wrangling MCQ Quiz Practice
• Type: Quiz Test
• Total Questions: 40
• Total Marks: 40
• Time: 40 minutes

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Data Wrangling

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1 / 40

What is the role of data validation in data wrangling?

Adding noise to data

Visualizing data patterns

Ensuring data quality and consistency

2 / 40

What is the first step in data wrangling?

Data collection

Data visualization

Data modeling

3 / 40

What is one common method for handling missing data?

Feature selection

Data scaling

Imputation

4 / 40

What is the main goal of data preprocessing?

To collect raw data

To prepare data for analysis and modeling

To visualize data patterns

5 / 40

What is the purpose of data standardization?

To handle missing data

To transform data into a standard normal distribution

To add noise to data

6 / 40

What is the purpose of data transformation?

To add noise to data

To increase data complexity

To convert data into a suitable format for analysis

7 / 40

What is the purpose of exploratory data analysis (EDA)?

To analyze and summarize data characteristics

To impute missing values

To remove outliers

8 / 40

What is the benefit of using data pipelines in data wrangling?

They automate and streamline data processes

They create new features

They increase data complexity

9 / 40

What is a key challenge in data cleaning?

Creating new features

Selecting the best machine learning algorithm

Handling inconsistent data formats

10 / 40

What does data cleaning involve?

Creating new features

Handling missing data and removing duplicates

Analyzing data patterns

11 / 40

What is one approach to data normalization?

Dropping duplicates

Adding noise to data

Min-Max scaling

12 / 40

What is one method for handling skewed data distributions?

Log transformation

Data imputation

Feature scaling

13 / 40

What is one method for detecting and handling data outliers?

Statistical methods such as Z-score or IQR

Feature scaling

Data imputation

14 / 40

What does outlier detection involve?

Identifying and handling extreme values in data

Removing duplicates

Creating new variables

15 / 40

What is the purpose of data merging in data wrangling?

Normalizing data

Removing outliers

Combining datasets based on common variables

16 / 40

What is the purpose of data sampling?

To select a subset of data for analysis

To normalize data

To create new features

17 / 40

What does data encoding involve?

Handling missing data

Adding noise to data

Converting categorical variables into numerical values

18 / 40

What is the role of data validation techniques like cross-validation?

To assess model performance

To visualize data patterns

To remove duplicates

19 / 40

What is the purpose of data reshaping?

Removing missing values

Normalizing data

Restructuring data to meet analysis needs

20 / 40

What is one technique for feature extraction in data wrangling?

Data imputation

Principal Component Analysis (PCA)

Handling missing values

21 / 40

What is the primary focus of data wrangling?

Creating data visualizations

Preparing raw data for analysis

Creating machine learning models

22 / 40

What is the purpose of data aggregation functions like mean and sum?

To summarize data values within groups

To normalize data

To remove duplicates

23 / 40

What is the purpose of data normalization?

To add noise to data

To remove outliers

To bring data into a standard scale

24 / 40

What is the advantage of using regular expressions in data cleaning?

They facilitate pattern matching and extraction

They scale data

They remove outliers

25 / 40

What is feature engineering in the context of data wrangling?

Creating new features from existing data

Visualizing data patterns

Scaling numerical data

26 / 40

What is the role of data integration in data wrangling?

Visualizing data

Combining data from different sources

Removing outliers

27 / 40

What is the role of data profiling in data wrangling?

Adding noise to data

Analyzing data quality and structure

Scaling numerical data

28 / 40

What is the purpose of data imputation techniques like mean and median?

To replace missing data values

To remove outliers

To scale data

29 / 40

What is the purpose of data deduplication in data wrangling?

To create new features

To remove duplicate records or observations

To impute missing values

30 / 40

What is the benefit of using data visualization in data wrangling?

It adds noise to data

It helps identify data patterns and outliers visually

It scales data

31 / 40

What is the purpose of data filtering in data wrangling?

To subset data based on specific conditions

To scale data

To create new features

32 / 40

What is the purpose of data augmentation in data wrangling?

To increase the size and diversity of the dataset

To handle missing values

To reduce data complexity

33 / 40

What is data wrangling?

The process of analyzing data trends

The process of visualizing data

The process of cleaning and organizing raw data

34 / 40

What is the purpose of data discretization techniques like binning?

To handle missing values

To convert continuous data into categorical data

To scale data

35 / 40

What is data reduction in data wrangling?

Reducing the complexity of data while retaining important information

Removing duplicates

Increasing the size of the dataset

36 / 40

What is the role of data discretization in data wrangling?

Handling missing values

Converting continuous data into categorical data

Creating new features

37 / 40

What is one method for handling duplicate data?

Dropping duplicates based on specific columns

Creating new features

Imputing missing values

38 / 40

What is a common technique for data aggregation?

Grouping data by a specific variable and applying a function

Creating scatter plots

Scaling data

39 / 40

What is the purpose of data imputation?

To remove outliers

To visualize data distributions

To fill in missing data values

40 / 40

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

To visualize data patterns

To create new features

To ensure data quality and consistency

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