MCQs

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Overall MCQs for Reference

What is the primary goal of Exploratory Data Analysis (EDA)?
a. Predict outcomes
b. Summarize data characteristics
c. Visualize predictions
d. Build models
Answer: b. Summarize data characteristicsplacementpreparation
Which of the following best describes EDA?
a. Making data more complicated
b. Simplify complex data
c. Discover patterns and insights in data
d. Visualize data only
Answer: c. Discover patterns and insights in dataexamveda
Which type of analysis is NOT part of EDA?
a. Univariate Analysis
b. Bivariate Analysis
c. Multivariate Analysis
d. Predictive Analysis
Answer: d. Predictive Analysisgeeksforgeeks
What is the first step when performing EDA?
a. Visualizing data
b. Cleaning missing values
c. Understanding the problem and the data
d. Handling outliers
Answer: c. Understanding the problem and the datageeksforgeeks
Which plot is most useful for identifying the relationship between two numerical variables?
a. Bar chart
b. Scatter plot
c. Box plot
d. Pie chart
Answer: b. Scatter plotgeeksforgeeks
Which is a common technique used during EDA?
a. Clustering
b. PCA
c. Descriptive statistics
d. Feature selection
Answer: c. Descriptive statisticsplacementpreparation
How can missing values be handled during EDA?
a. Ignoring them completely
b. Replacing them with a fixed value or imputation techniques
c. Deleting the entire dataset
d. Randomly assigning new values
Answer: b. Replacing them with a fixed value or imputation techniquesgeeksforgeeks
What does multivariate analysis focus on?
a. Only one variable
b. Relationship between two variables
c. Relationship between three or more variables
d. Analysis of missing values
Answer: c. Relationship between three or more variablesgeeksforgeeks
What is the significance of identifying skewness in data during EDA?
a. It helps in feature scaling
b. It determines model type
c. It affects data distribution assumptions
d. It improves visualization
Answer: c. It affects data distribution assumptionsplacementpreparation
Which visualization is best suited for outlier detection?
a. Line plot
b. Box plot
c. Histogram
d. Scatter plot
Answer: b. Box plotitfeature
Which Python library is most commonly used for EDA visualizations?
a. Keras
b. Matplotlib
c. Tensorflow
d. PyTorch
Answer: b. Matplotlibplacementpreparation
What are the measures of central tendency?
a. Mean, median, mode
b. Skewness and kurtosis
c. Variance and standard deviation
d. Outliers and missing values
Answer: a. Mean, median, modescribd
Which form of EDA generates summaries about sample data?
a. Data visualization
b. Statistical summaries
c. Predictive models
d. Model validation
Answer: b. Statistical summariesitfeature
Identifying missing values in data is part of:
a. Data validation
b. Feature selection
c. Data cleaning
d. Model training
Answer: c. Data cleaningexamveda
Which technique helps in reducing dimensions in EDA?
a. Outlier removal
b. PCA
c. K-means clustering
d. Box plot
Answer: b. PCAitfeature
What does visualization in EDA aim to achieve?
a. Communicate patterns
b. Hide data details
c. Choose model architectures
d. Predict outcomes
Answer: a. Communicate patternsplacementpreparation
Which of the following is a principle of analytic graphics?
a. Show causality
b. Integrate multiple modes of evidence
c. Limit to one variable per plot
d. Use color for personal information
Answer: b. Integrate multiple modes of evidencewayground
Most often, EDA relies on:
a. Visual techniques
b. Fixed models
c. Assumptions
d. Testing for statistical significance
Answer: a. Visual techniqueswayground
Why identify multicollinearity during EDA?
a. Improve model accuracy
b. Ensure independence among predictors
c. Remove missing values
d. Select important features
Answer: b. Ensure independence among predictorsplacementpreparation
Which is NOT a characteristic of exploratory graphs?
a. Made slowly
b. Axes not cleaned up
c. Color for personal information
d. Made quickly to reveal features
Answer: d. Made quickly to reveal featuressanfoundry
In EDA, outlier detection helps in:
a. Hiding abnormal values
b. Identifying data issues
c. Building models
d. Reducing model complexity
Answer: b. Identifying data issuesitfeature
Which of the following is NOT a method to deal with outliers in EDA?
a. Remove them
b. Transform variables
c. Ignore completely
d. Impute with mean/median
Answer: c. Ignore completelyitfeature
What does a histogram visualize?
a. Relationship between variables
b. Distribution of a single variable
c. Outlier detection
d. Missing values
Answer: b. Distribution of a single variableitfeature
Which summary statistic shows the spread of data?
a. Mean
b. Median
c. Mode
d. Standard deviation
Answer: d. Standard deviationscribd
What is the role of exploratory graphs in data analysis?
a. Made for formal presentations
b. Made quickly and roughly
c. Used in place of formal modeling
d. Used only for categorical data
Answer: b. Made quickly and roughlysanfoundry
Which is true about the base plotting system in R?
a. Margins and spacings adjusted automatically
b. Plots created with multiple function calls
c. Annotation requires separate functions
d. Most useful for conditioning plots
Answer: c. Annotation requires separate functionssanfoundry
Which tool is most widely used for data manipulation in Python EDA?
a. Pandas
b. Seaborn
c. Scikit-learn
d. NumPy
Answer: a. Pandassanfoundry
What does the median represent in a dataset?
a. Most frequent value
b. Middle value when sorted
c. Average value
d. Spread of values
Answer: b. Middle value when sortedscribd
What does a box plot NOT visualize?
a. Mean
b. Quartiles
c. Outliers
d. Spread
Answer: a. Meanitfeature
Which data type is NOT suitable for correlation analysis?
a. Categorical
b. Numerical
c. Ordinal
d. Continuous
Answer: a. Categoricalitfeature
What is the purpose of hierarchical clustering in EDA?
a. Dimensionality reduction
b. Data grouping
c. Outlier detection
d. Variable transformation
Answer: b. Data groupingitfeature
What is true about K-means clustering?
a. Number of clusters must be specified
b. Always stops in three iterations
c. Works only on categorical data
d. Requires fixed centroids
Answer: a. Number of clusters must be specifieditfeature
What does the mean of a dataset represent?
a. Spread of the data
b. Center of the data
c. Frequency of values
d. Number of outliers
Answer: b. Center of the datascribd
Which statistical measure is best for skewed data?
a. Mean
b. Median
c. Mode
d. All equally good
Answer: b. Medianscribd
What is overfitting in EDA context?
a. Model captures noise instead of pattern
b. Too many graphs created
c. Loss of missing value information
d. High summary statistics
Answer: a. Model captures noise instead of patternsanfoundry
How do you identify missing data in pandas DataFrame?
a. df.info()
b. df.describe()
c. df.isnull().sum()
d. df.head()
Answer: c. df.isnull().sum()sanfoundry
What is ‘feature engineering’ in EDA?
a. Model training
b. Creating new variables
c. Evaluating performance
d. Reducing data size
Answer: b. Creating new variablessanfoundry
Which Python library is preferred for interactive visualizations during EDA?
a. Matplotlib
b. Plotly
c. Scikit-learn
d. PIL
Answer: b. Plotlysanfoundry
Normalization in EDA refers to:
a. Removing outliers
b. Rescaling features to standard range
c. Feature selection
d. Checking data types
Answer: b. Rescaling features to standard rangesanfoundry
What does .head() function in Pandas show?
a. Full dataset
b. Last five rows
c. First five rows
d. Summary statistics
Answer: c. First five rowssanfoundry
Which of the following methods can reduce skewness?
a. Log transformation
b. Outlier removal
c. Mean substitution
d. Missing value imputation
Answer: a. Log transformationplacementpreparation
Why visualize correlations in EDA?
a. Improve random assignment
b. Identify relationships
c. Remove non-numeric variables
d. Ignore outliers
Answer: b. Identify relationshipsplacementpreparation
Which ML algorithm is most sensitive to outliers found in EDA?
a. Decision Trees
b. Linear Regression
c. KNN
d. Random Forest
Answer: b. Linear Regressionplacementpreparation
Which of the following describes kurtosis?
a. Data position
b. Data spread
c. Tailedness of distribution
d. Missing value count
Answer: c. Tailedness of distributionscribd
What is ‘Data Leakage’ in context of EDA?
a. Loss of information between variables
b. Accessing unauthorized data
c. Training set data inadvertently influencing test set
d. Random data selection
Answer: c. Training set data inadvertently influencing test setsanfoundry
Which technique is NOT for dimensionality reduction?
a. PCA
b. t-SNE
c. Feature scaling
d. LDA
Answer: c. Feature scalingsanfoundry
The process of summarizing the main characteristics of data without using formal models is called:
a. Feature engineering
b. Exploratory Data Analysis
c. Predictive analytics
d. Model selection
Answer: b. Exploratory Data Analysiswayground
Which summary statistic is robust to outliers?
a. Mean
b. Median
c. Standard deviation
d. Range
Answer: b. Medianscribd
What does a violin plot visualize?
a. Data distribution shape + summary statistics
b. Only mean
c. Only standard deviation
d. Only outliers
Answer: a. Data distribution shape + summary statisticsitfeature
EDA is most important for which stage in Machine Learning?
a. Data preprocessing
b. Model deployment
c. Hyperparameter tuning
d. Prediction generation
Answer: a. Data preprocessingsanfoundry

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