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Unit 5: Dimensionality Reduction and Neural Networks - Practice Quiz

INT234 — Predictive Analytics 50 Questions
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1 What is the primary objective of dimensionality reduction in predictive analytics?

A. To reduce the number of input variables while retaining essential information
B. To create artificial data points for training
C. To increase the number of features to capture more data
D. To increase the computational complexity of the model

2 Which phenomenon refers to the problem where the amount of data needed to generalize accurately grows exponentially with the dimensionality?

A. The Vanishing Gradient
B. The Overfitting Paradox
C. The Curse of Dimensionality
D. The Law of Large Numbers

3 Principal Component Analysis (PCA) is best described as which type of machine learning technique?

A. Semisupervised Learning
B. Reinforcement Learning
C. Supervised Learning
D. Unsupervised Learning

4 In PCA, what do the Principal Components represent?

A. The error terms of a regression model
B. The original features sorted by importance
C. The cluster centroids of the data
D. New orthogonal variables that maximize variance

5 Which mathematical concept is central to calculating Principal Components in PCA?

A. Derivatives and Integrals
B. Eigenvalues and Eigenvectors
C. Sine and Cosine functions
D. Fourier Transforms

6 Why is feature scaling (standardization) important before applying PCA?

A. To increase the number of dimensions
B. Because PCA is sensitive to the scale of the variables
C. Because PCA requires categorical data
D. To convert negative numbers to positive

7 What is the relationship between the first principal component and the second principal component?

A. They are identical
B. The second is the inverse of the first
C. They are parallel to each other
D. They are orthogonal (uncorrelated) to each other

8 Which plot is commonly used to determine the optimal number of principal components to retain?

A. Box plot
B. Histogram
C. Scatter plot
D. Scree plot

9 What is the main difference between Feature Selection and Feature Extraction?

A. They are exactly the same
B. Feature selection selects a subset of original variables; Feature extraction creates new variables
C. Feature selection creates new variables; Feature extraction selects a subset
D. Feature selection is for images; Feature extraction is for text

10 If a dataset has 50 variables and you apply PCA to select the top 5 components, what happens to the dimensionality?

A. It increases to 55
B. It becomes 0
C. It remains 50
D. It reduces to 5

11 What is a Feedforward Neural Network?

A. A network where information moves in only one direction, forward, from the input nodes
B. A network that only processes images
C. A network that does not use weights
D. A network where output is fed back into the input

12 What is the fundamental building block of a neural network?

A. Pixel
B. Neuron (Perceptron)
C. Decision Tree
D. Eigenvector

13 In a neural network, what is the role of the 'weight'?

A. To store the input data
B. To determine the number of layers
C. To calculate the accuracy
D. To represent the strength or importance of a connection between neurons

14 What is the purpose of an activation function in a neural network?

A. To initialize the weights to zero
B. To introduce non-linearity into the network
C. To reduce the size of the dataset
D. To sort the input data

15 Which of the following is a commonly used activation function that outputs values between 0 and 1?

A. ReLU (Rectified Linear Unit)
B. Linear
C. Sigmoid
D. Tanh

16 What is a Multi-layer Perceptron (MLP)?

A. A network that only has an input and an output layer
B. A type of PCA
C. A perceptron with no weights
D. A neural network with at least one hidden layer between input and output

17 What limitation of the single-layer perceptron did the MLP solve?

A. It could not handle numbers
B. It was too slow
C. It could not solve the XOR problem (non-linearly separable data)
D. It required too much memory

18 What algorithm is commonly used to train Multi-layer Perceptrons?

A. Principal Component Analysis
B. K-Means Clustering
C. Apriori Algorithm
D. Backpropagation

19 In the context of Backpropagation, what is the role of the 'Loss Function'?

A. To measure the difference between the predicted output and the actual target
B. To increase the speed of the network
C. To visualize the neural network
D. To randomly assign weights

20 What does 'Gradient Descent' do in a neural network?

A. It converts images to text
B. It iteratively adjusts weights to minimize the loss function
C. It removes outliers from data
D. It increases the number of neurons

21 What is an 'Epoch' in neural network training?

A. The number of hidden layers
B. The time it takes to code the model
C. The initial learning rate
D. One forward pass and one backward pass of all the training examples

22 Which activation function is defined as f(x) = max(0, x)?

A. Sigmoid
B. ReLU (Rectified Linear Unit)
C. Softmax
D. Tanh

23 What is the 'Bias' term in a neuron equation?

A. The error rate of the model
B. The number of inputs
C. A constant added to the product of inputs and weights to shift the activation function
D. A prejudice in the data

24 Convolutional Neural Networks (CNNs) are primarily used for which type of data?

A. Text sentiment analysis
B. Time-series financial data
C. Tabular sales data
D. Image and video data

25 What is the core operation in a CNN that allows it to detect features like edges?

A. Recursion
B. Multiplication
C. Flattening
D. Convolution

26 In a CNN, what is a 'Kernel' or 'Filter'?

A. A virus protection software
B. A small matrix of weights that slides over the input
C. The output layer of the network
D. The loss function

27 What is the purpose of 'Pooling' layers (e.g., Max Pooling) in a CNN?

A. To inverse the colors
B. To add color to the image
C. To increase the dimensions of the image
D. To reduce the spatial dimensions and computation while retaining important features

28 What does 'Stride' refer to in the context of CNNs?

A. The size of the pooling window
B. The learning rate
C. The step size the filter moves across the input image
D. The number of filters used

29 What is 'Padding' in a CNN?

A. Adding pixels (usually zeros) around the border of the input image
B. Coloring the image black and white
C. Compressing the image file
D. Removing pixels from the image

30 Before passing the output of convolutional layers to a fully connected dense layer, what operation must be performed?

A. Expanding
B. Inverting
C. Rotating
D. Flattening

31 Recurrent Neural Networks (RNNs) are best suited for what type of data?

A. Independent tabular records
B. Sequential data (e.g., time series, text)
C. Static images
D. Unstructured noise

32 What distinguishes an RNN from a standard Feedforward Neural Network?

A. RNNs only work on GPUs
B. RNNs cannot have hidden layers
C. RNNs have a feedback loop allowing information to persist
D. RNNs have no activation functions

33 In an RNN, what is the 'Hidden State'?

A. The final output prediction
B. A layer that is never trained
C. Data that is deleted after processing
D. The memory of the network capturing information about previous steps

34 What is the 'Vanishing Gradient Problem' commonly faced by standard RNNs?

A. Gradients become too large and explode
B. The loss function becomes zero immediately
C. Gradients become extremely small, preventing weights from updating effectively in earlier layers
D. The network runs out of memory

35 Which architecture was designed specifically to solve the Vanishing Gradient problem in RNNs?

A. PCA (Principal Component Analysis)
B. Perceptron
C. LSTM (Long Short-Term Memory)
D. CNN (Convolutional Neural Network)

36 What is 'Dropout' in the context of Neural Networks?

A. Removing bad data from the input
B. A regularization technique that randomly ignores neurons during training to prevent overfitting
C. Stopping the training early
D. A type of activation function

37 In a multi-class classification problem (e.g., classifying an image as a Cat, Dog, or Bird), which activation function is used in the output layer?

A. ReLU
B. Softmax
C. Sigmoid
D. Linear

38 Which of the following is a hyperparameter in a neural network?

A. Output predictions
B. Learning Rate
C. Biases
D. Weights

39 What does a Learning Rate of 0.001 imply?

A. The weights are updated by a small step size in the direction of the gradient
B. The model has 1000 layers
C. The accuracy will be 0.1%
D. The model will learn very fast

40 In the context of PCA, if the first two principal components explain 95% of the variance, what can be concluded?

A. You need at least 10 components
B. The other components contain 95% of the information
C. The data can be effectively reduced to 2 dimensions with minimal information loss
D. The data is useless

41 Which concept allows a CNN to recognize an object in an image regardless of where it is located in the image?

A. Overfitting
B. Translation Invariance
C. Linearity
D. Vanishing Gradient

42 What is the 'Gate' mechanism in LSTM used for?

A. To speed up the GPU
B. To connect to the internet
C. To calculate the convolution
D. To control what information is added to or removed from the cell state

43 Which of the following is an example of a Many-to-One RNN architecture?

A. Image Captioning (Image to Text)
B. Video Frame Prediction
C. Sentiment Analysis (Text to Sentiment Score)
D. Machine Translation (Sequence to Sequence)

44 In a fully connected (dense) layer, how are neurons connected?

A. Each neuron is connected to every neuron in the previous layer
B. Neurons are not connected
C. Only to the corresponding neuron in the previous layer
D. Randomly connected

45 Why is Non-Linearity important in dimensionality reduction (e.g., t-SNE vs PCA)?

A. It is not important
B. PCA captures linear relationships; non-linear methods capture complex manifolds
C. PCA cannot handle numbers
D. Non-linear methods are always faster

46 What is the primary disadvantage of using a Deep Neural Network compared to a Decision Tree?

A. Cannot handle large data
B. Simplicity
C. Lower accuracy
D. Lack of interpretability (Black Box nature)

47 In PCA, the Covariance Matrix is usually:

A. Symmetric
B. Undefined
C. Asymmetric
D. Diagonal with zeros

48 What is 'Weight Initialization'?

A. Calculating the final weights
B. Setting initial values for weights (often small random numbers) before training
C. Setting all weights to 0
D. Setting all weights to 1

49 Which of the following best describes 'Overfitting' in neural networks?

A. The model has too few parameters
B. The model performs poorly on training data and test data
C. The model performs well on test data but poorly on training data
D. The model performs well on training data but poorly on unseen test data

50 The Universal Approximation Theorem states that:

A. A neural network can never reach 100% accuracy
B. CNNs are better than RNNs
C. A feedforward network with a single hidden layer can approximate any continuous function
D. PCA is universal
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