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​
2. Which layer 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 layeraimcqs​
3. What is the main advantage of convolutional layers in CNNs?
   a. They capture local spatial patterns
   b. They handle sequential data
   c. They generate synthetic data
   d. They process variable-length inputs
   Answer: a. They capture local spatial patternsaimcqs​
4. What is the purpose of the pooling layer in a CNN?
   a. Reduce spatial dimensions of feature maps
   b. Introduce non-linearity
   c. Adjust weights and biases
   d. Compute gradients for backpropagation
   Answer: a. Reduce spatial dimensions of feature mapsscribd+1​
5. Which activation function is commonly used in CNN convolutional layers?
   a. Sigmoid
   b. Tanh
   c. ReLU
   d. Softmax
   Answer: c. ReLUscribd+1​
6. Which architecture is NOT commonly used in CNNs?
   a. AlexNet
   b. VGG
   c. Transformer
   d. ResNet
   Answer: c. Transformerstudocu​
7. What is the primary benefit of residual connections in ResNet?
   a. Training deeper networks
   b. Reducing convolutional layers
   c. Improving computational efficiency
   d. Eliminating need for activations
   Answer: a. Training deeper networksstudocu​
8. What is the purpose of dropout regularization in a CNN?
   a. Prevent overfitting by randomly disabling neurons
   b. Adjust learning rate
   c. Increase layers
   d. None of the above
   Answer: a. Prevent overfitting by randomly disabling neuronsscribd+1​
9. Which pooling method is most commonly used in CNNs?
   a. Max pooling
   b. Min pooling
   c. Average pooling
   d. Median pooling
   Answer: a. Max poolingstudocu​
10. Why are global average pooling (GAP) layers used in some CNN architectures?
    a. Reduce overfitting and minimize parameters
    b. Increase non-linearity
    c. Allow better feature visualization
    d. Speed up inference
    Answer: a. Reduce overfitting and minimize parametersstudocu​
11. What are 1×1 convolutions used for in Inception networks?
    a. Reduce computational cost
    b. Add non-linearity
    c. Perform dimensionality reduction
    d. All of the above
    Answer: d. All of the abovestudocu​
12. What is the main advantage of depthwise separable convolutions in MobileNet?
    a. Increased accuracy
    b. Reduced computational cost
    c. Reduced overfitting
    d. Increased parameters
    Answer: b. Reduced computational coststudocu​
13. Which layer applies non-linear transformations to feature maps?
    a. Convolutional layer
    b. Pooling layer
    c. Fully connected layer
    d. Activation layer
    Answer: d. Activation layeraimcqs​
14. What does the term “stride” refer to in CNNs?
    a. Step size of the filter across input
    b. Number of neurons in a layer
    c. Input data split size
    d. Output class count
    Answer: a. Step size of the filter across inputgeeksforgeeks​
15. What is dilation in the context of CNNs?
    a. Increasing filter size
    b. Space inserted between kernel elements
    c. Reducing feature maps
    d. Increasing feature dimension
    Answer: b. Space inserted between kernel elementsstudocu​
16. What does vanishing gradient in deep CNNs cause?
    a. Difficult training in deep networks
    b. Faster convergence
    c. Higher accuracy
    d. Model regularization
    Answer: a. Difficult training in deep networksscribd+1​
17. Batch normalization in CNNs helps:
    a. Reduce internal covariate shift
    b. Increase model complexity
    c. Increase layer count
    d. Remove activation functions
    Answer: a. Reduce internal covariate shiftstudocu​
18. What is transfer learning in CNNs?
    a. Reusing pretrained weights for new tasks
    b. Training from scratch
    c. Reducing generalization
    d. Eliminating convolutional layers
    Answer: a. Reusing pretrained weights for new tasksstudocu​
19. Which layer reduces feature map size but keeps spatial information?
    a. Max pooling
    b. Average pooling
    c. Convolutional
    d. Both a and b
    Answer: d. Both a and baimcqs+1​
20. Which activation function is mostly used in output layers for multi-class classification in CNNs?
    a. ReLU
    b. Softmax
    c. Tanh
    d. Sigmoid
    Answer: b. Softmaxstudocu​
21. A CNN input image usually must be:
    a. Square matrix
    b. Preprocessed for uniformity
    c. Color only
    d. Text only
    Answer: b. Preprocessed for uniformitygeeksforgeeks​
22. What do filters (kernels) do in convolutional layers?
    a. Detect visual features
    b. Apply pooling
    c. Optimize learning rate
    d. Flatten feature maps
    Answer: a. Detect visual featuresgeeksforgeeks​
23. Which task are CNNs state-of-the-art for?
    a. Image classification
    b. Speech synthesis
    c. NLP summarization
    d. Time series prediction
    Answer: a. Image classificationgeeksforgeeks​
24. What does backpropagation achieve in CNNs?
    a. Updates weights by error correction
    b. Removes pooling layers
    c. Set dropout rate
    d. Increase kernel shape
    Answer: a. Updates weights by error correctionscribd​
25. Which deep learning model best suits time series forecasting?
    a. RNN
    b. CNN
    c. GAN
    d. VAE
    Answer: a. RNNscribd​
26. Which CNN architecture was first to use ReLU activation extensively?
    a. LeNet
    b. AlexNet
    c. GoogLeNet
    d. VGG
    Answer: b. AlexNetstudocu​
27. What improves training speed and stability in deep CNNs?
    a. Batch normalization
    b. Increasing learning rate
    c. Data augmentation
    d. Fewer parameters
    Answer: a. Batch normalizationstudocu​
28. Data augmentation in CNN training is used to:
    a. Increase dataset diversity
    b. Reduce layer count
    c. Eliminate overfitting
    d. Add more pooling
    Answer: a. Increase dataset diversitystudocu​
29. Dropout rate refers to:
    a. Proportion of neurons deactivated in training
    b. Learning rate scheduling
    c. Number of convolutional filters
    d. Type of pooling
    Answer: a. Proportion of neurons deactivated in trainingscribd​
30. Which layer in CNNs makes the final prediction?
    a. Convolutional
    b. Pooling
    c. Fully connected
    d. Dropout
    Answer: c. Fully connectedgeeksforgeeks+1​
31. What is one limitation of excessive pooling?
    a. Loss of spatial info
    b. Increased accuracy
    c. Higher gradients
    d. More parameters
    Answer: a. Loss of spatial infostudocu​
32. What is a kernel in the context of CNNs?
    a. Small matrix used to scan the input
    b. Aggregation function
    c. Output layer
    d. Optimizer
    Answer: a. Small matrix used to scan the inputgeeksforgeeks​
33. Which models utilize skip connections?
    a. ResNet
    b. AlexNet
    c. VGG
    d. Inception
    Answer: a. ResNetstudocu​
34. What is feature map in CNNs?
    a. Output after convolution
    b. Data before pooling
    c. Loss function
    d. Regularizer
    Answer: a. Output after convolutiongeeksforgeeks​
35. How does max pooling work?
    a. Takes maximum value from a region
    b. Takes average
    c. Sums values
    d. Sorts values
    Answer: a. Takes maximum value from a regionaimcqs+1​
36. Strided convolution is used to:
    a. Reduce dimensionality
    b. Increase filter size
    c. Add non-linearity
    d. Regularize model
    Answer: a. Reduce dimensionalitystudocu​
37. The vanishing gradient problem makes deep networks:
    a. Harder to train
    b. Easier to train
    c. Overfit frequently
    d. Converge faster
    Answer: a. Harder to trainscribd+1​
38. What does zero padding achieve in CNNs?
    a. Maintain border information
    b. Reduce neuron count
    c. Add dropouts
    d. Increase stride
    Answer: a. Maintain border informationgeeksforgeeks​
39. Which regularization is commonly used in CNNs?
    a. Dropout
    b. Weight decay
    c. L2 regularization
    d. All of the above
    Answer: d. All of the abovescribd+1​
40. Why is ReLU preferred over sigmoid in deep CNNs?
    a. Reduces vanishing gradient effect
    b. Smoother outputs
    c. No non-linearity
    d. Increases computation
    Answer: a. Reduces vanishing gradient effectaimcqs+1​
41. CNN can be adapted for text classification by:
    a. Modifying input representation
    b. Applying pooling only
    c. Removing convolution
    d. Using fewer layers
    Answer: a. Modifying input representationscribd​
42. Feature extraction in CNNs is performed by:
    a. Convolutional layers
    b. Pooling layers
    c. Dropout layers
    d. Batch norm layers
    Answer: a. Convolutional layersaimcqs+1​
43. In which domain are CNNs widely used outside vision?
    a. Audio recognition
    b. Database sorting
    c. Cryptography
    d. OS simulation
    Answer: a. Audio recognitionscribd​
44. Which optimization algorithm is commonly used for deep CNN training?
    a. Adam
    b. K-means
    c. Q-learning
    d. PCA
    Answer: a. Adamstudocu​
45. What is receptive field in CNN context?
    a. Region of input influencing a neuron
    b. Final output category
    c. Pooling size only
    d. Loss function shape
    Answer: a. Region of input influencing a neurongeeksforgeeks​
46. Why are CNNs more efficient for images than MLPs?
    a. Fewer parameters by weight sharing
    b. Require large datasets
    c. Work only for binary data
    d. No need for normalization
    Answer: a. Fewer parameters by weight sharingaimcqs+1​
47. Which technique visualizes activations in deep CNNs?
    a. Activation maps
    b. Data plotting
    c. Weight normalization
    d. String parsing
    Answer: a. Activation mapsgeeksforgeeks​
48. CNNs in medical imaging are state-of-the-art for:
    a. Cancer detection
    b. Lesion segmentation
    c. Disease classification
    d. All of the above
    Answer: d. All of the abovegyansetu+1​
49. A common challenge in deep CNNs is:
    a. Overfitting
    b. Underfitting
    c. Simple tuning
    d. Batch normalization
    Answer: a. Overfittinggyansetu+1​
50. Which concept helps a model learn multiple tasks at once in deep CNNs?
    a. Multi-task learning
    b. Overfitting
    c. Regularization
    d. Backpropagation
    Answer: a. Multi-task learninggyansetu​

1. https://aimcqs.com/convolutional-neural-networks
2. https://www.scribd.com/document/851342076/Deep-Learning-117-MCQ
3. https://www.studocu.com/in/document/lovely-professional-university/deep-learning/mcq-practice-questions-on-cnns-and-autoencoders-cs101/126254331
4. https://www.geeksforgeeks.org/deep-learning/convolutional-neural-network-cnn-in-machine-learning/
5. https://www.gyansetu.in/blog/60-deep-learning-objective-type-quiz-mcqs-with-answers-for-interview-preparation/
6. https://psbtrainingacademy.com/applications-of-ai-mcqs/
7. https://aimcqs.com/fundamentals
8. https://www.aionlinecourse.com/ai-quiz-questions/deep-learning/convolutional-neural-networks-dl
9. https://www.sanfoundry.com/deep-learning-in-ai-neural-networks-cnns-rnns-transformers/
10. https://www.sanfoundry.com/1000-neural-networks-questions-answers/
11. https://www.scribd.com/document/488694104/Deep-Learning-MCQ
12. https://testbook.com/objective-questions/mcq-on-artificial-neural-network–5eea6a0e39140f30f369e522
13. 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
14. https://www.geeksforgeeks.org/quizzes/introduction-to-deep-learning-and-neural-networks-1/
15. https://github.com/amanchadha/coursera-deep-learning-specialization
16. https://itfeature.com/ds/ml/neural-network-quiz-5/