Introduction

Artificial Intelligence (AI) is the branch of Computer Science that is emerging nowadays, and it is the technology in demand by every top-notch company.

It is the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. This technology can rationalize and take steps that have the best chance of achieving a specific goal.

Machine learning and Deep learning are considered subsets of Artificial Intelligence.

The main goal of AI includes computer-enhanced learning, reasoning, and perception. It is being used in different ways, from Finance to Healthcare. That is why significant companies must complete their ongoing and future projects.

To prepare well for the interview related to AI, you must know from scratch. This article will provide you with all types of questions that interviewers ask during their recruitment process.

Basic Level Interview Questions

1. What do you know about Artificial Intelligence?
2. Explain the term AI with its advantages and disadvantages.
3. What are some functions for which we use AI?
4. What is the need for Artificial Intelligence?
5. Give some real-world applications of Artificial Intelligence.
6. What is the main difference between Artificial Intelligence, Machine learning, and Deep Learning?
7. What are the various types of AI?
8. Tell me about different domains of Artificial Intelligence.
9. What do you know about Supervised and Unsupervised Learning?
10. Explain Reinforcement Learning.
11. Explain the term “Q-Learning”?
12. Name the different programming languages that are used in Artificial Intelligence.
13. What is an agent in Artificial Intelligence?
14. Explain the uses of Agents in Artificial Intelligence.
15. Define the Markov Decision process.
16. Explain the concept of Reward Maximization.
17. Define parametric and non-parametric model?
18. What do you know about the term hyperparameter?
19. What is the difference between strong AI and weak AI?
20. Do you know about the Turing test in AI?
21. Define the term overfitting.
22. Name a technique to avoid overfitting in neural networks.
23. Explain NLP and its components.
24. Highlight the different components of the Expert System.
25. Explain the use of computer vision in AI.
26. Explain the minimax algorithm.
27. What is the importance of Game theory in AI?
28. Define eigenvalues and eigenvectors?
29. Name some of the commonly used Artificial Neural networks.
30. What is a chatbot?
31. How to avoid overfitting in Neural Networks?
32. Name some algorithms used for hyperparameter optimization.
33. Explain the working of the Face verification process in AI.
34. Can we solve logical inference in propositional logic?
35. Describe the lifetime of a variable.
36. Define intermediate tensors.
37. Elucidate the concept of the A* algorithm search method.
38. Describe the breadth-first search algorithm.
39. What do you know about the bidirectional search algorithm?
40. Explain the idea of an iterative deepening depth-first search algorithm.
41. Tell me something about the uniform cost search algorithm.
42. What is the relation between game theory and AI?
43. Define the term alpha-beta pruning.
44. Describe fuzzy logic.
45. List the various applications of fuzzy logic.
46. Explain partial order planning.
47. Explain FOPL.
48. Compare and contrast Inductive Machine Learning, Deductive Machine Learning, and Abductive Machine Learning.
49. Name some machine learning algorithms that you know.
50. Explain Naïve Bayes?

Advanced level Interview Questions

1. Define the Backpropagation algorithm.
2. How will you optimize the route weights by reducing the error in the model?
3. Define perception in machine learning.
4. List the various extraction techniques used for dimensionality reduction.
5. Is there any difference existing between KNN and K-means Clustering?
6. What do you understand by ensemble learning?
7. List the different steps involved in the process of Machine learning.
8. Define a Hash table.
9. What do you understand by regularization in Machine Learning?
10. What are the components involved in relational evaluation techniques?
11. What are model accuracy and model performance?
12. Explain the F1 score.
13. Name any three feature selection techniques in Machine Learning.
14. Explain a recommendation system.
15. What do you understand by the Bias-variance tradeoff?
16. Define the term TensorFlow.
17. How will you install TensorFlow?
18. What are the various TensorFlow objects?
19. Define a cost function.
20. Name different activation neurons or functions.
21. Explain vanishing gradient.
22. Define the term dropouts.
23. Explain the concept of Long short-term memory.
24. What are the various key components of LSTM?
25. What are different variants of RNN?
26. What is an autoencoder? What is its application?
27. Name the two components of a generative adversarial network(GAN).
28. How will deploy a GAN Model?
29. Define the term Heuristic in Artificial intelligence.
30. Explain informed search strategies Algorithm?
31. Highlight some differences between Robot Systems and AI Programs.
32. Do you know about Robotics?
33. List some of the benefits of expert systems.
34. What are the limitations of Expert systems?
35. Define a user Interface.
36. Explain the capabilities of Expert Systems.
37. Explain the architecture of Fuzzy Logic Systems.
38. Name the terminologies used in NLP.
39. What are the various difficulties faced in NLU?
40. Describe the Travelling Salesman Problem.
41. What is the reason behind utilizing the inference engine in AI?
42. How can AI be used to identify fraud?
43. List some common misunderstandings about AI.
44. Define Constraint Satisfaction Problems.
45. How does Bayesian Network relate to AI?
46. Define Automatic Programming.
47. What is the future of Artificial Intelligence?
48. Differentiate between Regression and Clustering.
49. What will be your approach to promoting your business using AI?
50. How are chatbots helpful in providing the best customer support to customers?