Top Artificial Intelligence (AI) Interview Questions & Answers for 2025

Artificial Intelligence is at the very epicentre of all technological innovations, coming together as an apex union between human ingenuity and machine intelligence. Its applications resound in industries, bringing about a change in the way we exist and work or interact with technology. The global market for artificial intelligence grew to touch a remarkable value of USD 150.2 billion in 2023 and is expected to rise at an outstanding CAGR of 36.8% during the forecast period from 2023 to 2030. This incredible growth of valuation in the AI market gives evidence of the indispensable role that it will play in modern society.

While AI pushes industries to adopt its potential, AI increases the demand for skilled professionals in this domain. It’s not just a career path-it’s opening the doorway to shaping the future. One looks forward to finding employment in this dynamic field; therefore, arming oneself with answers to the most frequently asked Artificial Intelligence interview questions becomes quite important.

These AI interview questions can be your passport to myriad opportunities lying inside this ever-evolving landscape.

AI Interview Questions: Basic Concepts

1. What is TensorFlow, and how does it apply in AI?

TensorFlow is an open-source library platform developed by the Google Brain team. Basically, it’s a maths library used for various machine learning applications. Using tensor flow, we can easily train and deploy the machine learning model in the cloud.

2. What are the major types of AI?

These include reactive machines, limited memory, theory of mind, and self-aware AI. Each represents growth in terms of more complexity and capability, ranging from simple reaction-based machines to those that understand and develop consciousness.

3. How is machine learning different from traditional programming?

Traditional programming thus requires the explicit writing of codes to make logical results from input data. Machine learning algorithms learn patterns and make decisions by themselves, with very minimal interference by humans.

4. What are some of the most common use cases of AI within a business?

Here are some of the most common ways to use AI in the business world:

• Automation of Customer Service: Chatbots and virtual assistants are commonly used to take customer calls and deal with queries and issues.
• Predictive Analytics: AI can predict what will happen in the future based on analysis of trends, data and behaviours from previous years.
• Personalisation: Marketing messages, product recommendations, and content tailored according to user-specific views.
• Fraud Detection: Analysis of transaction patterns to identify frauds and prevent them.
• Supply Chain Optimisation: Logistics, inventory management, and route planning will be redesigned using AI algorithms.
• Human Resources: Automation in the processes of recruitment and scouting for the right candidates is possible with AI-driven tools.
• Sales Forecasting: AI predicts future sales to alter sales strategies accordingly.
• Maintenance Prediction: Predictive maintenance at manufacturing to detect any potential machinery failure.
• Sentiment Analysis: Based on the feedback of customers about the brand, commentaries on social media, etc.
• Content Creation: Writing content, images, or videos on marketing and/or others.
• Market Research: Automation in gathering data from the market and its analysis for decisions in the business world.
• Health and Safety Monitoring: This application involves AI-driven monitoring of the workplace environment regarding health and safety adherence.
• Financial Analysis: Automation in financial reports, analysis, investment, and assessment of risks.
• Quality Control Process: Image recognition technologies can be applied for quality control to find defects in products for quality assurance.
• Voice Recognition: It may be applied to voice-activated commands to conduct a variety of services and other internal business processes.

5. What is market-basket analysis?

Market-basket analysis is one of the common techniques used to find the association between items. Large retailers use this technique frequently in order to attain maximum profit. Here, we need to identify various item combinations that are most often bought together.

Because of this, for example, if someone buys bread, he will buy butter. Showing such a correlation will enable the retail business to grow by offering their customers things that are relevant to them.

6. What is CNN?

A CNN is a useful deep-learning technique, especially for processing input images. Using learnable weights and biases, the network grants a score for various features or objects in the image to select which one is more important. This gives the network substantial power to distinguish between them.

7. What is Natural Language Processing?

NLP is, therefore, part of artificial intelligence and computer science, which deals with the relationship between computers and human beings in processing natural languages. It is related to techniques in computer science that use mechanisms of processing and examining the human language in combination with linguistics and mathematics.

8. What are Generative Adversarial Networks, and how does it work?

The GAN consists of two neural networks that collaborate: a generator and a discriminator. The generated data should resemble the training data and get classified by the discriminator. Both components compete with each other to improve, thereby making GAN capable of producing highly realistic data.

9. Describe decision trees in Machine Learning.

Decision Trees represent a type of supervised learning used in classification and regression problems. They model decisions usually with the possible consequences that include a representation like trees, with choices as branches and results as leaves, intuitively used for the making of decisions.

10. What do you understand by the term “Q-Learning”?

The famous algorithm that works in reinforcement learning is Q-learning. Basically, it is based on the Bellman equation. In this algorithm, the agent tries to learn policies that can provide the best actions in order to maximise rewards under particular circumstances. The agent learns these optimal policies from past experiences.

Q in Q-learning is used to indicate the quality of actions to be performed in every state. The agent ultimately aims at maximising Q.

11. What is the Bayesian Network, and why is it so vital in AI?

Bayesian networks are the graphical models used to show the probabilistic relationship between a set of variables. This is a directed cycle graph with multiple edges, where each edge describes conditional dependency.

Bayesian networks are probabilistic because these networks are built from a probability distribution and use probability theory in prediction and anomaly detection. The Bayesian network provides one of the most important conceptions in AI, as it is based on Bayes’ theorem and Bayes’s formula, which can be used to deduce answers to probabilistic questions.

12. Explain the minimax algorithm along with the different terms.

The minimax algorithm is a backtracking algorithm for game theory to make decisions in game theory. This algorithm provides the optimal moves of a player, supposing that another player is also playing optimally.

This algorithm is based on two players: one is called MAX, and the other is called MIN.

Following are some terminologies used in the Minimax Algorithm:

• Game tree: A tree structure with all possible moves.
• Initial State: The starting position of the board.
• Terminal State: Position of the board where the game ends.
• Utility Function: The function that assigns a numeric value for the outcome of the game.

13. Explain Knowledge Representation in AI.

It is that part of AI that deals with thinking by AI agents. It is used to represent the knowledge about the real world to the AI agents so that they can understand it and utilise the information to solve the complex problems in AI.

Following are the elements of Knowledge which are represented by the agent in the AI system:

• Objects
• Events
• Performance
• Meta-Knowledge
• Facts
• Knowledge-base

14. What is the use of computer vision in AI?

Computer vision is the field of artificial intelligence that is used to train computers so that they can interpret and obtain information from the visual world, such as images. Hence, computer vision uses AI technology to solve complex problems such as image processing, object detection, etc.

15. Why A* algorithm is important for AI?

The A* algorithm has become very important in AI because it is an efficient and effective method by which paths are found either in graph traversing or through pathfinding. This algorithm executes its functions through the use of heuristics for an approximate determination of the cost required to reach the goal from each node in order to optimise its search process for the shortest path.

16. Explain the Hidden Markov model.

The hidden Markov model is a statistical model that represents probability distributions over a chain of observations. A Hidden Markov Model is one that assumes that the state of a process generated at a particular time is hidden from the observer, and it assumes it satisfies the Markov property. The hidden Markov model finds most of its applications in temporal data.

17. What are parametric and non-parametric models?

In machine learning, there are two main types of models: parametric and non-parametric. Here, parameters are the predictor variables that are used in building the machine learning model. The explanation of the models is given below.

Parametric Model: Unlike a non-parametric model, the parametric model uses only one fixed number of parameters for developing an ML model. It makes strong assumptions about data. Examples of parametric models are Linear regression, Logistic Regression, Naïve Bayes, Perceptron, etc.

Non-Parametric Model: Non-parametric model utilises flexible numbers of parameters. In other words, the model considers a few assumptions about the data. These models are good for higher data and no prior knowledge. Examples of non-parametric models include Decision Tree, K-Nearest Neighbour, and SVM with Gaussian kernels, amongst others.

18. What do you understand by the hyper-parameter?

In machine learning, hyper-parameters are those that determine and control the complete training process. The parameters related to this are hidden layers, hidden units, activation functions, and so on. They are those parameters that are external to the model. The choice of good hyper-parameters makes a better algorithm.

19. What are the challenges in Natural Language Processing?

The problems to address are context processing, sarcasm, idiomatic expressions, ambiguity of words, and precision to produce exactly the same meaning in various languages and dialects. This complexity depends on whether robust models can accurately capture the meanings of or synthesise human language.

AI Interview Questions: Ethics, Bias, and Society

20. How can you ensure that your AI models are ethical and unbiased?

Ensuring that AI models operate within the boundaries of ethics and have no bias employs rigid testing across a wide variety of diverse datasets, continuous monitoring for bias, ethics embedded into the AI development process, and model decisions being transparent.

21. What are the most common ethical issues with AI?

Ethical issues include concerns about privacy, job loss to automation, accountability of decision-making processes, bias in artificial intelligence, and lastly, misuse of artificial intelligence.

22. What are some misconceptions about AI?

There have been several misconceptions related to artificial intelligence since the start of its evolution. Some of them are given below:

• AI does not require humans: The first misconception about AI is that it does not require humans. But in reality, each AI-based system is somewhere dependent on humans and will remain. Such as, it requires human-gathered data to learn about the data.
• AI is dangerous for humans: it is not inherently dangerous for humans, and it still has not reached Super AI or Strong AI, which is more intelligent than human beings. No powerful technology can be harmful if not misused.
• AI has reached its peak stage: Yet, we stand very far from the peak time of AI. Its peak time will take a long time to arrive.
• AI will take your job: This is one of the biggest misconceptions that AI will take most of our jobs. But actually, it is giving us a lot of new opportunities for employment.
• AI is a new technology: Though some people think this is a new technology, this technology was first thought of in 1840 in an English newspaper.

23. What are the ethical considerations in AI?

The ethical consideration involves fairness, transparency, privacy, and accountability of the AI systems. In relation to ethical deployment, AI technologies have to ensure that bias is avoided, user consent is respected, and the societal implications of automated decisions are envisaged.

24. How might AI influence our society?

It would be quite influential when AI improves performances in all areas of life, opens up new opportunities to innovate, improves health outcomes, and, at the same time, can become a source of increasing social inequalities or job losses in some cases.

25. What does bias in machine learning mean, and why is it so important?

Bias in machine learning is the error that results from the model due to over-simplification, assumptions, or prejudices in training data. It is important because it may result in some inaccuracies in prediction or decision-making and especially hurt any considerations for fairness or ethics.

AI in Practice: Top Artificial Intelligence Interview Questions

26. How can AI be used in cybersecurity?

AI in cybersecurity automates complicated processes of cyber threat detection and response, analyses huge volumes of data for threat detection, and gives predictions about where vulnerabilities may occur.

Also Read: Cyber Security Interview Questions

27. How can AI be applied in the health sector?

The various ways in which AI is being developed and used in the healthcare sector include diagnostic algorithms, personalised medicine, patient monitoring, and operational efficiencies. It can help in the analysis of complex medical data and improve diagnosis accuracy, thereby optimising treatments for predictive outcomes, which greatly improves health care.

28. How is AI used in an autonomous vehicle?

AI in intelligent vehicles encompasses perception, decision, and navigation. It processes sensor data to understand the environment, predicts the behaviour of other participants in traffic, and makes decisions in real time about how to navigate in a way that is safe and efficient.

29. Explain AI model explainability and highlight its importance.

AI model explainability is the ability to understand and explain the decisions taken by the AI model. The importance of this feature is that it brings transparency and builds trust, helping models make decisions for a reason.

30. How would you approach the solution of a new problem using AI?

The solution of a new problem using AI includes problem domain comprehension, data collection and pre-processing, model and algorithm selection, model training, and iterative improvements with performance metrics.

31. What purpose does data preprocessing serve in Machine Learning?

The preprocessing of data involves cleaning, normalising, and structuring raw data into a form that could be usable in machine learning algorithms. It enhances the accuracy of any given model, ensuring that the inputted data is consistent and relevant while filtering out the noise and irrelevant information.

AI Interview Questions: Advanced AI Concepts

32. What do you understand by the reward maximisation?

Reward maximisation is the terminology used in reinforcement learning, and it is the objective of the reinforcement learning agent. The reward is positive feedback by action for a transition from one state to another. In the case of taking an optimal policy, applying a good action provides a reward to the agent, and in the case of a bad action, one reward is subtracted. The goal of the agent is to maximise these rewards by applying optimal policies, which is termed reward maximisation.

33. Explain the concept of transfer learning and its advantages.

It just involves transferring a pre-trained model that was trained on a very large dataset to apply to another similar problem but of a smaller scale. This reduces training time and data requirements, therefore bringing about better performance of the model, especially for tasks where limited data exists.

34. Distinguish between symbolic and connectionist AI.

While symbolic AI or rule-based AI works on explicitly defined rules and logic for decision-making, connectionist AI learns through neural networks from patterns in data. While symbolic AI shines on clear-cut, well-defined tasks, connectionist AI does much better on pattern recognition or predictive tasks.

35. What are eigenvalues and eigenvectors?

The two major concepts of Linear algebra are Eigenvectors and eigenvalues.

Eigenvectors are unit vectors that possess a magnitude equal to 1.0.

Eigenvalues are the coefficients applied to the eigenvectors, or these are the magnitude by which the eigenvector is scaled.

36. What are feature vectors in the context of Machine Learning?

Feature vectors in machine learning represent objects with n-dimensional vectors of numerical features. Each dimension in a vector forms a feature relevant to an object for the algorithms to analyse or predict. They form the bedrock that the models need to comprehend patterns or classifications in the data.

Artificial Intelligence Interview Questions Related to Overfitting and Model Optimisation

37. What is Overfitting?

When an AI model is too complicated and just fits the training data too closely. It would start to memorise the outcome of training without knowing the real structure, patterns, and relations. That means such a model will yield good results on the training data but poor results on new, previously unseen data.

This generally applies to all machine learning algorithms, which, when the learned model becomes too complex considering the number and quality of training data, might fall into the case of overfitting. Sometimes, it even starts fitting the noise of the data rather than the true pattern. That degrades the performance and accuracy when used for the prediction or classification of new data.

The regularisation, cross-validation, and early stopping techniques during training avoid overfitting. All these help in the simplification of a complex model and, therefore, can generalise well on new unseen data.

38. What Techniques Are Used to Avoid Overfitting?

Cross-validation: One of these methods is cross-validation, where there are a number of folds the data could be divided into and then trained and tested on different folds. This prevents overfitting, which is essentially a model memorising the training data and performing generalisation poorly on new data.

Regularisation: This is one of the techniques of including a penalty term in the model’s objective function. It assists in ensuring the model doesn’t over-depend on a single feature. This will also prevent the model from fitting the noise present in the training data.

Early stopping: This technique involves stopping the training before the model’s performance starts to degrade on the training data. This is useful when one trains the model with several iterations.

Ensemble methods: Typically, these involve training more models and then combining their predictions for a single prediction. This helps reduce the variance and, hence, increases the robustness of the model.

Pruning: It’s a technique in which the complexity of any model is reduced by the removal of unimportant features or nodes.

Dropout: It is a technique that drops out random subsets of neurons during network training. This prevents the network from relying too heavily on any one neuron.

Bayesian approaches: In these approaches, prior information is combined and updated with new information using Bayes’ theorem in model parameters.

39. Bias-variance tradeoff: Explain the concept.

Bias-variance tradeoff basically tries to find that optimum level where the error due to bias balances the error due to variance. High bias and high variance result in problems of underfitting or overfitting, which in turn affect the performance of the model.

40. What are Some Differences Between Classification and Regression?

Classification and regression represent two types of supervised machine learning used to predict the output variable based on one or more input variables.

By classifying data into groups, classification is utilised to predict discrete responses. Regression analysis is used to anticipate numerical values and predict continuous responses.

Classification is a kind of supervised learning wherein the aim is to predict any given input with a categorical label or class. The output needs to be discrete and finite, such as “spam” or “not spam” in an email classification problem. Thus, in this case, the input data is labelled by a class, while the model learns to predict the class based on features.

Regression is a type of supervised learning whereby, for an input provided, it tries to predict a continuous value. The output will be real value – the price of a house or temperature. Input data are labelled with a continuous value, and the model learns from them in order to predict values according to features in the input.

AI Interview Questions: Game Theory and Rationality

41. What is Game Theory?

Game theory is an academic study of strategic decision making; it involves the study of the possible actions of players in a situation where the outcome for one depends not just on one’s own actions but also on the actions of others. Game theory is viewed as a mathematical methodology for analysing situations of conflict or cooperation between intelligent and rational decision makers. As a tool for analysing real life, game theory is applied in such diverse areas as auctions, bargaining, and the evolution of social norms.

42. How is Game Theory significant in AI?

Game theory is a logical and scientific study that forms a model of the possible interactions between two or more players. Here, rational implies that each player thinks others are equally rational and equally informed. In game theory, players deal with the given set of options in a multi-agent situation; it means the choice of one player affects the choice of the other or opponent players.

Game theory and AI are closely related; game theory is useful to AI and vice versa. In AI, game theory is being applied to broaden the ability to enable some of the key functionalities that are definitely required in this multi-agent environment where multiple agents try to interact with each other to achieve certain goals.

Different popular games, such as poker and chess, are considered to be logical games with certain specified rules. So, to play such games online or digitally, like on Mobile, laptop, etc., one has to create algorithms for such games. These algorithms are applied with the help of artificial intelligence.

43. Define rational agents and rationality.

An agent is said to be rational if it possesses well-defined preferences, has model uncertainty, and always executes the best action. In other words, an agent can never fail to take the best possible action in any given situation.

Rationality simply refers to the status of being reasonable and sensible with good sound judgement.

44. What is the Turing Test, and what importance does it have?

The Turing Test provides the basis for ascertaining the ability of a machine to display intelligent behaviour indistinguishable from human intelligence. Its importance is derived from the benchmark it sets for measuring the capability of systems in carrying out objectives concerned with intelligence similar to those of human beings.

Artificial Intelligence Interview Questions: Miscellaneous AI Concepts

45. List the different components of the Expert System.

An expert system mainly consists of three components:

• User Interface: It interacts between a user and an expert system to find the solution to a problem.
• Inference Engine: It is considered the main processing unit or brain of the expert system. It applies different inference rules to the knowledge base so that a conclusion can be drawn from it. The system extracts the information from the KB with the help of an Inference engine.
• Knowledge Base: The knowledge base is a type of storage area that stores domain-specific and high-quality knowledge.

46. Give a brief introduction of partial, alternate, artificial, and compound keys.

Partial Keys: The set of attributes that differentiates the weak entities within the same owner entity

Alternate Keys: All candidate keys other than the primary key are called alternate keys

Compound Key: It contains more than one field that allows the user to identify any particular record distinctly.

47. What is an Artificial neural network? Name some commonly used Artificial Neural networks.

Artificial neural networks are a statistical model inspired by functioning human brain cells called neurons. These neural networks include various AI technologies, such as deep learning and machine learning.

An Artificial neural network or ANN consists of multiple layers such as the Input layer, Output Layer, and hidden layers.

ANN, through several deep learning techniques, is one of the AI tools that can solve various complex problems like pattern recognition, facial recognition, and so on.

Some commonly used Artificial neural networks:

• Feedforward Neural Network
• Convolutional Neural Network
• Recurrent Neural Network
• Autoencoders

48. How would you assess the performance of any AI model?

The measure of the performance for classification problems may be based on accuracy, precision, recall, F1 score, and AUC-ROC, while for regression problems, it is mostly MSE or MAE. These different measures tell us how the model will predict or classify new data.

49. What are the limitations of AI today?

Current limitations of AI are that it lacks an understanding of context and common sense, is bound by rather high demands on data, could be possibly biassed in training, there are ethical concerns, and explanation of the decisions made by AI is a big challenge. Since this is an ongoing process, comprehensive research and development will be required for some time to come.

50. Since AI is one of the fastest-developing fields, how will you manage to update yourself in this field?

To be up-to-date about AI, the necessary things to do are: taking courses, continuously learning, attending conferences, reading research articles or papers, being part of an AI community, and practising all AI technologies.