MCQs

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Overall MCQs for Reference

1. What is the primary goal of image classification in machine learning?
   a. Predict continuous values
   b. Assign predefined labels to images
   c. Enhance image quality
   d. Cluster images without labels
   Answer: b. Assign predefined labels to imagesgeeksforgeeks+1​
2. Which machine learning algorithm is commonly used for image classification tasks?
   a. Linear Regression
   b. Support Vector Machines (SVM)
   c. K-Means Clustering
   d. Naive Bayes
   Answer: b. Support Vector Machines (SVM)easyexamnotes+1​
3. Which deep learning architecture is most widely used for image classification?
   a. Recurrent Neural Network (RNN)
   b. Decision Trees
   c. Convolutional Neural Network (CNN)
   d. Logistic Regression
   Answer: c. Convolutional Neural Network (CNN)aimcqs+1​
4. What is the main advantage of CNNs over traditional feature extraction methods in image classification?
   a. Manual feature engineering
   b. Automatic feature learning
   c. Increased computational complexity
   d. Fixed input size
   Answer: b. Automatic feature learningencord+1​
5. What is a typical preprocessing step in image classification pipelines?
   a. Data normalization
   b. Data encryption
   c. Data fragmentation
   d. Data obfuscation
   Answer: a. Data normalizationgeeksforgeeks​
6. Why is data augmentation used in image classification tasks?
   a. Increases security
   b. Reduces overfitting
   c. Decreases feature accuracy
   d. Slows down model training
   Answer: b. Reduces overfittingkili-technology+1​
7. What are ImageNet and CIFAR-10 examples of in image classification?
   a. Preprocessing techniques
   b. Evaluation metrics
   c. Labeled datasets
   d. Feature extraction methods
   Answer: c. Labeled datasetseasyexamnotes+1​
8. Which method is used to split data for training and testing in image classification?
   a. Feature selection
   b. Data augmentation
   c. Cross-validation
   d. Label encoding
   Answer: c. Cross-validationgeeksforgeeks​
9. In supervised learning for image classification, what must be included in training data?
   a. Unlabeled images
   b. Predefined labels
   c. Data encryption keys
   d. Random noise
   Answer: b. Predefined labelsdevelopers.google+1​
10. What is the purpose of feature extraction in image classification?
    a. Encrypt image data
    b. Reduce image dimensionality
    c. Increase image noise
    d. Generate labels automatically
    Answer: b. Reduce image dimensionalityscribd+1​
11. What type of activation function is frequently used in CNNs for image classification?
    a. Linear
    b. ReLU (Rectified Linear Unit)
    c. Sigmoid
    d. Softmax
    Answer: b. ReLU (Rectified Linear Unit)gyansetu​
12. Which of the following represents output from an image classification model?
    a. Pixel intensities
    b. Feature maps
    c. Probability distribution over classes
    d. Filtering kernel weights
    Answer: c. Probability distribution over classesdevelopers.google+1​
13. What does overfitting in image classification refer to?
    a. Model fits random noise
    b. Model fits training data too closely
    c. Model ignores training data
    d. Model underestimates complexity
    Answer: b. Model fits training data too closelyigmguru​
14. What is transfer learning in the context of image classification?
    a. Training without labeled data
    b. Using pre-trained model weights
    c. Ignoring feature selection
    d. Manual feature engineering
    Answer: b. Using pre-trained model weightsaimcqs+1​
15. What is the role of pooling layers in CNNs?
    a. Increase spatial resolution
    b. Reduce dimensionality and computation
    c. Add non-linearity
    d. Convert labels to features
    Answer: b. Reduce dimensionality and computationencord+1​
16. Which problem does dropout address when training image classifiers?
    a. Underfitting
    b. Overfitting
    c. Feature scaling
    d. Model evaluation
    Answer: b. Overfittinggyansetu+1​
17. Which metric is commonly used to evaluate performance in image classification?
    a. Mean squared error
    b. F1-score
    c. Image entropy
    d. Signal-to-noise ratio
    Answer: b. F1-scoregeeksforgeeks​
18. What is the first step in building an image classification model?
    a. Training the model directly
    b. Collecting and labeling data
    c. Evaluating accuracy
    d. Hyperparameter tuning
    Answer: b. Collecting and labeling datadevelopers.google+1​
19. What is the role of softmax in deep learning-based image classification?
    a. Feature normalization
    b. Converting scores to class probabilities
    c. Model regularization
    d. Spatial pooling
    Answer: b. Converting scores to class probabilitiesgeeksforgeeks​
20. What is the output of a binary image classification model?
    a. Bounding boxes
    b. Class probabilities for two classes
    c. Image segmentation masks
    d. Multi-class labels
    Answer: b. Class probabilities for two classesdevelopers.google​
21. What differentiates supervised and unsupervised learning for image classification?
    a. Need for labeled data
    b. Use of clustering
    c. Use of regression models
    d. Feature extraction domain
    Answer: a. Need for labeled datageeksforgeeks​
22. Which non-deep learning classifier is popular for small image datasets?
    a. K-means clustering
    b. K-nearest neighbor (KNN)
    c. CNN
    d. Random forest
    Answer: b. K-nearest neighbor (KNN)easyexamnotes​
23. What is the primary reason for normalizing pixel values in image classification?
    a. Increase model bias
    b. Stabilize training
    c. Add image noise
    d. Personalize outputs
    Answer: b. Stabilize trainingdevelopers.google+1​
24. What is AlexNet best known for?
    a. Image segmentation
    b. Winning the ImageNet competition
    c. Object detection
    d. Facial recognition
    Answer: b. Winning the ImageNet competitionaimcqs​
25. In what situation would label encoding be necessary?
    a. For continuous variables
    b. When labels are categorical
    c. When labels are encrypted
    d. On grayscale images
    Answer: b. When labels are categoricalgeeksforgeeks​
26. What is an epoch in training CNNs?
    a. One gradient computation
    b. One pass through entire dataset
    c. One neuron activation
    d. One forward propagation
    Answer: b. One pass through entire datasetgeeksforgeeks​
27. What does the term “fine-tuning” mean in transfer learning?
    a. Training from scratch
    b. Adjusting pre-trained model weights
    c. Changing network architecture
    d. Data augmentation
    Answer: b. Adjusting pre-trained model weightsgeeksforgeeks​
28. Why is the validation set used in image classification?
    a. For final testing
    b. To tune hyperparameters
    c. To encode labels
    d. For image segmentation
    Answer: b. To tune hyperparametersgeeksforgeeks​
29. What purpose does a confusion matrix serve in image classification evaluation?
    a. Visualizes pixel intensities
    b. Shows model accuracy
    c. Summarizes prediction outcomes
    d. Encrypts model weights
    Answer: c. Summarizes prediction outcomesgeeksforgeeks​
30. What effect does increasing convolutional filter size have?
    a. Decreases receptive field
    b. Increases receptive field
    c. Reduces memory usage
    d. Disables feature extraction
    Answer: b. Increases receptive fieldencord+1​
31. Which dataset is widely used for handwritten digit recognition image classification?
    a. CIFAR-10
    b. ImageNet
    c. MNIST
    d. COCO
    Answer: c. MNISTgeeksforgeeks​
32. What technique is used to handle class imbalance in image datasets?
    a. Data shuffling
    b. Oversampling minority class
    c. Feature scaling
    d. Filter normalization
    Answer: b. Oversampling minority classgeeksforgeeks​
33. Which method improves model generalization in image classification?
    a. Increasing training data size
    b. Reducing validation set size
    c. Ignoring feature transformations
    d. Removing regularization
    Answer: a. Increasing training data sizegeeksforgeeks​
34. What is batch normalization used for in CNNs?
    a. Decreases learning rate
    b. Reduces internal covariate shift
    c. Increases model depth
    d. Removes activation functions
    Answer: b. Reduces internal covariate shiftgyansetu​
35. Which method is typically used for dimensionality reduction in image classification?
    a. Principal Component Analysis (PCA)
    b. K-means clustering
    c. Histogram Equalization
    d. Image resizing
    Answer: a. Principal Component Analysis (PCA)scribd​
36. What is the effect of using more layers in a CNN model for classification?
    a. Reduces model flexibility
    b. Improves ability to learn complex features
    c. Prevents overfitting
    d. Lowers accuracy
    Answer: b. Improves ability to learn complex featuresencord+1​
37. Which loss function is most often used for multi-class image classification?
    a. Mean Absolute Error
    b. Mean Squared Error
    c. Categorical Cross-Entropy
    d. Hinge Loss
    Answer: c. Categorical Cross-Entropyencord+1​
38. What does Top-1 accuracy measure?
    a. Whether the top prediction matches ground truth
    b. Sum of all correct predictions
    c. All predictions for all classes
    d. Speed of model training
    Answer: a. Whether the top prediction matches ground truthdevelopers.google​
39. What is image segmentation in relation to image classification?
    a. Assigns pixel-wise labels
    b. Predicts bounding box coordinates
    c. Classifies entire images
    d. Clusters input images
    Answer: a. Assigns pixel-wise labelsgeeksforgeeks​
40. What approach is maximum likelihood classification based upon?
    a. Manual feature filtering
    b. Statistical probability of features
    c. Contextual segmentation
    d. Heuristic search
    Answer: b. Statistical probability of featuresviso​
41. Which approach is common for weakly supervised image classification?
    a. No labels used
    b. Partial labels or noisy labels used
    c. Zero images required
    d. Only validation set applied
    Answer: b. Partial labels or noisy labels usedgeeksforgeeks​
42. In CNNs, what are filters used for?
    a. Downsampling images
    b. Extracting spatial features
    c. Flattening tensors
    d. Normalizing pixel values
    Answer: b. Extracting spatial featuresencord+1​
43. What does the stride parameter in a convolution operation affect?
    a. Learning rate
    b. Movement of filter window
    c. Number of epochs
    d. Width of hidden layers
    Answer: b. Movement of filter windowgeeksforgeeks​
44. Which algorithm uses statistical models for image classification?
    a. Bayesian classifier
    b. K-nearest neighbor
    c. Histogram equalization
    d. Image resizing
    Answer: a. Bayesian classifieribm​
45. What process helps ensure fair model evaluation across image classes?
    a. Random shuffling
    b. Stratified sampling
    c. Feature normalization
    d. Activation scaling
    Answer: b. Stratified samplingdevelopers.google+1​
46. Which of the following is a challenge in image classification?
    a. High intra-class variance
    b. Uniform lighting conditions
    c. Perfect segmentation
    d. Unlimited labeled data
    Answer: a. High intra-class varianceencord​
47. Why do image classification models use one-hot encoding for labels?
    a. Simplifies regression
    b. Avoids label bias
    c. Converts categorical labels to numerical format
    d. Eliminates overfitting
    Answer: c. Converts categorical labels to numerical formatgeeksforgeeks​
48. What is an ensemble method in image classification?
    a. Training models individually
    b. Combining predictions of multiple models
    c. Removing weak learners
    d. Performing image segmentation first
    Answer: b. Combining predictions of multiple modelsgeeksforgeeks​
49. What does “real-time image classification” refer to?
    a. Batch predictions
    b. Immediate label output
    c. Automated image resizing
    d. Manual annotation
    Answer: b. Immediate label outputviso​
50. Which of the following is a key component in CNN architecture for image classification?
    a. Recurrent layers
    b. Convolutional layers
    c. Temporal pooling
    d. Text embeddings
    Answer: b. Convolutional layersencord+1​

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19. https://aimcqs.com/supervised-learning
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