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Artificial Intelligence MCQs with Answers

Practice important Artificial Intelligence MCQs with answers and explanations.

Multiple Choice Questions

Q1251: What is the goal of training in neural networks?
  • A: To find the best hypothesis fitting most training examples
  • B: To memorize training data
  • C: To generate random outputs
  • D: To increase network size
View Answer
A

Q1252: Which function is commonly used to measure total error during neural network training?
  • A: Mean Absolute Error
  • B: Cross Entropy
  • C: Mean Squared Error
  • D: Root Mean Squared Error
View Answer
C

Q1253: During training, what method helps avoid overfitting by dividing training data into k sets?
  • A: Validation
  • B: Clustering
  • C: Randomization
  • D: Weight updation
View Answer
A

Q1254: What is a common stopping criterion in ANN training?
  • A: Minimum Mean Squared Error threshold
  • B: Maximum number of epochs
  • C: Both A and B
  • D: None of these
View Answer
C

Q1255: A network that correctly generalizes input-output relationships for unseen data is said to have good ________.
  • A: Validation
  • B: Training
  • C: Generalization
  • D: Clustering
View Answer
C

Q1256: Which algorithm randomizes weights to small positive and negative values initially?
  • A: Gradient Descent
  • B: Back Propagation
  • C: K-means Clustering
  • D: Decision Tree
View Answer
B

Q1257: In backpropagation, the error is the difference between the desired output and the ________.
  • A: Input vector
  • B: Network output
  • C: Weight vector
  • D: Learning rate
View Answer
B

Q1258: In unsupervised learning, what is the "Best Matching Unit" (BMU)?
  • A: The node closest to the input vector
  • B: The node with the highest weight
  • C: The node with the lowest error
  • D: The node with the smallest radius
View Answer
A

Q1259: Which type of neural network is used for clustering and function approximation?
  • A: Feedforward
  • B: Convolutional
  • C: Self-organizing
  • D: Recurrent
View Answer
C

Q1260: Fill in the blank: The process of adjusting weights in a neural network during training is called ________.
  • A: Activation
  • B: Weight updation
  • C: Validation
  • D: Initialization
View Answer
B

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