MCQs

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Overall MCQs for Reference

Which characteristic best describes high-dimensional data?
a. Few features
b. Many features
c. Sparse data only
d. Limited observations
Answer: b. Many featureswikipedia+1
What is a common challenge associated with high-dimensional data?
a. Linear scalability
b. Overfitting
c. Underfitting
d. High interpretability
Answer: b. Overfittingpolymersearch+1
Dimensionality reduction techniques are often used in high-dimensional analysis primarily to:
a. Increase data dimensions
b. Reduce computational cost
c. Add more noise
d. Ignore data sparsity
Answer: b. Reduce computational costpll.harvard+1
Which technique is NOT typically used for dimensionality reduction?
a. Principal Component Analysis
b. t-SNE
c. K-Means Clustering
d. Linear Discriminant Analysis
Answer: c. K-Means Clusteringedx+1
The “curse of dimensionality” refers to:
a. High model accuracy in large dimensions
b. Exponential increase in data volume as dimensions grow
c. Reduced need for data cleaning
d. Sampling bias
Answer: b. Exponential increase in data volume as dimensions growgeeksforgeeks+1
High-dimensional data often contains many:
a. Redundant features
b. No features
c. Missing values only
d. Small sample sizes
Answer: a. Redundant featuressciencedirect+1
Which algorithm is preferable when dealing with high-dimensional datasets?
a. Decision Trees
b. k-Nearest Neighbors
c. Naive Bayes
d. Linear Regression
Answer: c. Naive Bayessciencedirect+1
Which visualization method is most suited for high-dimensional data?
a. Line chart
b. Pie chart
c. Parallel coordinates
d. Bar chart
Answer: c. Parallel coordinatespolymersearch+1
Sparsity in high-dimensional data means:
a. Many zero or missing values
b. Dense data
c. Low feature count
d. Maximum correlation
Answer: a. Many zero or missing valuespolymersearch+1
Principal Component Analysis transforms features by:
a. Maximizing variance
b. Minimizing variance
c. Increasing correlation
d. Adding random noise
Answer: a. Maximizing variancepll.harvard+1
In high-dimensional statistics, the number of features is often:
a. Lower than observations
b. Comparable or larger than observations
c. Irrelevant
d. Always fixed
Answer: b. Comparable or larger than observationswikipedia+1
Which metric is NOT commonly used to measure similarity in high-dimensional spaces?
a. Euclidean distance
b. Cosine similarity
c. Manhattan distance
d. Frequency count
Answer: d. Frequency countsciencedirect+1
Feature selection methods in high-dimensional data analysis aim to:
a. Reduce irrelevant variables
b. Add more features
c. Ignore labels
d. Increase redundancy
Answer: a. Reduce irrelevant variablesgeeksforgeeks+1
A major risk of high-dimensional modeling is:
a. Information overload
b. Spurious correlations
c. Fast training times
d. Decreased model accuracy
Answer: b. Spurious correlationsisical+1
Which is an unsupervised technique for handling high-dimensional data?
a. Logistic regression
b. Factor analysis
c. Linear regression
d. Decision tree
Answer: b. Factor analysisedx+1
High-dimensional data analysis is closely linked to:
a. Deep learning
b. Shallow learning
c. Boolean algebra
d. Time series decomposition
Answer: a. Deep learninggeeksforgeeks+1
The “large p, small n” problem occurs when:
a. Features outnumber samples
b. Samples outnumber features
c. Model does not converge
d. Only categorical data present
Answer: a. Features outnumber sampleswikipedia+1
Which of the following is a benefit of dimensionality reduction?
a. Increased noise
b. Improved interpretability
c. Data anonymization
d. More missing values
Answer: b. Improved interpretabilitypll.harvard+1
What does t-SNE stand for?
a. Two-dimensional Sparse Embedding
b. t-Distributed Stochastic Neighbor Embedding
c. Time Series Numeric Evaluation
d. Tensor Sparse Network Encoding
Answer: b. t-Distributed Stochastic Neighbor Embeddingpll.harvard+1
Which is a type of regularization for managing high-dimensional models?
a. Lasso regression
b. K-Means clustering
c. Random forest
d. Bootstrap aggregation
Answer: a. Lasso regressionsciencedirect+1
Manifold learning is a class of:
a. Feature selection techniques
b. Dimensionality reduction techniques
c. Distance measures
d. Model ensembling techniques
Answer: b. Dimensionality reduction techniquessciencedirect+1
In Big Data, high-dimensional data enables the discovery of:
a. Hidden patterns
b. Simple averages
c. Redundant relationships
d. Outliers only
Answer: a. Hidden patternsisical+1
The risk of incidental endogeneity is highest in:
a. Low-dimensional analysis
b. High-dimensional analysis
c. Short time series
d. Small categorical datasets
Answer: b. High-dimensional analysisisical+1
In genomics, high-dimensional data analysis often deals with:
a. Few genes
b. Thousands of genes/features
c. Only categorical data
d. Small decision trees
Answer: b. Thousands of genes/featurespolymersearch+1
Principal axes in PCA are:
a. Directions of minimum variance
b. Orthogonal directions of maximum variance
c. Non-linear axes
d. Arbitrary chosen directions
Answer: b. Orthogonal directions of maximum varianceedx+1
High-dimensional analysis in finance helps in:
a. Risk modeling
b. Invoice management
c. Logistics tracking
d. Solely visualization
Answer: a. Risk modelingpolymersearch+1
What is the main cause of noise accumulation in high-dimensional data?
a. Low feature count
b. Large sample size
c. Many features
d. Slow data input
Answer: c. Many featuresisical+1
Which of the following is NOT a drawback of high-dimensional data?
a. Computational complexity
b. High risk of overfitting
c. Easy visualization
d. Spurious results
Answer: c. Easy visualizationpolymersearch+1
Factor analysis is mainly used for:
a. Classification tasks
b. Reducing many variables into a few factors
c. Increasing model complexity
d. Data encryption
Answer: b. Reducing many variables into a few factorsedx+1
Multiple dimensional scaling (MDS) is used for:
a. Clustering
b. Visualization of high-dimensional data
c. Feature selection
d. Outlier detection
Answer: b. Visualization of high-dimensional datapll.harvard+1
In image processing, high-dimensional data analysis is necessary because images have:
a. Many pixels/features
b. Low contrast
c. Few colors
d. No noise
Answer: a. Many pixels/featuressciencedirect+1
Which statistical property often complicates analysis in high dimensions?
a. Independence
b. Multicollinearity
c. Homoscedasticity
d. Normality
Answer: b. Multicollinearitywikipedia+1
Overfitting in high-dimensional data can lead to:
a. Model generalization
b. Poor predictions on unseen data
c. Increased test accuracy
d. Faster computations
Answer: b. Poor predictions on unseen dataisical+1
In supervised learning with high-dimensional data, model selection must consider:
a. Irrelevant features
b. Overfitting risk
c. Hyperparameter tuning
d. All of the above
Answer: d. All of the abovegeeksforgeeks+1
Clustering high-dimensional data is often more challenging due to:
a. Loss of cluster structure
b. Easy visualization
c. High density of data
d. Fewer categories
Answer: a. Loss of cluster structuregeeksforgeeks+1
Which is a limitation of using Euclidean distance in very high dimensions?
a. Best similarity measure
b. Distance becomes less meaningful
c. Reduces computation time
d. Handles sparse data well
Answer: b. Distance becomes less meaningfulpll.harvard+1
Regularization in high-dimensional modeling helps to:
a. Penalize model complexity
b. Ignore minority classes
c. Increase sample size
d. Reduce training time
Answer: a. Penalize model complexitygeeksforgeeks+1
Singular Value Decomposition (SVD) is primarily used for:
a. Matrix factorization
b. Data encryption
c. Filtering categorical labels
d. Improving class balance
Answer: a. Matrix factorizationedx+1
Feature extraction differs from feature selection by:
a. Creating new features from existing ones
b. Picking features without modification
c. Ignoring all features
d. Randomly adding features
Answer: a. Creating new features from existing onessciencedirect+1
The “elbow method” helps choose:
a. Optimal number of clusters or components
b. Noise level
c. Dimensionality reduction technique
d. Normalization strategy
Answer: a. Optimal number of clusters or componentspll.harvard+1
In high-dimensional text mining, the data is often represented as:
a. Word vectors
b. Simple strings
c. Numbers only
d. Binary codes only
Answer: a. Word vectorspolymersearch+1
“Big Data” and high-dimensionality share which feature?
a. Many variables and large sample size
b. Only categorical variables
c. Few observations
d. Only structured data
Answer: a. Many variables and large sample sizeisical+1
Kernel methods are widely used for:
a. Non-linear dimensionality reduction
b. Data sorting
c. Feature deletion
d. Basic visualization
Answer: a. Non-linear dimensionality reductionsciencedirect+1
High-dimensionality can lead to which type of correlation?
a. Spurious correlation
b. Strong correlation
c. No correlation
d. Deterministic correlation
Answer: a. Spurious correlationwikipedia+1
What is an example of a scalable framework for high-dimensional data analysis?
a. Hadoop
b. Excel
c. MySQL
d. Access
Answer: a. Hadoopgtu-mcq+1
In high-dimensional analysis, noise filtering is crucial because:
a. Reduces irrelevant information
b. Ignores all outliers
c. Adds more features
d. Speeds up computation only
Answer: a. Reduces irrelevant informationisical+1
Which deep learning architecture is highly suited for high-dimensional data?
a. Convolutional Neural Networks
b. Simple perceptrons
c. Linear regression models
d. Decision lists
Answer: a. Convolutional Neural Networksgeeksforgeeks+1
Data sparsity in high-dimensional datasets most commonly affects:
a. Model robustness
b. Visualization
c. Overfitting risk
d. All of the above
Answer: d. All of the abovepolymersearch+1
Large-scale high-dimensional datasets may necessitate which storage solutions?
a. Distributed databases
b. Single CSV file
c. Flat text
d. Relational spreadsheets
Answer: a. Distributed databasesgtu-mcq+1
The future of high-dimensional data analysis will likely involve:
a. Advanced AI integration
b. Ignoring Big Data
c. Manual processing
d. Shallow models
Answer: a. Advanced AI integrationgeeksforgeeks+1

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