MCQs

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Overall MCQs for Reference

1. What is the primary purpose of a Convolutional Neural Network (CNN)?
   a. Object detection
   b. Image classification
   c. Text generation
   d. Reinforcement learning
   Answer: b. Image classificationaimcqs+1​
2. Which layer type is typically used to extract local features in a CNN?
   a. Convolutional layer
   b. Pooling layer
   c. Fully connected layer
   d. Activation layer
   Answer: a. Convolutional layergithub+1​
3. What is the advantage of using convolutional layers in a CNN?
   a. Capture local spatial patterns in the input data
   b. Handle sequential data
   c. Generate synthetic data
   d. Handle variable-length inputs
   Answer: a. Capture local spatial patterns in the input dataaimcqs+1​
4. What is the purpose of the pooling layer in a CNN?
   a. Reduce the spatial dimensions of the feature maps
   b. Introduce non-linearity to the network
   c. Adjust weights and biases of the network
   d. Compute gradients for backpropagation
   Answer: a. Reduce the spatial dimensions of the feature mapsgithub+1​
5. Which activation function is commonly used in convolutional layers of a CNN?
   a. Sigmoid
   b. ReLU
   c. Tanh
   d. Softmax
   Answer: b. ReLUprojectpro+1​
6. In CNNs, what is a kernel or filter?
   a. Algorithm for optimization
   b. Matrix applied for feature extraction
   c. Pooling strategy
   d. Loss function
   Answer: b. Matrix applied for feature extractiongeeksforgeeks+1​
7. What does stride refer to in convolution operations?
   a. Steps taken in pooling
   b. Steps filter moves over the input
   c. Number of filters
   d. Activation function
   Answer: b. Steps filter moves over the inputgeeksforgeeks+1​
8. What is zero padding in CNNs used for?
   a. Increase number of filters
   b. Preserve spatial dimensions
   c. Normalize feature maps
   d. Speed up training
   Answer: b. Preserve spatial dimensionsprojectpro+1​
9. Which layer type connects neurons to every neuron in the next layer?
   a. Pooling layer
   b. Fully connected layer
   c. Convolutional layer
   d. Batch normalization layer
   Answer: b. Fully connected layergithub+1​
10. What does “feature map” in a CNN mean?
    a. Input layer
    b. Output of convolution operation
    c. Activation function
    d. Pooling result
    Answer: b. Output of convolution operationgeeksforgeeks+1​
11. Which pooling technique takes the highest value in a window?
    a. Average pooling
    b. Max pooling
    c. Global pooling
    d. Min pooling
    Answer: b. Max poolingprojectpro+1​
12. CNNs are most suitable for which data type?
    a. Sequential data
    b. Tabular data
    c. Grid-like data (images)
    d. Text data
    Answer: c. Grid-like data (images)geeksforgeeks+1​
13. Which layer helps CNNs generalize better by reducing parameters?
    a. Dense layer
    b. Pooling layer
    c. Dropout layer
    d. Input layer
    Answer: b. Pooling layergithub+1​
14. Why are CNNs preferred over classical image processing techniques?
    a. Manual feature engineering
    b. Automatic feature extraction
    c. Slow computation
    d. Only linear activations
    Answer: b. Automatic feature extractiongeeksforgeeks+1​
15. What is the effect of increasing kernel size in a convolutional layer?
    a. Smaller receptive field
    b. Sparse feature extraction
    c. Larger receptive field
    d. Less computations
    Answer: c. Larger receptive fieldgithub+1​
16. Which function is used for classification in the output layer of CNN?
    a. ReLU
    b. Tanh
    c. Softmax
    d. Linear
    Answer: c. Softmaxprojectpro+1​
17. What does “channel” in CNN typically refer to?
    a. Output size
    b. Input color dimensions (e.g., RGB)
    c. Number of pooling layers
    d. Gradient computation
    Answer: b. Input color dimensions (e.g., RGB)geeksforgeeks+1​
18. What is the purpose of filter/kernel in a convolutional layer?
    a. Determines layer size
    b. Specifies feature map size
    c. Extracts local features
    d. None of the above
    Answer: c. Extracts local featuresaimcqs+1​
19. Which layer type is used to normalize input data in CNNs?
    a. Convolutional layer
    b. Pooling layer
    c. Batch normalization layer
    d. Activation layer
    Answer: c. Batch normalization layeraimcqs+1​
20. What is the primary goal of training a CNN?
    a. Minimize prediction error
    b. Maximize number of layers
    c. Achieve 100% accuracy
    d. None of the above
    Answer: a. Minimize prediction erroraimcqs+1​
21. Which layer introduces translation invariance in CNNs?
    a. Convolutional layer
    b. Pooling layer
    c. Fully connected layer
    d. Activation layer
    Answer: a. Convolutional layeraimcqs+1​
22. What is the purpose of the output layer in a CNN?
    a. Provides final prediction or probabilities
    b. Normalizes the input
    c. Reduces feature maps
    d. Generates intermediate features
    Answer: a. Provides final prediction or probabilitiesgithub+1​
23. What does backpropagation do in CNNs?
    a. Reduces feature maps
    b. Updates model weights
    c. Generates output classes
    d. Applies pooling
    Answer: b. Updates model weightsgeeksforgeeks+1​
24. What is a receptive field in CNN?
    a. Network output
    b. Area of input matrix processed by filter
    c. Layer type
    d. Pooling window
    Answer: b. Area of input matrix processed by filtergithub+1​
25. What is transfer learning in CNNs?
    a. Sharing filters between layers
    b. Using pre-trained models on new tasks
    c. Increasing padding
    d. Decreasing kernel size
    Answer: b. Using pre-trained models on new tasksaionlinecourse+1​
26. Which optimizer is commonly used in training CNNs?
    a. Adam
    b. Momentum
    c. RMSProp
    d. All of the above
    Answer: d. All of the aboveprojectpro+1​
27. What is dropout used for in CNN architectures?
    a. Increasing training data
    b. Reducing overfitting
    c. Highlighting features
    d. Pooling
    Answer: b. Reducing overfittinggeeksforgeeks+1​
28. Which characteristic is unique to CNNs compared to MLPs?
    a. Local connectivity
    b. Use of softmax
    c. Linear activation
    d. High parameter count
    Answer: a. Local connectivitygithub+1​
29. What happens during the forward pass of a CNN?
    a. Weight update
    b. Loss calculation
    c. Data flows through layers
    d. Gradient computation
    Answer: c. Data flows through layersgeeksforgeeks+1​
30. What is an epoch in CNN training?
    a. One image processed
    b. One batch processed
    c. One full pass through the dataset
    d. Optimizer step
    Answer: c. One full pass through the datasetprojectpro+1​
31. When is overfitting likely in CNN?
    a. Small training dataset
    b. Few layers
    c. Use of pooling
    d. Only dense layers
    Answer: a. Small training datasetgithub+1​
32. What is Data Augmentation in CNNs?
    a. Adding artificial samples by transforming existing data
    b. Increasing learning rate
    c. Pruning filters
    d. Removing layers
    Answer: a. Adding artificial samples by transforming existing datagyansetu+1​
33. Which is a common application for CNNs?
    a. Image classification
    b. Time series prediction
    c. Speech recognition
    d. Database indexing
    Answer: a. Image classificationgeeksforgeeks+1​
34. What do weights in CNN control?
    a. Output class
    b. Feature extraction
    c. Learning speed
    d. Input type
    Answer: b. Feature extractiongithub+1​
35. Which metric indicates CNN performance for classification?
    a. Accuracy
    b. Mean squared error
    c. AUC
    d. Recall
    Answer: a. Accuracyprojectpro+1​
36. What is global average pooling?
    a. Pooling over entire spatial dimension
    b. Taking max value from patch
    c. Convolution operation
    d. Padding with zeros
    Answer: a. Pooling over entire spatial dimensionprojectpro+1​
37. What is vanishing gradient in CNNs?
    a. Gradients become too small for weight update
    b. Gradients explode
    c. Loss increases
    d. Weights set to zero
    Answer: a. Gradients become too small for weight updateprojectpro+1​
38. What does “stride 1” mean in convolution?
    a. Filter moves one step at a time
    b. Multiple filters used
    c. Filter increases in size
    d. Output flattened
    Answer: a. Filter moves one step at a timegeeksforgeeks+1​
39. Which is a technique to handle imbalanced datasets with CNN?
    a. Data augmentation
    b. Increasing filters
    c. Decreasing pooling
    d. Changing stride
    Answer: a. Data augmentationgyansetu+1​
40. When is batch normalization applied in CNNs?
    a. After convolutional layer
    b. Before pooling
    c. Only in output layer
    d. Only in input layer
    Answer: a. After convolutional layeraimcqs+1​
41. In CNNs, what is a parameter?
    a. Learnable value
    b. Fixed hyperparameter
    c. Input layer output
    d. Activation result
    Answer: a. Learnable valuegithub+1​
42. How does convolution reduce computation versus dense layers?
    a. Shared weights
    b. Increased parameters
    c. Data duplication
    d. Higher learning rate
    Answer: a. Shared weightsgeeksforgeeks+1​
43. What is “spatial hierarchy of features” in CNNs?
    a. Features extracted at different network depths
    b. Only top layer features
    c. Flattening feature maps
    d. Random initialization
    Answer: a. Features extracted at different network depthscloudxlab+1​
44. What does flattening do in CNNs?
    a. Converts feature maps to a 1D vector
    b. Adds filters
    c. Applies pooling
    d. Increases spatial dimensions
    Answer: a. Converts feature maps to a 1D vectorgithub+1​
45. What is an input volume in CNN?
    a. 3D array representing image data
    b. Activation value
    c. Filter
    d. Pooling output
    Answer: a. 3D array representing image datawayground+1​
46. Which loss function is common for multi-class image classification with CNNs?
    a. Binary cross-entropy
    b. Mean squared error
    c. Categorical cross-entropy
    d. Hinge loss
    Answer: c. Categorical cross-entropyprojectpro+1​
47. How is translation invariance achieved in CNNs?
    a. Convolutions and pooling
    b. Weight sharing
    c. Dense layers
    d. Data augmentation only
    Answer: a. Convolutions and poolingaimcqs+1​
48. What defines depth in a CNN model?
    a. Number of layers
    b. Width of filter
    c. Input channels
    d. Feature map size
    Answer: a. Number of layersgeeksforgeeks+1​
49. What is receptive field expansion in deeper layers?
    a. Filters see larger parts of input
    b. Output reduced
    c. Fewer weights required
    d. Only pooling used
    Answer: a. Filters see larger parts of inputcloudxlab+1​
50. Which approach prevents overfitting in CNNs?
    a. Dropout and data augmentation
    b. Increasing filter size
    c. Removing softmax
    d. Reducing learning rate
    Answer: a. Dropout and data augmentationgyansetu+1​

1. https://aimcqs.com/convolutional-neural-networks
2. https://github.com/Devinterview-io/cnn-interview-questions
3. https://www.projectpro.io/article/convolutional-neural-network-interview-questions-and-answers/727
4. https://www.geeksforgeeks.org/deep-learning/convolutional-neural-network-cnn-in-machine-learning/
5. https://www.geeksforgeeks.org/machine-learning/introduction-convolution-neural-network/
6. https://www.aionlinecourse.com/ai-quiz-questions/deep-learning/convolutional-neural-networks-dl
7. https://www.gyansetu.in/blog/60-deep-learning-objective-type-quiz-mcqs-with-answers-for-interview-preparation/
8. https://cloudxlab.com/assessment/displayslide/5858/cnn-mcq-cnns-allows-the-network-to-concentrate-on-low-level-features-in-the-first-hidden-layer-then-assemble-them-into-higher-level-features-in-the-next-hidden-layer-and-so-on
9. https://wayground.com/admin/quiz/5f2544e17c727f001b6b97c4/convolutional-neural-network
10. https://www.scribd.com/document/896655603/CNN-Mcqs
11. https://mriquestions.com/convolutional-network.html
12. https://www.sanfoundry.com/1000-neural-networks-questions-answers/
13. https://www.scribd.com/document/458954860/Quiz02
14. https://cambum.net/CCE/Lec8.pdf
15. https://devinterview.io/questions/machine-learning-and-data-science/cnn-interview-questions/
16. https://blog.accredian.com/tag/convolutional-neural-network-questions-and-answers/