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 techniques like cross-validation?

To remove duplicates

To visualize data patterns

To assess model performance

2 / 40

What is the main goal of data preprocessing?

To visualize data patterns

To collect raw data

To prepare data for analysis and modeling

3 / 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

4 / 40

What is one method for detecting and handling data outliers?

Statistical methods such as Z-score or IQR

Feature scaling

Data imputation

5 / 40

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

To remove outliers

To impute missing values

To analyze and summarize data characteristics

6 / 40

What is the purpose of data normalization?

To bring data into a standard scale

To remove outliers

To add noise to data

7 / 40

What is the purpose of data filtering in data wrangling?

To create new features

To subset data based on specific conditions

To scale data

8 / 40

What is the role of data validation in data wrangling?

Adding noise to data

Ensuring data quality and consistency

Visualizing data patterns

9 / 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

10 / 40

What is one method for handling duplicate data?

Dropping duplicates based on specific columns

Imputing missing values

Creating new features

11 / 40

What is data wrangling?

The process of cleaning and organizing raw data

The process of visualizing data

The process of analyzing data trends

12 / 40

What does data encoding involve?

Converting categorical variables into numerical values

Adding noise to data

Handling missing data

13 / 40

What does data cleaning involve?

Handling missing data and removing duplicates

Creating new features

Analyzing data patterns

14 / 40

What is one approach to data normalization?

Min-Max scaling

Dropping duplicates

Adding noise to data

15 / 40

What is the first step in data wrangling?

Data collection

Data modeling

Data visualization

16 / 40

What is a key challenge in data cleaning?

Selecting the best machine learning algorithm

Handling inconsistent data formats

Creating new features

17 / 40

What is the purpose of data reshaping?

Removing missing values

Restructuring data to meet analysis needs

Normalizing data

18 / 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

19 / 40

What is one method for handling skewed data distributions?

Data imputation

Feature scaling

Log transformation

20 / 40

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

To summarize data values within groups

To remove duplicates

To normalize data

21 / 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

22 / 40

What is the role of data profiling in data wrangling?

Adding noise to data

Scaling numerical data

Analyzing data quality and structure

23 / 40

What is feature engineering in the context of data wrangling?

Visualizing data patterns

Creating new features from existing data

Scaling numerical data

24 / 40

What is the purpose of data merging in data wrangling?

Combining datasets based on common variables

Removing outliers

Normalizing data

25 / 40

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

It helps identify data patterns and outliers visually

It scales data

It adds noise to data

26 / 40

What is the purpose of data sampling?

To create new features

To select a subset of data for analysis

To normalize data

27 / 40

What is the primary focus of data wrangling?

Creating data visualizations

Preparing raw data for analysis

Creating machine learning models

28 / 40

What does outlier detection involve?

Identifying and handling extreme values in data

Removing duplicates

Creating new variables

29 / 40

What is one common method for handling missing data?

Imputation

Feature selection

Data scaling

30 / 40

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

They scale data

They facilitate pattern matching and extraction

They remove outliers

31 / 40

What is one technique for feature extraction in data wrangling?

Data imputation

Principal Component Analysis (PCA)

Handling missing values

32 / 40

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

To remove outliers

To replace missing data values

To scale data

33 / 40

What is the role of data integration in data wrangling?

Visualizing data

Combining data from different sources

Removing outliers

34 / 40

What is data reduction in data wrangling?

Removing duplicates

Increasing the size of the dataset

Reducing the complexity of data while retaining important information

35 / 40

What is the role of data discretization in data wrangling?

Handling missing values

Creating new features

Converting continuous data into categorical data

36 / 40

What is the purpose of data transformation?

To convert data into a suitable format for analysis

To add noise to data

To increase data complexity

37 / 40

What is a common technique for data aggregation?

Scaling data

Grouping data by a specific variable and applying a function

Creating scatter plots

38 / 40

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

To ensure data quality and consistency

To create new features

To visualize data patterns

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 purpose of data standardization?

To add noise to data

To transform data into a standard normal distribution

To handle missing data

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