MCQs

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Overall MCQs for Reference

1. What is Deep Learning?
   a. A subset of Machine Learning
   b. A programming language
   c. A data visualization tool
   d. A type of database
   Answer: agyansetu​
2. Which of the following is a type of deep learning model?
   a. Decision Tree
   b. Convolutional Neural Network (CNN)
   c. KNN
   d. Naive Bayes
   Answer: bgyansetu​
3. What does an artificial neuron mimic?
   a. Processor
   b. Biological neuron
   c. SQL query
   d. Memory cell
   Answer: bgyansetu​
4. Which activation function is most commonly used in hidden layers?
   a. Sigmoid
   b. ReLU
   c. Softmax
   d. Tanh
   Answer: bgyansetu​
5. What is the main purpose of an activation function?
   a. Normalize input data
   b. Introduce non-linearity
   c. Train the model
   d. Reduce overfitting
   Answer: bgyansetu​
6. Which layer type extracts spatial features in images?
   a. Fully connected
   b. Dropout
   c. Convolutional
   d. Pooling
   Answer: cgyansetu​
7. Deep Learning models require:
   a. Less data
   b. High computational power
   c. Fewer computations
   d. No training
   Answer: bgyansetu​
8. Which framework is developed by Google for Deep Learning?
   a. PyTorch
   b. Theano
   c. TensorFlow
   d. Keras
   Answer: cgyansetu​
9. Which deep learning architecture is best for sequential data?
   a. CNN
   b. GAN
   c. RNN
   d. DBN
   Answer: conlineexammaker​
10. What is backpropagation in the context of deep learning?
    a. Forward pass method
    b. Adjusting weights based on prediction errors
    c. Preventing overfitting
    d. Feature selection
    Answer: bonlineexammaker​
11. Which activation function is used in binary classification output layers?
    a. Sigmoid
    b. ReLU
    c. Tanh
    d. Softmax
    Answer: aonlineexammaker​
12. What is the vanishing gradient problem?
    a. Gradients become too large
    b. Small learning rate
    c. Gradients approach zero, slow learning
    d. Parallelizing algorithms
    Answer: conlineexammaker​
13. What is the main advantage of transfer learning?
    a. Faster training
    b. Avoid labels
    c. Use pre-trained models for new tasks
    d. Simplify network
    Answer: conlineexammaker​
14. Data augmentation is used to:
    a. Add noise
    b. Increase model complexity
    c. Expand dataset with transformations
    d. Reduce bias
    Answer: conlineexammaker​
15. Which layer prevents co-adaptation of features?
    a. BatchNorm
    b. Dropout
    c. Flatten
    d. Conv
    Answer: bgyansetu​
16. Which function is often used in output layers for multi-class classification?
    a. ReLU
    b. Sigmoid
    c. Tanh
    d. Softmax
    Answer: dscribd​
17. What is the role of gradient descent?
    a. Minimize loss function
    b. Find optimal weights
    c. Update network parameters
    d. All of the above
    Answer: daimcqs​
18. What is a neuron’s output a function of?
    a. Just weights
    b. Input data
    c. Weighted sum and activation function
    d. Loss metric
    Answer: caimcqs​
19. Which optimizer adapts the learning rate for each parameter?
    a. SG
    b. Adam
    c. RMSProp
    d. Adagrad
    Answer: bscribd​
20. What’s the purpose of pooling in CNNs?
    a. Flatten layer
    b. Reduce spatial dimension
    c. Increase non-linearity
    d. Drop neurons
    Answer: bscribd​
21. Which neural network is best known for translation tasks?
    a. RNN
    b. CNN
    c. Seq2Seq with attention
    d. DBN
    Answer: cscribd​
22. Batch normalization helps in:
    a. Reducing vanishing gradients
    b. Regularization
    c. Both a and b
    d. Data labeling
    Answer: caimcqs​
23. What does an autoencoder do?
    a. Classifies images
    b. Reduces dimensionality
    c. Detects outliers
    d. All of the above
    Answer: bgyansetu​
24. Which is a model explanation technique?
    a. LIME
    b. SVM
    c. Sigmoid
    d. CNN
    Answer: agyansetu​
25. Dropout is best described as:
    a. Data cleaning
    b. Weight initialization
    c. Randomly disabling neurons
    d. Output activation
    Answer: caimcqs​
26. Reinforcement learning is best for:
    a. Unlabeled data
    b. Classification
    c. Delayed rewards
    d. Regression
    Answer: caimcqs​
27. Which is not a popular deep learning framework?
    a. TensorFlow
    b. PyTorch
    c. Keras
    d. Scikit-learn
    Answer: daimcqs​
28. The main loss function for classification in neural networks:
    a. Mean squared error
    b. Cross-entropy
    c. Absolute error
    d. Hinge loss
    Answer: bgyansetu​
29. A recurrent neural network (RNN) is suitable for:
    a. Image classification
    b. Text and sequence processing
    c. Unsupervised learning
    d. Dimensionality reduction
    Answer: bonlineexammaker​
30. Which of the following is not a type of layer in deep learning?
    a. Convolutional
    b. Pooling
    c. Dropout
    d. Cluster
    Answer: dscribd​
31. Which technique helps deep learning models learn multiple tasks?
    a. Clustering
    b. Multi-task learning
    c. Singular value decomposition
    d. Label encoding
    Answer: bgyansetu​
32. Which model is commonly used for object detection?
    a. Yolov3
    b. SVM
    c. LDA
    d. HMM
    Answer: ascribd​
33. Which function helps in multiclass probability outputs?
    a. Sigmoid
    b. Softmax
    c. ReLU
    d. Tanh
    Answer: bscribd​
34. CNNs are best suited for:
    a. Regression
    b. Image processing
    c. Anomaly detection
    d. Reinforcement learning
    Answer: bonlineexammaker​
35. Which method generates new and realistic data?
    a. GAN
    b. SVM
    c. RNN
    d. CNN
    Answer: ascribd​
36. The “learning rate” in deep learning:
    a. Data normalization
    b. Controls weight update size
    c. Controls neuron activation
    d. Feature selection
    Answer: bonlineexammaker​
37. What is Xavier initialization used for?
    a. Weight initialization
    b. Data normalization
    c. Loss computation
    d. Hyperparameter tuning
    Answer: ascribd​
38. Vanishing gradients often occur with:
    a. Linear activation
    b. Sigmoid/tanh activation
    c. ReLU activation
    d. Concatenation
    Answer: bscribd​
39. Which method is well-suited for unsupervised feature learning?
    a. CNN
    b. RNN
    c. Autoencoder
    d. GAN
    Answer: cscribd​
40. The basic unit of a deep learning network is:
    a. Filter
    b. Kernel
    c. Neuron
    d. Layer
    Answer: cscribd​
41. What does a higher number of epochs usually cause?
    a. Underfitting
    b. Overfitting risk
    c. Fewer gradients
    d. Data normalization
    Answer: bgyansetu​
42. Which deep learning technique is used for learning from unlabeled data?
    a. Supervised learning
    b. Reinforcement learning
    c. Unsupervised learning
    d. Transfer learning
    Answer: caimcqs​
43. A model that identifies objects in images is doing:
    a. Regression
    b. Object detection
    c. Clustering
    d. Feature reduction
    Answer: bscribd​
44. To handle exploding gradient, which method is recommended?
    a. Gradient clipping
    b. Early stopping
    c. Feature selection
    d. Pooling
    Answer: aonlineexammaker​
45. Which metric is commonly used for classification problems?
    a. Mean Absolute Error
    b. R-squared
    c. Accuracy
    d. Mean Squared Error
    Answer: caimcqs​
46. Which of the following is not a challenge in deep learning?
    a. Data scarcity
    b. Interpretability
    c. Overfitting
    d. Data normalization
    Answer: dgyansetu​
47. Which component is considered the “memory” in an LSTM network?
    a. Input gate
    b. Output gate
    c. Cell state
    d. Forget gate
    Answer: cscribd​
48. What is the advantage of using GPUs in deep learning?
    a. Data augmentation
    b. Incremental learning
    c. Parallel computation
    d. Feature scaling
    Answer: cgyansetu​
49. Which concept refers to pre-trained models applied to new problems?
    a. Active learning
    b. Transfer learning
    c. Feature learning
    d. Data mining
    Answer: bgyansetu​
50. GAN stands for:
    a. General Adversarial Network
    b. Graphical Attention Network
    c. Generative Adversarial Network
    d. General Autoencoder Network
    Answer: cgyansetu​

1. https://www.gyansetu.in/blog/60-deep-learning-objective-type-quiz-mcqs-with-answers-for-interview-preparation/
2. https://onlineexammaker.com/kb/30-deep-learning-quiz-questions-and-answers/
3. https://www.scribd.com/document/851342076/Deep-Learning-117-MCQ
4. https://aimcqs.com/fundamentals
5. https://www.scribd.com/document/488694104/Deep-Learning-MCQ
6. https://www.acmecollinsschool.com/deep-learning-mcqs-questions.html
7. https://interviewkickstart.com/blogs/articles/machine-learning-mcqs-answers
8. https://studyglance.in/objquestions/display.php?url=btech%2Fnndlunit1.txt&title=Neural+Networks+and+Deep+Learning-Unit-1+Objective+Type+Questions&subject=NNDL&furl=data%2Fbtech%2Fbtechobjquestions.xlsx
9. https://www.sanfoundry.com/1000-neural-networks-questions-answers/
10. https://www.sanfoundry.com/deep-learning-in-ai-neural-networks-cnns-rnns-transformers/
11. https://www.aionlinecourse.com/ai-quiz-questions/machine-learning/deep-learning-ml
12. https://www.gtbit.ac.in/assets/img/pdf/ADL%20Question%20bank.pdf
13. https://gmstat.com/data-science/dl/mcqs-deep-learning-1/
14. https://www.geeksforgeeks.org/quizzes/introduction-to-deep-learning-and-neural-networks-1/
15. https://www.youtube.com/watch?v=FugpqOEPuBg
16. https://www.igmguru.com/blog/machine-learning-mcqs
17. https://www.interviewbit.com/machine-learning-mcq/