{ "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." } ] }