data/Neural-Networks.json

{
  "title": "Neural Networks Mastery: 100 MCQs",
  "description": "A comprehensive set of 100 multiple-choice questions designed to test and deepen your understanding of Neural Networks for classification tasks, covering fundamentals, architectures, activation functions, optimization, regularization, and practical scenarios.",
  "questions": [
    {
      "id": 1,
      "questionText": "What is the primary goal of a neural network for classification?",
      "options": [
        "Predict continuous values",
        "Reduce dimensionality of data",
        "Classify input data into predefined categories",
        "Cluster data points"
      ],
      "correctAnswerIndex": 2,
      "explanation": "For classification tasks, neural networks aim to predict discrete class labels for input data."
    },
    {
      "id": 2,
      "questionText": "What is an 'epoch' in neural network training?",
      "options": [
        "A single pass through the entire training dataset",
        "A type of activation function",
        "Number of hidden layers",
        "Number of neurons in a layer"
      ],
      "correctAnswerIndex": 0,
      "explanation": "An epoch is one complete pass through the training dataset during training."
    },
    {
      "id": 3,
      "questionText": "Which activation function is commonly used in hidden layers of neural networks?",
      "options": [
        "ReLU",
        "Softmax",
        "Sigmoid",
        "Linear"
      ],
      "correctAnswerIndex": 0,
      "explanation": "ReLU (Rectified Linear Unit) is commonly used in hidden layers due to its efficiency and ability to reduce vanishing gradient problems."
    },
    {
      "id": 4,
      "questionText": "Which activation function is typically used in the output layer for multi-class classification?",
      "options": [
        "ReLU",
        "Tanh",
        "Softmax",
        "Sigmoid"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Softmax outputs probabilities for each class and is used in multi-class classification."
    },
    {
      "id": 5,
      "questionText": "Scenario: A neural network predicts probabilities 0.7, 0.2, 0.1 for three classes. Which class is predicted?",
      "options": [
        "Class 2",
        "Class 3",
        "Class 1",
        "Cannot predict"
      ],
      "correctAnswerIndex": 2,
      "explanation": "The class with the highest probability (0.7) is chosen as the prediction."
    },
    {
      "id": 6,
      "questionText": "What is the role of weights in a neural network?",
      "options": [
        "Determine the strength of connections between neurons",
        "Provide output predictions",
        "Store input data",
        "Define the number of layers"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Weights determine how strongly a neuron's input influences its output."
    },
    {
      "id": 7,
      "questionText": "What is 'bias' in a neural network neuron?",
      "options": [
        "A learning rate parameter",
        "The output of a neuron",
        "Number of neurons in a layer",
        "A constant added to the weighted sum of inputs"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Bias allows the activation function to shift and helps the model fit data better."
    },
    {
      "id": 8,
      "questionText": "Scenario: A network overfits training data. What is a suitable remedy?",
      "options": [
        "Add dropout or regularization",
        "Reduce batch size",
        "Increase learning rate",
        "Use fewer neurons"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Dropout or regularization helps prevent overfitting by reducing reliance on specific neurons or large weights."
    },
    {
      "id": 9,
      "questionText": "What is 'forward propagation'?",
      "options": [
        "Computing output by passing inputs through the network layers",
        "Updating weights via backpropagation",
        "Shuffling the dataset",
        "Normalizing inputs"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Forward propagation computes the output by applying weights, biases, and activation functions through the network."
    },
    {
      "id": 10,
      "questionText": "What is 'backpropagation'?",
      "options": [
        "Activation function selection",
        "Forward pass of inputs",
        "Algorithm for updating weights using gradient descent",
        "Data preprocessing step"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Backpropagation computes gradients of the loss function with respect to weights to update them and minimize error."
    },
    {
      "id": 11,
      "questionText": "Scenario: Training loss decreases but validation loss increases. What is happening?",
      "options": [
        "Good fit",
        "Underfitting",
        "Overfitting",
        "Gradient vanishing"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Overfitting occurs when the model fits training data well but generalizes poorly to unseen data."
    },
    {
      "id": 12,
      "questionText": "Which optimizer adapts learning rates per parameter?",
      "options": [
        "Gradient Descent",
        "RMSProp",
        "Adam",
        "SGD"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Adam optimizer adapts learning rates for each parameter and combines benefits of RMSProp and momentum."
    },
    {
      "id": 13,
      "questionText": "Scenario: Neural network training is very slow. Which is a common solution?",
      "options": [
        "Use mini-batch gradient descent",
        "Remove activation functions",
        "Increase number of layers",
        "Increase epochs drastically"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Mini-batch gradient descent speeds up training by updating weights on small batches rather than the entire dataset."
    },
    {
      "id": 14,
      "questionText": "What is the vanishing gradient problem?",
      "options": [
        "Activation function outputs zero always",
        "Loss increases during training",
        "Weights explode",
        "Gradients become too small to update weights effectively in deep networks"
      ],
      "correctAnswerIndex": 3,
      "explanation": "In deep networks with sigmoid or tanh, gradients can shrink, slowing or stopping learning."
    },
    {
      "id": 15,
      "questionText": "Scenario: A neuron uses sigmoid activation. Output is near 0. What can happen to gradient?",
      "options": [
        "Gradient is maximum",
        "Gradient is negative always",
        "Gradient becomes very small (vanishing gradient)",
        "Gradient does not change"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Sigmoid outputs near 0 or 1 lead to small gradients, slowing learning."
    },
    {
      "id": 16,
      "questionText": "What is the purpose of softmax in classification?",
      "options": [
        "Convert logits into probability distribution over classes",
        "Compute loss function",
        "Reduce overfitting",
        "Normalize input features"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Softmax converts raw output scores into probabilities summing to 1."
    },
    {
      "id": 17,
      "questionText": "Scenario: You have a 3-class classification problem. Which loss function is appropriate?",
      "options": [
        "Hinge loss",
        "Binary cross-entropy",
        "Mean squared error",
        "Categorical cross-entropy"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Categorical cross-entropy is suitable for multi-class classification."
    },
    {
      "id": 18,
      "questionText": "Scenario: Some features have different ranges. What should you do?",
      "options": [
        "Leave as is",
        "Normalize or standardize inputs",
        "Add dropout",
        "Change activation function"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Normalization/standardization helps the network train faster and converge better."
    },
    {
      "id": 19,
      "questionText": "Scenario: Too large learning rate causes:",
      "options": [
        "Exact solution",
        "No effect",
        "Divergence of loss",
        "Slow convergence"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Large learning rates can overshoot minima, causing loss to diverge."
    },
    {
      "id": 20,
      "questionText": "Scenario: Too small learning rate causes:",
      "options": [
        "Overfitting automatically",
        "Gradient explosion",
        "Slow convergence",
        "Divergence of loss"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Small learning rates lead to very slow weight updates and training."
    },
    {
      "id": 21,
      "questionText": "Scenario: You add more hidden layers but performance worsens. Likely reason?",
      "options": [
        "Loss function not needed",
        "Optimizer issue",
        "Overfitting or vanishing gradient",
        "Better learning"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Deep networks may overfit or suffer vanishing gradients if not designed properly."
    },
    {
      "id": 22,
      "questionText": "What is dropout?",
      "options": [
        "Feature scaling",
        "Randomly deactivating neurons during training to prevent overfitting",
        "Increasing neurons",
        "Reducing learning rate"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Dropout prevents co-adaptation of neurons and reduces overfitting."
    },
    {
      "id": 23,
      "questionText": "Scenario: Output layer has one neuron with sigmoid activation. Task?",
      "options": [
        "Binary classification",
        "Clustering",
        "Regression",
        "Multi-class classification"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Sigmoid outputs a probability between 0 and 1, suitable for binary classification."
    },
    {
      "id": 24,
      "questionText": "Scenario: You have imbalanced classes. How to adjust training?",
      "options": [
        "Reduce batch size",
        "Change activation to ReLU",
        "Use class weights or oversample minority class",
        "Ignore imbalance"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Class weights or oversampling helps prevent bias toward majority class."
    },
    {
      "id": 25,
      "questionText": "Scenario: Confusion matrix shows high false positives. What can you adjust?",
      "options": [
        "Number of epochs",
        "Dropout rate",
        "Learning rate",
        "Decision threshold"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Adjusting threshold balances sensitivity and specificity."
    },
    {
      "id": 26,
      "questionText": "What is the effect of batch normalization?",
      "options": [
        "Stabilizes learning by normalizing activations",
        "Reduces learning rate",
        "Increases overfitting",
        "Removes activation functions"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Batch normalization reduces internal covariate shift, speeding up training and improving performance."
    },
    {
      "id": 27,
      "questionText": "Scenario: Input features are categorical. How to use in neural network?",
      "options": [
        "Convert to embeddings or one-hot encoding",
        "Use raw categories directly",
        "Ignore categorical features",
        "Convert to random numbers"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Neural networks require numeric input; categorical data must be encoded."
    },
    {
      "id": 28,
      "questionText": "Scenario: Network predictions are confident but wrong. Likely cause?",
      "options": [
        "Overfitting or biased data",
        "Gradient vanishing",
        "Dropout too high",
        "Learning rate too small"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Overfitting or data bias can lead to confident wrong predictions."
    },
    {
      "id": 29,
      "questionText": "Scenario: Adding more neurons improves training but not validation. Reason?",
      "options": [
        "Overfitting",
        "Underfitting",
        "Vanishing gradient",
        "Poor initialization"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Increased model capacity fits training data but harms generalization."
    },
    {
      "id": 30,
      "questionText": "Scenario: Outputs are probabilities. How to compute loss for classification?",
      "options": [
        "Use cross-entropy loss",
        "Mean squared error",
        "Hinge loss",
        "Absolute error"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Cross-entropy loss is standard for probability-based classification outputs."
    },
    {
      "id": 31,
      "questionText": "Scenario: You notice your model is underfitting. Which is a possible solution?",
      "options": [
        "Apply more dropout",
        "Increase network capacity (more layers/neurons)",
        "Reduce training data",
        "Decrease learning rate"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Increasing network capacity allows the model to learn more complex patterns and reduce underfitting."
    },
    {
      "id": 32,
      "questionText": "Scenario: Your network is overfitting. Which regularization technique helps?",
      "options": [
        "Increasing learning rate",
        "L1 or L2 regularization",
        "Adding more layers",
        "Removing batch normalization"
      ],
      "correctAnswerIndex": 1,
      "explanation": "L1 or L2 regularization penalizes large weights, reducing overfitting."
    },
    {
      "id": 33,
      "questionText": "Scenario: You apply dropout during training. What is its effect during inference?",
      "options": [
        "Dropout continues randomly",
        "No dropout is applied; weights are scaled",
        "Network outputs zeros",
        "Learning rate changes automatically"
      ],
      "correctAnswerIndex": 1,
      "explanation": "During inference, dropout is disabled and weights are scaled to maintain output expectations."
    },
    {
      "id": 34,
      "questionText": "Scenario: Your network’s loss oscillates during training. What can help?",
      "options": [
        "Increase hidden layers",
        "Add more neurons",
        "Reduce learning rate or use optimizer with momentum",
        "Use ReLU instead of sigmoid"
      ],
      "correctAnswerIndex": 2,
      "explanation": "A high learning rate can cause oscillation. Reducing it or using momentum stabilizes updates."
    },
    {
      "id": 35,
      "questionText": "Scenario: Gradients are exploding in deep network. What is a solution?",
      "options": [
        "Gradient clipping",
        "Increase learning rate",
        "Reduce batch size",
        "Remove activation functions"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Gradient clipping limits gradient values to prevent large updates."
    },
    {
      "id": 36,
      "questionText": "Scenario: Training is slow and unstable. Which technique stabilizes and accelerates training?",
      "options": [
        "Reduce neurons",
        "Batch normalization",
        "L1 regularization",
        "Dropout"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Batch normalization normalizes layer inputs, stabilizing gradients and speeding up training."
    },
    {
      "id": 37,
      "questionText": "Scenario: Validation accuracy plateaus. Which learning rate strategy can help?",
      "options": [
        "Increase dropout",
        "Learning rate decay or scheduler",
        "Add more hidden layers",
        "Use sigmoid instead of ReLU"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Gradually decreasing learning rate can help the network converge to a better minimum."
    },
    {
      "id": 38,
      "questionText": "Scenario: You have imbalanced classes. Which approach helps classification?",
      "options": [
        "Use class weights or resampling",
        "Normalize features",
        "Increase hidden layers",
        "Use only majority class"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Class weights or resampling ensures minority classes are properly learned."
    },
    {
      "id": 39,
      "questionText": "Scenario: Input features have different scales. Which problem occurs if not normalized?",
      "options": [
        "Overfitting",
        "Output becomes zero",
        "Slower convergence or unstable training",
        "Activation function fails"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Feature scaling ensures weights update appropriately, avoiding slow or unstable convergence."
    },
    {
      "id": 40,
      "questionText": "Scenario: Using sigmoid activation in hidden layers of a deep network. Possible issue?",
      "options": [
        "Exploding gradients",
        "Underfitting",
        "Vanishing gradients",
        "Overfitting"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Sigmoid outputs can cause very small gradients in deep networks, slowing learning."
    },
    {
      "id": 41,
      "questionText": "Scenario: Softmax output probabilities are all similar. What does this indicate?",
      "options": [
        "Perfect predictions",
        "Network is uncertain or not trained well",
        "Network output is binary",
        "Overfitting"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Similar probabilities indicate low confidence and that the network may require more training or features."
    },
    {
      "id": 42,
      "questionText": "Scenario: You want the network to ignore some neurons during training randomly. Technique?",
      "options": [
        "L2 regularization",
        "Dropout",
        "Gradient clipping",
        "Batch normalization"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Dropout randomly disables neurons to reduce co-adaptation and prevent overfitting."
    },
    {
      "id": 43,
      "questionText": "Scenario: Learning rate is too high and loss diverges. Immediate solution?",
      "options": [
        "Reduce learning rate",
        "Use sigmoid activation",
        "Increase neurons",
        "Add more layers"
      ],
      "correctAnswerIndex": 0,
      "explanation": "High learning rates cause overshooting; lowering it stabilizes training."
    },
    {
      "id": 44,
      "questionText": "Scenario: You want to regularize large weights specifically. Technique?",
      "options": [
        "Gradient clipping",
        "Dropout",
        "L2 regularization",
        "Batch normalization"
      ],
      "correctAnswerIndex": 2,
      "explanation": "L2 penalizes large weights directly, helping prevent overfitting."
    },
    {
      "id": 45,
      "questionText": "Scenario: You want to create sparsity in connections (many weights zero). Technique?",
      "options": [
        "Dropout",
        "L2 regularization",
        "L1 regularization",
        "Batch normalization"
      ],
      "correctAnswerIndex": 2,
      "explanation": "L1 regularization encourages weights to become zero, creating sparsity."
    },
    {
      "id": 46,
      "questionText": "Scenario: Using ReLU activation, some neurons never activate. Problem name?",
      "options": [
        "Exploding gradient",
        "Vanishing gradient",
        "Overfitting",
        "Dead neurons"
      ],
      "correctAnswerIndex": 3,
      "explanation": "ReLU outputs zero for negative inputs; some neurons may stop activating permanently if gradients vanish."
    },
    {
      "id": 47,
      "questionText": "Scenario: You add batch normalization before activation. Effect?",
      "options": [
        "Removes gradient vanishing",
        "Reduces overfitting automatically",
        "Increases neurons",
        "Stabilizes inputs to activation function, improving training"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Batch normalization reduces internal covariate shift, helping gradients propagate effectively."
    },
    {
      "id": 48,
      "questionText": "Scenario: Network trained with mini-batches. What is benefit?",
      "options": [
        "Efficient computation and smoother gradient estimates",
        "No effect on convergence",
        "Exact gradient every step",
        "Removes overfitting"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Mini-batches balance efficiency and gradient stability."
    },
    {
      "id": 49,
      "questionText": "Scenario: Using Adam optimizer. Advantage over standard SGD?",
      "options": [
        "Requires less data",
        "Slower convergence",
        "Adaptive learning rates per parameter and momentum",
        "Removes activation function"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Adam combines momentum and adaptive learning rates for faster and more reliable convergence."
    },
    {
      "id": 50,
      "questionText": "Scenario: Network predictions are biased toward one class. Likely cause?",
      "options": [
        "Dead neurons",
        "Vanishing gradient",
        "Exploding gradient",
        "Class imbalance or inappropriate loss weighting"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Bias often occurs when some classes dominate training, requiring class weights or resampling."
    },
    {
      "id": 51,
      "questionText": "Scenario: High training accuracy, low validation accuracy. What does it indicate?",
      "options": [
        "Underfitting",
        "Overfitting",
        "Good generalization",
        "Vanishing gradient"
      ],
      "correctAnswerIndex": 1,
      "explanation": "The model fits training data well but fails to generalize to new data."
    },
    {
      "id": 52,
      "questionText": "Scenario: Network training is slow. You want faster convergence. Technique?",
      "options": [
        "Add more layers",
        "Reduce data",
        "Increase dropout",
        "Use momentum or adaptive optimizers"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Momentum and adaptive optimizers accelerate convergence by smoothing gradients."
    },
    {
      "id": 53,
      "questionText": "Scenario: Using softmax for 5-class classification. What constraint must output satisfy?",
      "options": [
        "All probabilities sum to 1",
        "All outputs zero or one",
        "Sum of squared outputs = 1",
        "All outputs positive integers"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Softmax converts logits to a probability distribution summing to 1."
    },
    {
      "id": 54,
      "questionText": "Scenario: Neural network with multiple hidden layers has slow learning. Likely cause?",
      "options": [
        "Vanishing gradients due to deep sigmoid/tanh activations",
        "Data imbalance",
        "Overfitting",
        "Softmax activation"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Deep sigmoid or tanh layers can shrink gradients, slowing learning."
    },
    {
      "id": 55,
      "questionText": "Scenario: You want output probabilities to reflect confidence. Which activation and loss?",
      "options": [
        "Sigmoid with MSE",
        "Softmax activation with cross-entropy loss",
        "Linear with MAE",
        "ReLU with hinge loss"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Softmax with cross-entropy outputs calibrated probabilities for multi-class classification."
    },
    {
      "id": 56,
      "questionText": "Scenario: Adding more neurons improved training but increased validation loss. Cause?",
      "options": [
        "Underfitting",
        "Learning rate too small",
        "Gradient vanishing",
        "Overfitting"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Increased model capacity fits training data but harms generalization."
    },
    {
      "id": 57,
      "questionText": "Scenario: Using ReLU activation, learning rate too high. Effect?",
      "options": [
        "Loss always decreases",
        "Some neurons may die permanently (dead neurons)",
        "Gradient vanishing occurs",
        "Training speeds up without issue"
      ],
      "correctAnswerIndex": 1,
      "explanation": "High learning rates with ReLU can cause weights to push outputs negative permanently, killing neurons."
    },
    {
      "id": 58,
      "questionText": "Scenario: Batch normalization applied. Effect on learning rate?",
      "options": [
        "Requires lower learning rate",
        "Allows higher learning rates safely",
        "No effect",
        "Reduces learning rate automatically"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Normalization stabilizes training, allowing higher learning rates."
    },
    {
      "id": 59,
      "questionText": "Scenario: Neural network outputs are confident but incorrect. What to analyze?",
      "options": [
        "Learning rate only",
        "Activation function only",
        "Batch size only",
        "Data quality, feature engineering, and possible bias"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Errors often arise from biased data, missing features, or mislabeled samples."
    },
    {
      "id": 60,
      "questionText": "Scenario: Multi-class classification with one-hot labels. Loss function?",
      "options": [
        "Binary cross-entropy",
        "Hinge loss",
        "MSE",
        "Categorical cross-entropy"
      ],
      "correctAnswerIndex": 3,
      "explanation": "One-hot labels require categorical cross-entropy to measure prediction errors."
    },
    {
      "id": 61,
      "questionText": "Scenario: Training loss decreases slowly despite sufficient epochs. Possible cause?",
      "options": [
        "Batch size too large",
        "Dead neurons",
        "Overfitting",
        "Learning rate too small"
      ],
      "correctAnswerIndex": 3,
      "explanation": "A small learning rate results in slow convergence."
    },
    {
      "id": 62,
      "questionText": "Scenario: You want faster training on large datasets. Technique?",
      "options": [
        "Reduce layers",
        "Increase dropout",
        "Use mini-batches or GPUs",
        "Reduce neurons"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Mini-batches and hardware acceleration improve training speed."
    },
    {
      "id": 63,
      "questionText": "Scenario: You notice gradient oscillations in shallow network. Cause?",
      "options": [
        "Vanishing gradient",
        "High learning rate or noisy gradients",
        "Dead neurons",
        "Class imbalance"
      ],
      "correctAnswerIndex": 1,
      "explanation": "High learning rates can cause unstable updates and oscillating loss."
    },
    {
      "id": 64,
      "questionText": "Scenario: Network uses tanh in hidden layers. Advantage over sigmoid?",
      "options": [
        "Faster computation",
        "Prevents overfitting",
        "Removes vanishing gradient completely",
        "Outputs zero-centered, improving gradient flow"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Tanh outputs in [-1,1], helping gradients propagate better than sigmoid."
    },
    {
      "id": 65,
      "questionText": "Scenario: Network trained with noisy labels. Solution?",
      "options": [
        "Add more layers",
        "Use ReLU",
        "Increase regularization and possibly label smoothing",
        "Reduce learning rate only"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Regularization and label smoothing help mitigate noise impact."
    },
    {
      "id": 66,
      "questionText": "Scenario: You want to prevent overfitting but maintain capacity. Technique?",
      "options": [
        "Reduce neurons",
        "Increase batch size only",
        "Reduce layers",
        "Dropout or L2 regularization"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Dropout and weight decay help generalize without reducing model capacity."
    },
    {
      "id": 67,
      "questionText": "Scenario: Softmax probabilities are consistently close to 0.5 in binary classification. Cause?",
      "options": [
        "Overfitting",
        "Batch normalization failure",
        "Network not trained sufficiently or poor initialization",
        "Gradient explosion"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Poor training or initialization leads to low-confidence predictions."
    },
    {
      "id": 68,
      "questionText": "Scenario: You want to accelerate convergence using previous gradients. Technique?",
      "options": [
        "Gradient clipping",
        "Dropout",
        "Momentum",
        "Batch normalization"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Momentum uses past gradients to accelerate convergence and smooth updates."
    },
    {
      "id": 69,
      "questionText": "Scenario: Using SGD with mini-batches. Effect on gradient estimate?",
      "options": [
        "Always smaller than full gradient",
        "Always larger than full gradient",
        "Provides noisy but unbiased estimate of true gradient",
        "Exact gradient"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Mini-batches give noisy gradient approximations, which help generalization."
    },
    {
      "id": 70,
      "questionText": "Scenario: You observe network saturates at high loss. Likely cause?",
      "options": [
        "Activation functions causing vanishing gradients",
        "Softmax outputs",
        "Learning rate too small",
        "Too many neurons"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Saturation occurs when sigmoid/tanh outputs flatten, reducing gradient and slowing learning."
    },
    {
      "id": 71,
      "questionText": "Scenario: You are classifying high-resolution images with a fully connected network and poor performance. Likely solution?",
      "options": [
        "Switch to ReLU",
        "Increase hidden layers in fully connected network",
        "Reduce training data",
        "Use Convolutional Neural Networks (CNNs)"
      ],
      "correctAnswerIndex": 3,
      "explanation": "CNNs leverage spatial information and reduce parameters for image classification, unlike dense networks."
    },
    {
      "id": 72,
      "questionText": "Scenario: Classifying sequences of text. Which network type is most suitable?",
      "options": [
        "Fully connected network",
        "Recurrent Neural Networks (RNNs) or LSTMs",
        "CNNs only",
        "Autoencoders"
      ],
      "correctAnswerIndex": 1,
      "explanation": "RNNs and LSTMs handle sequential dependencies effectively in text or time-series data."
    },
    {
      "id": 73,
      "questionText": "Scenario: Imbalanced multi-class classification. Which strategy is appropriate?",
      "options": [
        "Increase learning rate",
        "Use batch normalization only",
        "Reduce hidden layers",
        "Use class weighting, oversampling minority classes, or focal loss"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Techniques like class weighting or focal loss mitigate the impact of imbalanced data on training."
    },
    {
      "id": 74,
      "questionText": "Scenario: Network predicts very high confidence for wrong predictions. Which technique can help?",
      "options": [
        "Add more neurons",
        "Remove batch normalization",
        "Increase learning rate",
        "Label smoothing"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Label smoothing reduces overconfidence by softening target labels during training."
    },
    {
      "id": 75,
      "questionText": "Scenario: You want to interpret which features most influence network predictions. Technique?",
      "options": [
        "Apply dropout",
        "Use SHAP or LIME for interpretability",
        "Reduce batch size",
        "Increase hidden layers"
      ],
      "correctAnswerIndex": 1,
      "explanation": "SHAP and LIME provide insights into feature importance for neural network predictions."
    },
    {
      "id": 76,
      "questionText": "Scenario: Training a deep CNN suffers from vanishing gradients. Solution?",
      "options": [
        "Increase dropout",
        "Reduce dataset size",
        "Use residual connections (ResNet) or batch normalization",
        "Use softmax in hidden layers"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Residual connections allow gradients to bypass layers, mitigating vanishing gradient problems."
    },
    {
      "id": 77,
      "questionText": "Scenario: Multi-class classification with overlapping classes. Which metric is most informative?",
      "options": [
        "Accuracy only",
        "Binary cross-entropy",
        "F1-score per class",
        "Mean squared error"
      ],
      "correctAnswerIndex": 2,
      "explanation": "F1-score balances precision and recall, providing better insight for overlapping classes."
    },
    {
      "id": 78,
      "questionText": "Scenario: Network shows high variance across validation folds. Likely cause?",
      "options": [
        "Learning rate too small",
        "Vanishing gradients",
        "Dead neurons",
        "Overfitting or insufficient regularization"
      ],
      "correctAnswerIndex": 3,
      "explanation": "High variance indicates the model fits some folds well but fails on others due to overfitting."
    },
    {
      "id": 79,
      "questionText": "Scenario: Using CNN for images, which technique reduces overfitting?",
      "options": [
        "Increase batch size only",
        "Use sigmoid activation",
        "Reduce learning rate only",
        "Data augmentation"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Data augmentation increases dataset diversity, reducing overfitting on limited training data."
    },
    {
      "id": 80,
      "questionText": "Scenario: Network outputs are consistently wrong for a particular class. Cause?",
      "options": [
        "Class is underrepresented or features insufficient",
        "Learning rate too high",
        "Batch normalization issue",
        "Dropout too low"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Insufficient representation or feature information for a class leads to poor predictions."
    },
    {
      "id": 81,
      "questionText": "Scenario: You want to reduce computation in CNN while maintaining accuracy. Technique?",
      "options": [
        "Use depthwise separable convolutions or pruning",
        "Increase fully connected layers",
        "Use sigmoid activation",
        "Reduce batch size"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Depthwise separable convolutions and pruning reduce computation while retaining accuracy."
    },
    {
      "id": 82,
      "questionText": "Scenario: Using RNN, you observe long-term dependencies are not learned. Solution?",
      "options": [
        "Use LSTM or GRU instead of vanilla RNN",
        "Increase hidden layers only",
        "Use ReLU activation in RNN",
        "Reduce batch size"
      ],
      "correctAnswerIndex": 0,
      "explanation": "LSTM and GRU have memory gates to capture long-term dependencies effectively."
    },
    {
      "id": 83,
      "questionText": "Scenario: Multi-label classification. Which activation in output layer?",
      "options": [
        "Softmax",
        "ReLU",
        "Sigmoid per output neuron",
        "Tanh"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Sigmoid allows each output to be independent for multi-label classification."
    },
    {
      "id": 84,
      "questionText": "Scenario: Multi-label classification. Appropriate loss function?",
      "options": [
        "Categorical cross-entropy",
        "Hinge loss",
        "Binary cross-entropy",
        "Mean squared error"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Binary cross-entropy evaluates each output independently in multi-label tasks."
    },
    {
      "id": 85,
      "questionText": "Scenario: Neural network trained on small dataset with overfitting. Best strategy?",
      "options": [
        "Increase hidden layers",
        "Reduce learning rate only",
        "Use sigmoid activation only",
        "Data augmentation and regularization"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Augmenting data and regularization improves generalization on small datasets."
    },
    {
      "id": 86,
      "questionText": "Scenario: Classifier misclassifies rare but critical cases. Metric to focus on?",
      "options": [
        "Accuracy",
        "Loss function only",
        "Batch size",
        "Recall or F2-score for minority class"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Recall emphasizes capturing minority class correctly, important in critical cases."
    },
    {
      "id": 87,
      "questionText": "Scenario: Gradients vanish in deep LSTM. Likely cause?",
      "options": [
        "Dropout too low",
        "Overfitting",
        "Batch normalization",
        "Improper initialization or too deep layers"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Deep networks may still suffer vanishing gradients if weights are poorly initialized."
    },
    {
      "id": 88,
      "questionText": "Scenario: You want explainability for image classification. Technique?",
      "options": [
        "Reduce layers",
        "Use softmax only",
        "Increase dropout",
        "Use Grad-CAM or saliency maps"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Grad-CAM highlights important regions influencing CNN predictions."
    },
    {
      "id": 89,
      "questionText": "Scenario: Network converges to poor local minimum. Strategy?",
      "options": [
        "Increase dropout only",
        "Use different initialization, optimizers, or learning rate schedules",
        "Remove batch normalization",
        "Reduce neurons"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Initialization and optimizer strategies help escape poor local minima."
    },
    {
      "id": 90,
      "questionText": "Scenario: Network trained with adversarial examples. Purpose?",
      "options": [
        "Increase hidden layers",
        "Reduce learning rate",
        "Reduce overfitting",
        "Improve robustness against input perturbations"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Adversarial training prepares the network to handle small input perturbations safely."
    },
    {
      "id": 91,
      "questionText": "Scenario: CNN with skip connections. Advantage?",
      "options": [
        "Reduces dataset size",
        "Mitigates vanishing gradient and allows deeper networks",
        "Removes need for activation",
        "Reduces neurons only"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Skip connections in ResNet allow gradients to bypass layers, improving deep network training."
    },
    {
      "id": 92,
      "questionText": "Scenario: Multi-class classification with imbalanced data. Strategy to monitor?",
      "options": [
        "Loss function only",
        "Use per-class precision, recall, and F1-score",
        "Accuracy only",
        "Batch size only"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Per-class metrics reveal model performance for minority classes better than overall accuracy."
    },
    {
      "id": 93,
      "questionText": "Scenario: You want to deploy a network efficiently on edge devices. Strategy?",
      "options": [
        "Use deep fully connected layers",
        "Increase neurons",
        "Model compression, pruning, quantization",
        "Increase batch size"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Compression techniques reduce memory and compute requirements for deployment."
    },
    {
      "id": 94,
      "questionText": "Scenario: CNN predictions degrade on slightly shifted images. Technique?",
      "options": [
        "Use sigmoid instead of ReLU",
        "Reduce neurons",
        "Data augmentation with shifts or spatial transformers",
        "Increase hidden layers"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Data augmentation improves generalization to variations not seen in training."
    },
    {
      "id": 95,
      "questionText": "Scenario: Multi-class classification with label noise. Robust approach?",
      "options": [
        "Increase learning rate",
        "Add more layers",
        "Reduce batch size",
        "Use label smoothing or robust loss functions"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Label smoothing and robust losses mitigate the impact of incorrect labels."
    },
    {
      "id": 96,
      "questionText": "Scenario: Recurrent network fails on long sequences. Alternative?",
      "options": [
        "Use dropout only",
        "Use Transformer-based architectures",
        "Increase hidden units in RNN",
        "Increase batch size"
      ],
      "correctAnswerIndex": 1,
      "explanation": "Transformers handle long-range dependencies better than RNNs/LSTMs."
    },
    {
      "id": 97,
      "questionText": "Scenario: Neural network trained with batch size 1. Issue?",
      "options": [
        "No effect",
        "Overfitting automatically",
        "Noisy gradient updates and slower convergence",
        "Dead neurons"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Batch size 1 (stochastic) leads to noisy gradients and unstable training."
    },
    {
      "id": 98,
      "questionText": "Scenario: Outputs are probabilities but poorly calibrated. Technique?",
      "options": [
        "Increase learning rate",
        "Reduce layers",
        "Use temperature scaling or calibration methods",
        "Increase neurons"
      ],
      "correctAnswerIndex": 2,
      "explanation": "Calibration methods adjust predicted probabilities to better reflect true likelihoods."
    },
    {
      "id": 99,
      "questionText": "Scenario: Multi-class network with many small classes. Strategy?",
      "options": [
        "Reduce learning rate",
        "Use standard cross-entropy only",
        "Use ReLU in output layer",
        "Oversample small classes or use focal loss"
      ],
      "correctAnswerIndex": 3,
      "explanation": "Oversampling or focal loss emphasizes minority classes during training."
    },
    {
      "id": 100,
      "questionText": "Scenario: Network deployed in real-time system misclassifies rare events. Approach?",
      "options": [
        "Retrain with targeted sampling or weighted loss for rare events",
        "Use smaller network",
        "Increase learning rate only",
        "Reduce batch size"
      ],
      "correctAnswerIndex": 0,
      "explanation": "Targeted retraining or weighted loss ensures rare but critical events are correctly learned."
    }
  ]
}