Exploratory Data Analysis MCQs – Class 12 Data Science (844)
Exploratory Data Analysis MCQs built to ace your score — exam-oriented questions covering every topic in the CBSE Class 12 Data Science syllabus with zero gaps.
Q1. Which of the following best describes Exploratory Data Analysis (EDA)?
a) A process of building machine learning models
b) A process of carrying out an initial analysis of available data to find patterns and anomalies
c) A process of collecting new data through surveys
d) A process of deploying models to production
Q2. EDA is primarily conducted with the help of:
a) Hypothesis testing alone
b) Summary statistics and graphical representations
c) Only pie charts
d) Only variance calculations
Q3. In real-life practice, which type of EDA technique is used the most?
a) Statistical
b) Graphical
c) Both are used equally
d) Neither is used
Q4. A data scientist wants to analyse only the height variable of a dataset without studying its relationship with any other variable. Which analysis should be performed?
a) Bivariate analysis
b) Multivariate analysis
c) Univariate analysis
d) Cluster analysis
Q5. Which of the following is NOT a statistical method mentioned for univariate analysis?
a) Mean
b) Median
c) Variance
d) Correlation between two variables
Q6. Which graph is particularly useful for identifying outliers and viewing quantile ranges?
a) Pie chart
b) Box plot
c) Line chart
d) Frequency polygon
Q7. A histogram is used to represent:
a) Frequency distribution versus range
b) Relationship between two variables
c) Only categorical variables
d) Missing values only
Q8. A scatter plot of a single variable mainly helps in:
a) Showing correlation with another variable
b) Visualising the spread of data points of that variable
c) Showing frequency of categories
d) Showing clusters of images
Q9. Assertion (A): EDA is useful for finding patterns and spotting anomalies in data.
Reason (R): EDA uses summary statistics and graphical representations to analyse data.
a) Both Assertion and Reason are true, and Reason is the correct explanation of Assertion.
b) Both Assertion and Reason are true, but Reason is not the correct explanation of Assertion.
c) Assertion is true, but Reason is false.
d) Assertion is false, but Reason is true.
Q10. Assertion (A): In univariate analysis, relationships between two variables are analysed.
Reason (R): Univariate analysis deals with only one variable from the dataset.
a) Both Assertion and Reason are true, and Reason is the correct explanation of Assertion.
b) Both Assertion and Reason are true, but Reason is not the correct explanation of Assertion.
c) Assertion is true, but Reason is false.
d) Assertion is false, but Reason is true.
Q11. Assertion (A): A box plot can help identify outliers.
Reason (R): A box plot helps us see the quantile ranges of a variable and whether outliers are present.
a) Both Assertion and Reason are true, and Reason is the correct explanation of Assertion.
b) Both Assertion and Reason are true, but Reason is not the correct explanation of Assertion.
c) Assertion is true, but Reason is false.
d) Assertion is false, but Reason is true.
Q12. The box plot is especially useful for:
a) Showing quantile ranges and detecting outliers
b) Showing frequency of categories
c) Showing correlation between variables
d) Showing image clusters
Q13. Bivariate analysis is carried out mainly to determine:
a) Summary statistics of a single variable
b) The relationship between two variables
c) The number of clusters
d) Missing values in a dataset
Q14. Which graphical method is NOT used for bivariate analysis, as per the text?
a) Scatter plot
b) Line chart
c) Pair plot
d) Frequency polygon
Q15. A pair plot is primarily used for:
a) Univariate analysis
b) Bivariate analysis
c) Data cleaning
d) Removing outliers
Q16. The main objective of bivariate analysis is to:
a) Remove duplicate observations
b) Find relationships between variables
c) Convert text into numerical data
d) Remove all outliers
Q17. Which analysis is used to analyse more than two variables in a dataset?
a) Univariate analysis
b) Bivariate analysis
c) Multivariate analysis
d) Frequency analysis
Q18. Which of the following is mentioned as a method of multivariate analysis?
a) Principal Component Analysis
b) Pie chart
c) Histogram
d) Frequency table
Q19. Clustering, as an unsupervised learning technique, finds application in:
a) Fixing data type issues
b) Image compression and pattern recognition
c) Removing duplicate rows
d) Bivariate correlation only
Q20. Data cleaning refers to:
a) Collecting fresh data from the field
b) Identifying and correcting/removing incorrect, incomplete or inaccurate data
c) Building predictive models
d) Creating pair plots
Q21. Duplicate observations in a dataset most frequently arise:
a) During model training
b) While combining datasets from multiple sources or scraping data online
c) During feature scaling
d) While plotting scatter plots
Q22. Removing irrelevant observations from a dataset primarily helps to:
a) Increase duplicate values
b) Ensure the model learns only from good, relevant data
c) Increase the number of outliers
d) Add unnecessary columns
Q23. Which statement about outliers is correct?
a) Every outlier must always be removed
b) Outliers can never affect models
c) Some outliers may be valid data and should be preserved
d) Outliers are the same as duplicate observations
Q24. Which model is stated to be less robust to outliers than decision tree models?
a) Linear regression
b) Cluster analysis
c) Principal Component Analysis
d) Canonical Correlation Analysis
Q25. The two common techniques used to handle missing data are:
a) Removing the row, or inserting a value close to the mean/mode
b) Deleting the entire dataset
c) Converting numbers into text
d) Creating a pair plot
Q26. Which sequence correctly represents important data-cleaning steps discussed in the chapter?
a) Remove duplicates → Remove irrelevant observations → Remove unwanted outliers → Fix data types → Handle missing data
b) Fix data types → Create graphs → Add duplicates → Remove variables → Build model
c) Handle missing data → Add outliers → Create duplicates → Fix graphs → Cluster
d) Create charts → Build model → Remove duplicates → Add irrelevant observations → Ignore missing data
Q27. Statement I: Data cleaning refers to identifying incorrect, incomplete and inaccurate data.
Statement II: Data cleaning always requires deleting the entire dataset.
a) Both Statement I and Statement II are true
b) Both Statement I and Statement II are false
c) Statement I is true, Statement II is false
d) Statement I is false, Statement II is true
Q28. Statement I: Bivariate analysis is a good way to measure correlations between two variables.
Statement II: Univariate analysis techniques involve only statistical methods and no graphical methods.
a) Both Statement I and Statement II are true
b) Both Statement I and Statement II are false
c) Statement I is true, Statement II is false
d) Statement I is false, Statement II is true
