providing a list of the 80 most common “artificial intelligence” interview questions would be impractical and it would depend on the specific role and company. However, I can provide you with some general questions that could be asked in an AI interview:

  1. Can you explain the difference between supervised and unsupervised learning?
  2. What is the purpose of a neural network?
  3. What is the difference between deep learning and machine learning?
  4. Can you explain the concept of overfitting in a model?
  5. What is the difference between a generative and discriminative model?
  6. How do you handle missing data in a dataset?
  7. Can you explain the concept of gradient descent?
  8. How do you evaluate the performance of a machine learning model?
  9. Can you explain the concept of regularization in a model?
  10. How do you handle a class imbalance in a dataset?
  11. Can you explain the concept of entropy in information theory?
  12. How do you handle categorical variables in a dataset?
  13. Can you explain the concept of reinforcement learning?
  14. What is the difference between L1 and L2 regularization?
  15. Can you explain the concept of Bayesian statistics?
  16. How do you prevent a model from becoming biased?
  17. Can you explain the concept of a decision tree?
  18. How do you handle time series data in a dataset?
  19. Can you explain the concept of support vector machines?
  20. How do you select features for a machine learning model?
  21. Can you explain the concept of natural language processing (NLP)?
  22. How do you handle text data in a dataset?
  23. Can you explain the concept of a hidden Markov model (HMM)?
  24. How do you evaluate the performance of an NLP model?
  25. Can you explain the concept of sentiment analysis?
  26. How do you handle missing data in a time series dataset?
  27. Can you explain the concept of a recurrent neural network (RNN)?
  28. How do you handle outliers in a dataset?
  29. Can you explain the concept of a convolutional neural network (CNN)?
  30. How do you handle multicollinearity in a dataset?
  31. Can you explain the concept of a generative adversarial network (GAN)?
  32. How do you handle imbalanced classes in a dataset?
  33. Can you explain the concept of a transformer model?
  34. How do you select a model for a specific problem?
  35. Can you explain the concept of an autoencoder?
  36. How do you handle missing data in a categorical variable?
  37. Can you explain the concept of a long short-term memory (LSTM) network?
  38. How do you handle high-dimensional data?
  39. Can you explain the concept of a self-organizing map (SOM)?
  40. How do you handle non-stationary time series data?
  41. Can you explain the concept of a Hopfield network?
  42. How do you handle missing data in a continuous variable?
  43. Can you explain the concept of a Boltzmann machine?
  44. How do you handle categorical variables with multiple levels?
  45. Can you explain the concept of a Restricted Boltzmann machine (RBM)?
  46. How do you handle categorical variables with high cardinality?
  47. Can you explain the concept of a deep belief network (DBN)?
  48. How do you handle correlated variables in a dataset?
  49. Can you explain the concept of a stacked autoencoder?
  50. How do you handle high-dimensional categorical variables?
  51. Can you explain the concept of transfer learning?
  52. How do you handle imbalanced classes in a time series dataset?
  53. Can you explain the concept of a capsule network?
  54. How do you handle missing data in a text dataset?
  55. Can you explain the concept of a variational autoencoder (VAE)?
  56. How do you handle categorical variables with missing levels?
  57. Can you explain the concept of an attention mechanism?
  58. How do you handle categorical variables with rare levels?
  59. Can you explain the concept of a normalizing flow?
  60. How do you handle categorical variables with high dimensionality?
  61. Can you explain the concept of adversarial training?
  62. How do you handle categorical variables with multiple rare levels?
  63. Can you explain the concept of meta-learning?
  64. How do you handle categorical variables with multiple high-dimensional levels?
  65. Can you explain the concept of one-shot learning?
  66. How do you handle categorical variables with multiple rare high-dimensional levels?
  67. Can you explain the concept of zero-shot learning?
  68. How do you handle categorical variables with multiple missing levels?
  69. Can you explain the concept of lifelong learning?
  70. How do you handle categorical variables with multiple rare missing levels?
  71. Can you explain the concept of few-shot learning?
  72. How do you handle categorical variables with multiple high-dimensional missing levels?
  73. Can you explain the concept of meta-learning?
  74. How do you handle high-dimensional categorical variables with missing levels?
  75. Can you explain the concept of adversarial examples?
  76. How do you handle high-dimensional categorical variables with rare levels?
  77. Can you explain the concept of the interpretability of AI models?
  78. How do you handle high-dimensional categorical variables with multiple levels?
  79. Can you explain the concept of an explainable AI (XAI)?
  80. How do you handle high-dimensional categorical variables with missing levels?

 

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