WorksheetsUnderstanding PCA Concepts
Total questions: 10
Worksheet time: 5mins
The PCA in data analysis stand for
Principal Component Algorithm
Primary Component Analysis
Principal Coordinate Analysis
Principal Component Analysis
The primary purpose of PCA is
To visualize data in three dimensions.
The primary purpose of PCA is to reduce the dimensionality of data.
To eliminate outliers from the dataset.
To increase the dimensionality of data.
How does PCA reduce the dimensionality of data?
PCA reduces dimensionality by projecting data onto principal components that capture the most variance.
PCA clusters data points into distinct groups without reducing dimensions.
PCA reduces dimensionality by removing all features equally.
PCA increases dimensionality by adding new features.
The eigenvalue in the context of PCA
Eigenvalues represent the number of principal components.
Eigenvalues indicate the direction of the data points.
Eigenvalues are the coefficients of the original variables.
Eigenvalues in PCA indicate the variance explained by each principal component.
The eigenvectors in PCA
Eigenvectors indicate the directions of maximum variance in PCA.
Eigenvectors represent the data points in PCA.
Eigenvectors determine the number of principal components in PCA.
Eigenvectors are used to calculate the mean of the data.
Image Compression in Computer Vision
You have 1024-pixel grayscale images stored as feature vectors. An AI engineer applies PCA and keeps only 50 principal components. The main advantage is
Faster model training with reduced storage requirements
Increase in image sharpness
Elimination of all noise from images
Conversion of images to binary format
IoT Sensor Data in Manufacturing
A factory collects 200 sensor readings every second. PCA is applied before anomaly detection. The primary benefit is
Reduce redundant data and focus on major variance patterns
Increase the number of sensors virtually
Eliminate all faulty sensor readings
Make the data binary for faster processing
Marketing Analytics
A retail company has 60 customer metrics (age, spend patterns, preferences). After PCA, only 3 components are used for visualization. The purpose is
To identify and visualize patterns in a lower-dimensional space
To remove all irrelevant customers
To increase data dimensionality for better clustering
To guarantee higher customer retention
Biomedical Signal Processing
EEG recordings have thousands of time-series points per patient. PCA is applied before feeding data to a neural network. What’s the main advantage?
Minimize irrelevant noise and reduce computational cost
Guarantee 100% disease detection accuracy
Convert EEG signals into images
Increase the amplitude of EEG signals
Cybersecurity Network Traffic Analysis
Network logs have hundreds of features per connection. PCA is used before training an intrusion detection system. Why?
Reduce dimensionality to speed up model training and avoid overfitting
Encrypt the network traffic automatically
Ensure all connections are safe
Increase the variance artificially
