1. What is the primary goal of computer vision in AI?
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To generate synthetic images
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To enable machines to interpret and understand visual data
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To replace all human vision capabilities
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To enhance audio recognition models
2. Which of the following is a common application of computer vision?
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Speech recognition
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Optical Character Recognition (OCR)
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Text summarization
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Machine translation
3. What is the purpose of image segmentation in computer vision?
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To split an image into meaningful parts for analysis
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To blur an image for artistic effects
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To reduce the size of an image
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To convert an image into text
4. Which algorithm is commonly used for face detection?
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R-CNN
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YOLO (You Only Look Once)
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NaΓ―ve Bayes
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K-Means Clustering
5. What type of neural network is most commonly used in computer vision tasks?
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Recurrent Neural Network (RNN)
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Generative Adversarial Network (GAN)
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Bayesian Network
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Convolutional Neural Network (CNN)
6. Which dataset is widely used for training image classification models?
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MNIST
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IMDB Reviews
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ImageNet
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COCO
7. What is an activation function commonly used in deep learning for image processing?
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Softmax
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ReLU (Rectified Linear Unit)
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Sigmoid
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Tanh
8. What is the main advantage of YOLO over R-CNN?
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YOLO is significantly faster in object detection
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YOLO provides higher accuracy in text recognition
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YOLO does not require a trained model
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YOLO works only on grayscale images
9. What technique is used to artificially increase the size of a dataset in image recognition?
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Dropout
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Data Augmentation
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Batch Normalization
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Gradient Descent
10. What is Transfer Learning in computer vision?
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Using pre-trained models to solve new tasks with minimal training
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Learning without labeled data
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A method of compressing image data
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A way to improve audio processing models
11. What is the role of edge detection in image processing?
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To detect the boundaries of objects within an image
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To enhance image resolution
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To change the color scheme of an image
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To compress image data
12. Which of the following is NOT an image classification architecture?
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ResNet
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VGGNet
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LeNet
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Word2Vec
13. What does COCO dataset stand for?
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Common Objects in Context
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Computer Object Classification and Optimization
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Convolutional Object Categorization Output
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Complex Overlapping Computer Objects
14. What is the main challenge in object detection?
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Recognizing multiple objects in different scales and orientations
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Understanding spoken language
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Translating text to different languages
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Storing high-resolution images
15. What type of problem does Optical Character Recognition (OCR) solve?
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Speech-to-text conversion
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Identifying text within images
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Image compression
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Face recognition
16. Which of these architectures is commonly used for real-time object detection?
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Fast R-CNN
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YOLO
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AlexNet
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GPT-3
17. What is the term for generating images using AI models?
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Image Synthesis
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Optical Recognition
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Image Compression
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Image Clustering
18. How does CNN differ from traditional neural networks for images?
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CNN has no hidden layers
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CNN does not require training data
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CNN is used only for text processing
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CNN uses convolutional layers to extract spatial features
19. What is the function of Batch Normalization in deep learning?
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It speeds up training and stabilizes the learning process
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It reduces the number of parameters in a model
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It increases the dataset size
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It converts an image into text
20. Which deep learning framework is commonly used for computer vision?
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TensorFlow
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PyTorch
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OpenCV
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All of the above
21. What is the main advantage of ResNet architecture?
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It solves the vanishing gradient problem using residual connections
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It reduces model training time to zero
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It does not use convolutional layers
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It is only used for NLP
22. What is a bounding box in object detection?
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A file format for storing images
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A method of encrypting images
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A rectangular box that encloses an object in an image
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A way to remove background noise
23. What is a heatmap in computer vision?
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A visual representation of areas of interest in an image
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A temperature measurement tool
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A method of increasing image brightness
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A way to classify grayscale images
24. Which of these is a loss function commonly used in image classification?
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Mean Squared Error
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Cross-Entropy Loss
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BLEU Score
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Jaccard Index
25. What is the purpose of feature extraction in image recognition?
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To remove noise from an image
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To change the resolution of an image
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To store images more efficiently
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To identify and represent important patterns in an image
26. What is the role of a fully connected layer in a CNN?
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To apply convolution operations to an image
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To reduce the size of an image
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To map extracted features to the final output classification
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To perform edge detection
27. What is one drawback of deep learning models in image recognition?
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They require large amounts of labeled data for training
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They cannot process grayscale images
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They always misclassify objects
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They do not support real-time processing
28. Which of these is an advanced architecture for handling sequential image data?
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LSTM (Long Short-Term Memory)
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CNN (Convolutional Neural Network)
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GAN (Generative Adversarial Network)
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R-CNN (Region-Based Convolutional Neural Network)
29. What does the term "overfitting" mean in computer vision models?
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The model performs better on new data than on training data
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The model memorizes training data but fails to generalize well to new images
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The model does not learn any patterns
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The model ignores high-resolution images
30. What is a key function of GANs in computer vision?
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To classify images into categories
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To detect objects in real-time
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To perform edge detection
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To generate realistic synthetic images