MCQs

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Overall MCQs for Reference

What is the main purpose of data exploration in data science?
a. Coding models
b. Discovering patterns
c. Deploying solutions
d. Ensuring cybersecurity
Answer: b. Discovering patternstechtarget+2
Which statistical tool is commonly used during data exploration?
a. Scatter plot
b. Hash map
c. Packet sniffer
d. Firewall
Answer: a. Scatter plotsanfoundry+1
What is the primary aim of Exploratory Data Analysis (EDA)?
a. Make data harder to interpret
b. Discover patterns and insights
c. Discard irrelevant data
d. Encode categorical variables
Answer: b. Discover patterns and insightsexamveda+2
Which activity is NOT typically part of exploratory data analysis?
a. Data cleaning
b. Model deployment
c. Visualization
d. Summarizing data
Answer: b. Model deploymentgeeksforgeeks+1
Which tool is often used for data visualization in data exploration?
a. VPN
b. Tableau
c. IDS
d. Honeypot
Answer: b. Tableausanfoundry+1
What is an outlier in a dataset?
a. The mean value
b. The median value
c. A value far from the norm
d. The minimum value
Answer: c. A value far from the normmatillion+2
Which of the following best describes unstructured data?
a. Data in spreadsheets
b. Text documents and images
c. Data in relational tables
d. Normalized data
Answer: b. Text documents and imagessanfoundry+1
What does “feature engineering” mean in data exploration?
a. Running security tests
b. Creating or modifying variables
c. Scaling servers
d. Reducing noise
Answer: b. Creating or modifying variablesgeeksforgeeks+1
Correlation matrices in EDA are used to:
a. Protect sensitive data
b. Identify relationships between variables
c. Encrypt data
d. Reduce file size
Answer: b. Identify relationships between variablesmatillion+1
Which statement about missing values is true in data exploration?
a. They must always be deleted
b. They are ignored
c. They can affect analysis results
d. They increase correlation
Answer: c. They can affect analysis resultssimplilearn+1
Identifying skewness in data helps to:
a. Drop rows
b. Choose visualization types
c. Understand data distribution
d. Remove metadata
Answer: c. Understand data distributionplacementpreparation+1
Which plot is MOST suitable for visualizing the distribution of a single variable?
a. Scatter plot
b. Bar chart
c. Histogram
d. Line chart
Answer: c. Histogramsanfoundry+1
In which step of the data science process does data exploration usually occur?
a. After deployment
b. Before data cleaning
c. Before modeling
d. After evaluation
Answer: c. Before modelingtechtarget+1
What is the use of a box plot in data exploration?
a. Show database relations
b. Visualize data distribution and outliers
c. Encrypt features
d. Test software
Answer: b. Visualize data distribution and outlierssanfoundry+1
Which method is commonly used to handle outliers in exploratory analysis?
a. Visualization
b. Data removal or transformation
c. Data collection
d. Concatenation
Answer: b. Data removal or transformationmatillion+1
Feature scaling refers to:
a. Increasing model size
b. Adjusting variable ranges
c. Adding more features
d. Changing data types
Answer: b. Adjusting variable rangesplacementpreparation+1
What is “data profiling” in the context of exploration?
a. Attacking servers
b. Examining dataset structure/summary
c. Building models
d. Logging user actions
Answer: b. Examining dataset structure/summaryqlik+1
Which of the following is NOT a key aspect of EDA?
a. Creating dashboards
b. Identifying data types
c. Validating model accuracy
d. Detecting data anomalies
Answer: c. Validating model accuracysimplilearn+1
What is the purpose of using scatter plots in EDA?
a. Model aggregation
b. Visualize relation between two variables
c. Optimize queries
d. Detect server downtime
Answer: b. Visualize relation between two variablesgeeksforgeeks+1
Which of these is essential to look for during data exploration?
a. Security flaws
b. Patterns and trends
c. Export speed
d. Package dependencies
Answer: b. Patterns and trendstechtarget+1
Data normalization helps by:
a. Keeping all data in plaintext
b. Bringing data into a comparable scale
c. Infiltrating datasets
d. Building neural networks
Answer: b. Bringing data into a comparable scalegeeksforgeeks
What technique can be used to reduce dimensionality in data exploration?
a. PCA (Principal Component Analysis)
b. Data anonymization
c. Sorting
d. Hashing
Answer: a. PCA (Principal Component Analysis)placementpreparation+1
What is meant by “data wrangling”?
a. Data visualization
b. Cleaning and transforming raw data
c. Model evaluation
d. Server monitoring
Answer: b. Cleaning and transforming raw datamatillion+1
Which library is widely used in Python for basic data visualizations?
a. NumPy
b. Pandas
c. Matplotlib
d. PyTorch
Answer: c. Matplotlibplacementpreparation+1
A high correlation between two features can indicate:
a. Server error
b. Redundancy in data
c. High accuracy
d. More diverse information
Answer: b. Redundancy in datamatillion+1
Which type of visualization is best for outlier detection?
a. Histogram
b. Bar chart
c. Box plot
d. Heatmap
Answer: c. Box plotsanfoundry+1
Data exploration is MOST effective when combined with:
a. Penetration testing
b. Visualization and summary statistics
c. Cloud migration
d. Server scripts
Answer: b. Visualization and summary statisticstechtarget+2
Anomalies detected through EDA are:
a. Indicators of trends
b. Data points that differ from the norm
c. Always errors
d. Always beneficial
Answer: b. Data points that differ from the normtechtarget+1
The first phase of data exploration should be:
a. Model selection
b. Data collection and understanding variables
c. Visualization
d. Feature scaling
Answer: b. Data collection and understanding variablesgeeksforgeeks+1
Which of the following techniques is NOT part of data exploration?
a. Statistical summary
b. Feature selection
c. Deployment
d. Outlier detection
Answer: c. Deploymentsimplilearn+1
What is the role of descriptive statistics in EDA?
a. Evaluate cyber threats
b. Summarize data features
c. Encrypt data
d. Remove unwanted files
Answer: b. Summarize data featuresmatillion+1
Which type of chart is suitable for representing categorical variables?
a. Pie chart
b. Scatter plot
c. Histogram
d. Line plot
Answer: a. Pie chartgeeksforgeeks
Heatmaps are used in data exploration to:
a. Show data transmission
b. Visualize correlations
c. Increase data security
d. Decompose features
Answer: b. Visualize correlationssanfoundry+1
The process of deriving new variables from existing data is called:
a. Data splitting
b. Feature engineering
c. Hashing
d. Indexing
Answer: b. Feature engineeringsanfoundry+1
When visualizing data, what should always be checked?
a. Encryption status
b. Axis labels and scales
c. Server uptime
d. Server memory
Answer: b. Axis labels and scalessanfoundry+1
Which Python library is best known for working with dataframes?
a. Keras
b. Pandas
c. TensorFlow
d. Flask
Answer: b. Pandasplacementpreparation+1
What is one reason data scientists perform EDA before modeling?
a. Reduce computational time
b. Check for relationships, patterns, and data quality issues
c. Increase training accuracy
d. Deploy faster
Answer: b. Check for relationships, patterns, and data quality issuessimplilearn+2
A bar chart in EDA is useful for:
a. Categorical variable visualization
b. Data encryption
c. Neural network training
d. Metadata correction
Answer: a. Categorical variable visualizationmatillion+1
Which technique helps identify missing data visually?
a. Heatmap
b. Line chart
c. Pie chart
d. Area plot
Answer: a. Heatmapsanfoundry+1
Multicollinearity is:
a. Multiple data sources
b. High correlation among independent variables
c. Data stored in multiple clouds
d. Low variance
Answer: b. High correlation among independent variablesplacementpreparation+1
Identifying duplicate values in data is necessary because:
a. They can bias analysis
b. They always increase accuracy
c. They speed up computation
d. They enable encryption
Answer: a. They can bias analysissimplilearn+2
Which is NOT a visualization technique in data exploration?
a. Box plot
b. Scatter plot
c. Histogram
d. NAT traversal
Answer: d. NAT traversalgeeksforgeeks+1
Which process comes directly after data exploration in a typical data science workflow?
a. Data collection
b. Feature scaling
c. Model building
d. Data encryption
Answer: c. Model buildingibm+2
Outliers in a dataset can indicate:
a. Errors or unusual behavior
b. Better security
c. Always model improvement
d. Nothing important
Answer: a. Errors or unusual behaviortechtarget+2
The detection of missing values can be done using:
a. Statistical summary
b. VPN logs
c. Server logs
d. MAC addresses
Answer: a. Statistical summarysimplilearn+1
Which of these is a common file format for storing structured data?
a. .exe
b. .png
c. .csv
d. .dll
Answer: c. .csvsanfoundry+1
Z-score is used for:
a. Classification
b. Outlier detection
c. Data encryption
d. Visualization
Answer: b. Outlier detectionmatillion+1
Which visualization is commonly used to compare several categorical variables?
a. Heatmap
b. Box plot
c. Pie chart
d. Scatter plot
Answer: a. Heatmapsanfoundry+1
Data aggregation in EDA helps by:
a. Summarizing datasets
b. Embedding security
c. Encrypting variables
d. Indexing servers
Answer: a. Summarizing datasetsgeeksforgeeks+1
Why should outliers be handled in EDA?
a. To increase error
b. To skew results
c. To ensure accurate analysis
d. To increase variance
Answer: c. To ensure accurate analysistechtarget+2

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