Artificial Intelligence Interview Questions
Artificial Intelligence Interview Questions
Basic Level
1. What is Artificial Intelligence (AI)?
AI simulates human intelligence processes by machines, particularly computer systems. These processes include learning, reasoning, and self-correction.
2. What are the main goals of AI?
The primary goal is to create systems that can learn, reason, perceive, and interact with the environment similarly to humans.
3. What is machine learning?
Machine learning is a subset of AI that allows systems to learn and improve from experience without being explicitly programmed automatically.
4. How is AI different from machine learning?
AI is the broader concept of machines being able to carry out tasks in a way that we would consider “smart.” Machine learning is a current application of AI based on the idea that we should give machines access to data and let them learn for themselves.
5. What is a neural network?
A neural network is a series of algorithms that endeavors to recognize underlying relationships in a data set through a process that mimics how the human brain operates.
6. Define supervised learning.
Supervised learning is a type of machine learning where the model is trained on a labeled dataset, meaning that each training example is paired with an output label.
7. What is unsupervised learning?
Unsupervised learning involves training models on neither classified nor labeled data, allowing the algorithm to act on that data without guidance.
8. What is reinforcement learning?
Reinforcement learning is an area of machine learning where an agent learns to make decisions by performing specific actions and observing the rewards or penalties resulting from those actions.
9. What are the typical applications of AI?
Typical applications include natural language processing, speech recognition, robotics, autonomous vehicles, and game playing.
10. Explain the term 'overfitting' in machine learning.
Overfitting occurs when a machine learning model learns the training data too well, including the noise and outliers, thus performing poorly on new, unseen data.
11. What is the Turing Test?
The Turing Test, proposed by Alan Turing, is a method of AI inquiry for determining whether a computer can exhibit human-like intelligence.
12. What is natural language processing (NLP)?
NLP is a field of Artificial Intelligence that allows machines to read, understand, and derive meaning from human languages.
13. What is deep learning?
Deep learning is a subset of machine learning involving neural networks with three or more layers, enabling the model to learn from large amounts of data.
14. What is a chatbot?
A chatbot is an AI application that simulates a human conversation through voice commands, text chats, or both.
15. Define the term 'algorithm' in the context of AI.
In AI, an algorithm is a set of rules that a machine follows to learn from data and to make decisions or predictions.
16. What is computer vision?
Computer vision is a field of AI that trains computers to interpret and understand the visual world using digital images and deep learning models.
17. What is an expert system?
An expert system is a computer system that mimics the decision-making abilities of a human expert in a particular field.
18. What is the difference between AI and robotics?
AI is the intelligence behind decision-making, while robotics involves building robots that can perform physical tasks autonomously or semi-autonomously.
19. Explain 'feature extraction' in machine learning.
Feature extraction involves reducing the resources required to describe a large data set. It is a crucial step in the pre-processing of data in machine learning.
20. What is a decision tree?
A decision tree is a flowchart-like structure used in machine learning and data mining to make decisions based on input data.
21. What is a 'dataset'?
A dataset is a data collection that a machine learning model is trained on or tested against.
22. Define 'clustering' in machine learning.
Clustering is a type of unsupervised learning where data points are grouped such that objects in the same group are more similar than those in other groups.
23. What are neural network layers?
Layers in a neural network are a collection of nodes that work together in a specific manner to process input data and generate outputs.
24. What is a 'training set'?
A training set is a dataset that trains a machine-learning model to understand and learn from the input data.
25. What are 'parameters' in a machine learning model?
Parameters are the variables in a model that are learned from the data and are used to make predictions or decisions.
26. What is a 'loss function'?
A loss function measures the difference between the predicted and actual values in a dataset, guiding the optimization of the model.
27. What is a 'model' in machine learning?
A model in machine learning is a mathematical representation of a real-world process used to make predictions or decisions based on input data.
28. What is an 'epoch' in deep learning?
An epoch in deep learning is a single pass through the entire training dataset during the training of a neural network.
29. What is 'backpropagation'?
Backpropagation is a training algorithm for neural networks that adjusts the network weights to minimize the loss function.
30. What is 'gradient descent'?
Gradient descent is an optimization algorithm used to minimize the loss function by iteratively moving towards the steepest descent of the function.
31. What are 'hyperparameters'?
Hyperparameters are the configurations external to the model and cannot be directly learned from the regular training process but are crucial for the model's performance.
32. What is a 'perceptron'?
A perceptron is the simplest type of artificial neuron, serving as the building block for more complex neural networks.
33. What is 'data augmentation'?
Data augmentation involves creating additional training data from the existing data using various transformations to prevent overfitting and improve the model's generalization.
34. What is 'transfer learning'?
Transfer learning is a technique in machine learning where a pre-trained model is reused on a new problem by fine-tuning it to adapt to the latest data.
35. What is a 'confusion matrix'?
A confusion matrix is a table used to evaluate the performance of a classification model by summarizing the correct and incorrect predictions.
36. What is the role of Artificial Intelligence (AI) in the development and functioning of Generative AI models?
Artificial Intelligence (AI) is the backbone of Generative AI, enabling it to create new content by learning patterns from existing data. Through advanced AI techniques like neural networks, Generative AI models can produce contextually relevant and often realistic outputs, such as text, images, and music.
Artificial Intelligence Interview Questions
Intermediate Level
37. What is the difference between batch and online learning?
Batch learning trains the model using the entire dataset simultaneously, while online learning updates the model incrementally as new data comes in.
38. Explain the concept of 'regularization' in machine learning.
Regularization involves techniques to prevent overfitting by adding a penalty term to the loss function, reducing the complexity of the model.
39. What is the 'bias-variance tradeoff'?
The bias-variance tradeoff is the balance between the model's ability to minimize bias (error from incorrect assumptions) and variance (error from sensitivity to small fluctuations in the training set).
40. What are 'ensemble methods'?
Ensemble methods combine multiple machine learning models to improve prediction accuracy by reducing variance and bias.
41. What is 'bagging' in ensemble learning?
Bagging, or Bootstrap Aggregating, is an ensemble method that improves the stability and accuracy of machine learning algorithms by training each model in the ensemble on a random subset of the data.
42. What is 'boosting' in ensemble learning?
Boosting is an ensemble technique that sequentially trains models to focus on the errors of previous models, improving the overall model performance.
43. What are convolutional neural networks (CNNs)?
CNNs are a class of deep learning networks particularly effective in processing data with a grid-like topology, such as images, by using convolutional layers to extract features.
44. What are recurrent neural networks (RNNs)?
RNNs are a class of neural networks designed for processing sequential data by maintaining a 'memory' of previous inputs, making them suitable for tasks like language modeling and time series prediction.
45. What is 'dropout' in neural networks?
Dropout is a regularization technique were randomly selected neurons are ignored during training, preventing overfitting by introducing noise into the learning process.
46. Explain the term 'epoch' in the context of training neural networks.
An epoch in neural network training is a single iteration through the entire training dataset used to update the model's weights.
47. What is 'cross-validation'?
Cross-validation is a technique for evaluating the generalizability of a model by partitioning the data into training and validation sets multiple times, ensuring the model's performance is consistent.
48. What is a 'learning rate'?
The learning rate is a hyperparameter that determines the step size at each iteration while moving toward a minimum of the loss function in training a model.
49. Explain the difference between 'parametric' and 'non-parametric' models.
Parametric models have a fixed number of parameters and make assumptions about the data distribution. In contrast, non-parametric models do not assume a fixed distribution and can adapt to the data's complexity.
50. What is a 'support vector machine' (SVM)?
An SVM is a supervised learning model used for classification and regression tasks, which finds the best hyperplane that separates classes in the feature space.
51. What is the purpose of 'kernel trick' in SVM
The kernel trick enables SVMs to perform efficiently in high-dimensional spaces by using kernel functions to transform the input data without explicitly computing the coordinates in that space.
52. What is 'principal component analysis' (PCA)?
PCA is a dimensionality reduction technique that transforms the original variables into a new set of uncorrelated variables, called principal components, ordered by the amount of variance they capture.
53. What is 'Q-learning' in reinforcement learning?
Q-learning is a model-free reinforcement learning algorithm that learns the value of an action in a particular state by using the Bellman equation to update Q-values.
54. What is the 'Markov Decision Process' (MDP)?
An MDP is a mathematical framework for modeling decision-making situations where outcomes are partly random and partly under the control of a decision-maker.
55. What are 'word embeddings'?
Word embeddings are vector representations of words in a continuous vector space, capturing semantic meanings and relationships between words.
56. What is 'LSTM' in neural networks?
Long Short-Term Memory (LSTM) is a type of RNN architecture that can capture long-term dependencies by using unique gating mechanisms to control the flow of information.
57. What is the 'gradient vanishing problem'?
The gradient vanishing problem occurs in deep networks when gradients used to update the weights become very small, leading to slow or stalled learning in earlier layers.
58. What is 'Bayesian inference'?
Bayesian inference is a method of statistical inference that updates the probability estimate for a hypothesis as additional evidence is acquired.
59. What is the 'curse of dimensionality'?
The curse of dimensionality refers to the challenges and difficulties that arise when analyzing and organizing data in high-dimensional spaces, where the volume of the space increases exponentially.
60. What is 'overparameterization' in deep learning?
Overparameterization occurs when a deep learning model has more parameters than necessary, which can lead to overfitting and reduced generalization.
61. What is a 'GAN' (Generative Adversarial Network)?
A GAN is a class of deep learning models where two neural networks, a generator and a discriminator, are trained simultaneously to generate new data samples that resemble the training data.
62. What is 'data normalization'?
Data normalization is scaling individual data samples to have a mean of zero and a standard deviation of one to improve the performance and stability of machine learning models.
63. What is 'feature scaling'?
Feature scaling is normalizing the range of independent variables or features in a dataset to ensure that each feature contributes equally to the distance calculations used in machine learning algorithms.
64. What is 'Ridge Regression'?
Ridge Regression is a technique used to analyze multiple regression data that suffer from multicollinearity by adding a degree of bias to the regression estimates.
65. What is 'Lasso Regression'?
Lasso Regression is a type of linear regression that uses shrinkage, where data values are shrunk towards a central point, like the mean, to prevent overfitting.
66. Explain 'AdaBoost' in ensemble learning.
AdaBoost, or Adaptive Boosting, is an ensemble learning technique that combines weak learners into strong learners by reweighing the training samples based on the errors of the previous learners.
67. What is 't-SNE'?
t-SNE (t-distributed Stochastic Neighbor Embedding) is a machine learning algorithm for dimensionality reduction that is particularly well suited for visualizing high-dimensional datasets.
68. What is 'Reinforcement Learning'?
Reinforcement Learning is a type of machine learning where an agent learns to behave in an environment by performing actions and observing the results, optimizing for a reward signal.
69. What is a 'policy gradient' in reinforcement learning?
Policy gradient methods are a class of algorithms in reinforcement learning that optimize the policy directly by computing the gradient of the expected reward concerning the policy parameters.
70. What are the practical applications of artificial intelligence in prompt engineering, and how can AI enhance the efficiency and effectiveness of prompt generation?'?
Artificial intelligence in prompt engineering is used to automatically generate, refine, and optimize prompts for various tasks, such as natural language processing, content creation, and customer support. AI enhances efficiency by quickly producing relevant prompts, improving accuracy, and personalizing responses based on context and user needs.
Artificial Intelligence Interview Questions
Advanced Level
71. What is a 'Bayesian Network'?
A Bayesian Network is a probabilistic graphical model representing a set of variables and their conditional dependencies using a directed acyclic graph.
72. Explain the concept of 'attention mechanism' in deep learning.
Attention mechanisms allow neural networks to focus on different input parts sequentially, improving the model's performance on tasks like machine translation and image captioning.
73. What is 'Transformer architecture' in neural networks?
Transformer architecture is a deep learning model architecture that uses self-attention mechanisms to weigh the influence of different input parts, excelling at sequence-to-sequence tasks like language translation.
74. What is 'Autoencoder'?
An autoencoder is a neural network that learns efficient data representations unsupervised by encoding the input into a lower-dimensional space and then decoding it back to reconstruct the input.
75. What is a 'variational autoencoder' (VAE)?
A VAE is a type of autoencoder that uses a probabilistic approach to encode the input into a latent space, allowing for generating new, similar data by sampling from the latent distribution.
76. What is 'meta-learning'?
Meta-learning, or "learning to learn," is a machine learning subfield where models learn new tasks quickly by leveraging prior knowledge from similar tasks.
77. What is 'Federated Learning'?
Federated Learning is a distributed machine learning approach where models are trained across multiple devices or servers without sharing the raw data, preserving data privacy.
78. Explain 'Graph Neural Networks' (GNNs).
GNNs are a neural network that directly operates on graphs, learning to represent nodes, edges, and graph-level properties. They are helpful for tasks like social network analysis and molecular modeling.
79. What is 'Reinforcement Learning with Function Approximation'?
Reinforcement Learning with Function Approximation uses function approximators like neural networks to estimate the value of functions or policies, enabling the agent to handle high-dimensional or continuous state spaces.
80. What is a 'Knowledge Graph'?
A knowledge graph is a graph-based data structure that stores interlinked descriptions of entities, including their properties and relationships, used in semantic search and AI applications.
81. What is 'Capsule Network'?
A Capsule Network is a type of neural network that tries to model hierarchical relationships in data by organizing neurons into "capsules," which can capture more information about the spatial and pose relationships in data.
82. Explain 'Neural Architecture Search' (NAS).
NAS is a process of automating the design of neural network architectures, often using reinforcement learning or evolutionary algorithms, to find optimal architectures for specific tasks.
83. What is 'Causal Inference' in AI?
Causal Inference involves understanding the causal relationships between variables instead of merely identifying correlations to make predictions about the effects of interventions.
84. What is 'Generative Pre-trained Transformer' (GPT)?
GPT is a transformer-based neural network model trained on a large corpus of text unsupervised to generate human-like text and perform various language tasks.
85. What is 'Active Learning'?
Active Learning is a machine learning approach where the model actively queries the user or a data source to label new data points with the desired outputs, optimizing the learning process.
86. Explain 'Monte Carlo Tree Search' (MCTS).
MCTS is a heuristic search algorithm used in decision processes and game-playing, exploring possible moves by building a search tree and using random sampling to evaluate the potential of each move.
87. What is 'Explainable AI' (XAI)?
XAI refers to techniques and models that provide clear and interpretable explanations of how an AI system makes decisions, enhancing transparency and trustworthiness.
88. What is 'Catastrophic Forgetting' in neural networks?
Catastrophic Forgetting occurs when a neural network forgets previously learned information upon learning new information, particularly in continuous learning scenarios.
89. What is 'Few-Shot Learning'?
Few-shot learning is a machine learning approach where models are trained to learn new tasks from a few training examples, addressing the challenges of data scarcity.
90. What is 'Quantum Machine Learning'?
Quantum Machine Learning is a subfield of AI that explores the integration of quantum computing with machine learning algorithms to improve computational efficiency and solve complex problems.
91. What is 'Adversarial Training'?
Adversarial Training is a technique to improve the robustness of machine learning models by training them on adversarial examples, which are intentionally modified inputs designed to deceive the model.
92. What is 'Meta-Reasoning'?
Meta-reasoning is the ability of an AI system to reason about its reasoning processes, optimizing its cognitive strategies and decision-making capabilities.
93. Explain 'Deep Reinforcement Learning' (DRL).
DRL combines deep learning with reinforcement learning, using neural networks as function approximators to enable agents to learn and perform complex tasks in high-dimensional environments.
94. What is 'Bayesian Optimization'?
Bayesian Optimization is a strategy for optimizing expensive-to-evaluate functions using a probabilistic model to make informed decisions about where to sample next.
95. What is 'Hyperparameter Tuning'?
Hyperparameter Tuning selects the best set of hyperparameters for a machine learning model, typically involving techniques like grid search, random search, or Bayesian optimization.
96. What is 'Multi-Agent Reinforcement Learning'?
Multi-agent reinforcement Learning is a branch of reinforcement learning where multiple agents interact and learn in a shared environment, optimizing their collective performance or competing against each other.
97. What is 'Contrastive Learning'?
Contrastive Learning is a technique in machine learning where the model learns to differentiate between similar and dissimilar data points, often used in representation learning.
98. Explain 'Robustness in Machine Learning Models'.
Robustness in machine learning models refers to their ability to maintain performance when faced with variations or perturbations in the input data, such as noise or adversarial attacks.
99. What is 'Curriculum Learning' in AI
Curriculum Learning is a training strategy where a model is first trained on more manageable tasks and then gradually exposed to more complex tasks, mimicking the human learning process.
100. What is the role of Artificial Intelligence in MLOps
AI in MLOps automates and optimizes model management, including training, deployment, and monitoring. It enhances efficiency by enabling continuous model updates and issue resolution. AI-driven tools streamline collaboration and ensure models remain effective and accurate in production