Data Science

Data Wrangling MCQs With Answers

Welcome to the Data Wrangling MCQs with Answers. In this post, we have shared Data Wrangling Online Test for different competitive exams. Find practice Data Wrangling Practice Questions with answers in Computer Tests exams here. Each question offers a chance to enhance your knowledge regarding Data Wrangling.

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

Note: Answer of the questions will change randomly each time you start the test. Practice each quiz test at least 3 times if you want to secure High Marks. Once you are finished, click the View Results button. If any answer looks wrong to you in Quizzes. simply click on question and comment below that question. so that we can update the answer in the quiz section.

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Data Wrangling MCQs

Data Wrangling

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1 / 40

What is the purpose of data transformation?

2 / 40

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

3 / 40

What is the primary focus of data wrangling?

4 / 40

What is data wrangling?

5 / 40

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

6 / 40

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

7 / 40

What is one common method for handling missing data?

8 / 40

What is the purpose of data filtering in data wrangling?

9 / 40

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

10 / 40

What is the purpose of data discretization techniques like binning?

11 / 40

What is feature engineering in the context of data wrangling?

12 / 40

What is the purpose of data sampling?

13 / 40

What is the purpose of data augmentation in data wrangling?

14 / 40

What is the role of data validation in data wrangling?

15 / 40

What is the purpose of data normalization?

16 / 40

What is the role of data integration in data wrangling?

17 / 40

What does data encoding involve?

18 / 40

What is the purpose of data merging in data wrangling?

19 / 40

What is one method for detecting and handling data outliers?

20 / 40

What is the purpose of data imputation?

21 / 40

What is the purpose of data reshaping?

22 / 40

What is the first step in data wrangling?

23 / 40

What is the role of data discretization in data wrangling?

24 / 40

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

25 / 40

What does outlier detection involve?

26 / 40

What is the main goal of data preprocessing?

27 / 40

What is one method for handling duplicate data?

28 / 40

What is one approach to data normalization?

29 / 40

What is one technique for feature extraction in data wrangling?

30 / 40

What is one method for handling skewed data distributions?

31 / 40

What is a key challenge in data cleaning?

32 / 40

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

33 / 40

What is the purpose of data standardization?

34 / 40

What is data reduction in data wrangling?

35 / 40

What is the purpose of data deduplication in data wrangling?

36 / 40

What does data cleaning involve?

37 / 40

What is a common technique for data aggregation?

38 / 40

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

39 / 40

What is the role of data profiling in data wrangling?

40 / 40

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

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