60 Deep Learning Objective Type Quiz & MCQs with Answers for Interview Preparation

Practice these MCQs to strengthen your Deep Learning knowledge and prepare confidently for job interviews or certification exams.
Whether you’re aiming to break into AI roles or master neural network architectures as part of your career path — you’re in the right place.

60+ Deep Learning MCQs covering everything from perceptrons, CNNs, RNNs, activation functions, and backpropagation — complete with answers to help you test and solidify your understanding, no guesswork involved.

Section 1: Basics of Deep Learning

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: A

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: B

3. What does an artificial neuron mimic?
A. Processor
B. Biological neuron
C. SQL query
D. Memory cell
Answer: B

4. Which activation function is most commonly used in hidden layers?
A. Sigmoid
B. ReLU
C. Softmax
D. Tanh
Answer: B

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: B

6. Which layer type is used to extract spatial features in images?
A. Fully connected
B. Dropout
C. Convolutional
D. Pooling
Answer: C

7. Deep Learning models require:
A. Less data
B. High computational power
C. Fewer computations
D. No training
Answer: B

8. Which framework is developed by Google for Deep Learning?
A. PyTorch
B. Theano
C. TensorFlow
D. Keras
Answer: C

9.What does GPU stand for in Deep Learning context?
A. Graphical Programming Unit
B. General Purpose Unit
C. Graphics Processing Unit
D. Graph Processing Unit
Answer: C

10.Which function is usually used in the output layer for binary classification?
A. ReLU
B. Softmax
C. Sigmoid
D. Tanh
Answer: C

Section 2: Neural Networks

11.Which of the following is not a type of layer in a neural network?
A. Input
B. Hidden
C. Output
D. Recursive
Answer: D

12. What is a perceptron?
A. Type of loss function
B. Type of layer
C. Basic unit of a neural network
D. Training technique
Answer: C

13.Which algorithm is used to update weights in neural networks?
A. Naive Bayes
B. Gradient Descent
C. PCA
D. SVM
Answer: B

14.What is backpropagation used for?
A. Initializing weights
B. Forward pass
C. Error correction
D. Data augmentation
Answer: C

15.A feedforward neural network moves data:
A. In both directions
B. In a loop
C. Backward only
D. Forward only
Answer: D

16.Which network is suitable for sequential data?
A. CNN
B. RNN
C. GAN
D. DNN
Answer: B

17.The vanishing gradient problem mostly affects which network?
A. CNN
B. GAN
C. RNN
D. DNN
Answer: C

18.Which technique helps prevent overfitting in deep networks?
A. Gradient Descent
B. Dropout
C. One-hot encoding
D. Feature Scaling
Answer: B

19.Which function is used for multi-class classification?
A. Sigmoid
B. Softmax
C. Tanh
D. ReLU
Answer: B

20.Which type of RNN can retain longer memory?
A. GRU
B. LSTM
C. Vanilla RNN
D. BiRNN
Answer: B

Section 3: CNNs and Image Processing

21. What is the main role of pooling layers in CNNs?
A. Increase accuracy
B. Extract features
C. Reduce spatial dimensions
D. Normalize data
Answer: C

22. Which type of pooling is commonly used?
A. Average pooling
B. Max pooling
C. Min pooling
D. Random pooling
Answer: B

23. What does a convolution operation do in a CNN?
A. Compresses input
B. Applies a filter to extract features
C. Connects neurons
D. Backpropagates error
Answer: B

24. What is a filter/kernel in CNN?
A. Loss function
B. Matrix used to detect patterns
C. Activation function
D. Regularization method
Answer: B

25. Which of the following increases model complexity in CNN?
A. Pooling layers
B. Larger filters
C. Fewer layers
D. Smaller batch size
Answer: B

26. CNNs are best suited for which data type?
A. Text
B. Audio
C. Images
D. Tabular data
Answer: C

27. Which of the following is a famous CNN architecture?
A. BERT
B. LSTM
C. VGGNet
D. GPT
Answer: C

28. What does flattening do in CNNs?
A. Compresses data
B. Converts 2D data to 1D
C. Removes noise
D. Reduces overfitting
Answer: B

29.Stride in convolution operation controls:
A. Depth
B. Output accuracy
C. Movement of the filter
D. Loss value
Answer: C

30. Which is a disadvantage of CNNs?
A. Good for image recognition
B. Need large dataset
C. Efficient computation
D. Weight sharing
Answer: B

Section 4: RNNs and NLP

31.Which problem does LSTM solve better than RNN?
A. Gradient descent
B. Short-term memory
C. Vanishing gradient
D. Batch processing
Answer: C

32.What does NLP stand for?
A. Neural Layer Processing
B. Natural Language Processing
C. Node Level Processing
D. None of the above
Answer: B

33.Which model is primarily used in NLP tasks?
A. CNN
B. RNN
C. LSTM
D. Both B and C
Answer: D

34.Which of the following is not typically used in NLP?
A. Tokenization
B. Embedding
C. Pooling
D. Stemming
Answer: C

35.Word embeddings are used to:
A. Generate images
B. Train neural networks
C. Convert words to vectors
D. Segment sentences
Answer: C

36.Which of the following is a popular word embedding technique?
A. Word2Vec
B. CNN
C. MaxPooling
D. Adam
Answer: A

37.Sequence-to-sequence models are used in:
A. Image classification
B. Text generation
C. Speech recognition
D. Both B and C
Answer: D

38.The attention mechanism improves:
A. Memory of CNNs
B. Context understanding in sequences
C. Model compression
D. Hyperparameter tuning
Answer: B

39.Transformer models eliminate the need for:
A. Convolutions
B. Recurrence
C. Dropout
D. Pooling
Answer: B

40.Which of these is a transformer-based model?
A. ResNet
B. Word2Vec
C. BERT
D. VGG
Answer: C

Section 5: Training and Optimization

41.Which optimizer is commonly used in Deep Learning?
A. RMSProp
B. Adam
C. SGD
D. All of the above
Answer: D

42.Batch size refers to:
A. Size of the input image
B. Number of epochs
C. Number of samples processed before updating weights
D. Number of layers
Answer: C

43.Epoch refers to:
A. Number of layers
B. One full pass through training data
C. Loss function
D. Model complexity
Answer: B

44.Which loss function is used for binary classification?
A. Categorical cross-entropy
B. Hinge loss
C. Binary cross-entropy
D. MSE
Answer: C

45.Early stopping is a method to:
A. Train faster
B. Avoid underfitting
C. Prevent overfitting
D. Save memory
Answer: C

46.Which of these is a regularization technique?
A. Backpropagation
B. L2 penalty
C. Softmax
D. One-hot encoding
Answer: B

47.Learning rate controls:
A. Loss function
B. How fast model updates weights
C. Input data size
D. Training duration
Answer: B

48.Which metric is used for regression tasks?
A. Accuracy
B. Precision
C. Mean Squared Error (MSE)
D. AUC
Answer: C

49.Hyperparameter tuning involves:
A. Feature scaling
B. Adjusting model parameters manually
C. Data augmentation
D. Model evaluation
Answer: B

50.Which tool is used to visualize training metrics?
A. PyTorch
B. TensorBoard
C. Pandas
D. NumPy
Answer: B

Section 6: Advanced Topics and Applications

51.GAN stands for:
A. General Adversarial Network
B. Graphical Attention Network
C. Generative Adversarial Network
D. General Autoencoder Network
Answer: C

52.Autoencoders are used for:
A. Classification
B. Regression
C. Dimensionality reduction
D. Optimization
Answer: C

53.In GANs, the generator aims to:
A. Classify real data
B. Discriminate data
C. Fool the discriminator
D. Train CNNs
Answer: C

54.Which of these is a deep reinforcement learning algorithm?
A. DQN
B. CNN
C. LSTM
D. RNN
Answer: A

55.Which field uses deep learning for medical image analysis?
A. Radiology
B. Dermatology
C. Ophthalmology
D. All of the above
Answer: D

56.Which concept helps a model learn multiple tasks simultaneously?
A. Transfer learning
B. Fine-tuning
C. Multi-task learning
D. Clustering
Answer: C

57.Transfer learning helps when:
A. Training data is abundant
B. You have pre-trained models
C. Labels are missing
D. GPU is unavailable
Answer: B

58.Which layer is used to prevent co-adaptation of features?
A. BatchNorm
B. Dropout
C. Flatten
D. Conv
Answer: B

59.Which technique helps explain predictions in deep models?
A. LIME
B. ReLU
C. Sigmoid
D. Pooling
Answer: A

60.Which of the following is not a challenge in deep learning?
A. Data scarcity
B. Interpretability
C. Overfitting
D. Data normalization
Answer: D

Mastering these Deep Learning MCQs is a valuable step toward understanding neural networks, model optimization, and advanced AI applications.
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