Posted on : 03 Nov, 2020, 02:23:53 PM

Top 50 Artificial Intelligence Interview Questions & Answers

Top 50 Artificial Intelligence Interview Questions & Answers

  • Ever since the term Artificial intelligence has become a new buzz word in this technology-driven era, the need and demand for AI engineers are felt across all IT sectors. 

In this blog, we will look at the top 50 questions with their answers related to AI, which are often asked during the interview.  

So read on,

1. Define Artificial intelligence?

  • Artificial intelligence is a branch of computer science, which emphasizes creating an intelligent machine that can imitate human behavior in terms of thinking, decision making. With AI, we do not need to pre-program the machine to perform a task; instead, we can design a machine with the programmed algorithms, and it can perform on its own, without any human intervention.


2. Why do we need artificial intelligence?

Artificial intelligence is a need of the hour, as the world is moving towards automation; we need AI to solve complex problems, make our routine life more convenient by automating tasks, save man-power, and to perform various other tasks.


3 Give some common uses and applications of AI?


  • Here, your answer should talk about the common yet relevant applications of AI, which can reflect your knowledge about this field. There are various real-world applications of AI and some of them are given below:


  • Google search engine- When we start to search for something on Google, it gives us suggestions relatable to our query and that is because of different AI technologies.
  • Ridesharing applications- Different ridesharing applications use AI and machine learning to determine the type of ride, show the estimated time, traffic on various routes and price of the ride, etc.
  • Product recommendations- when we search for a specific product on Amazon, Flipkart, we get similar product recommendations and this is because of different ML algorithms. Similarly, on Netflix, we get personalized recommendations for movies and web series.


4. Explain the difference between AI, machine learning, and Deep learning differ from each other?

Artificial intelligence Machine learning Deep learning

Artificial intelligence originated around the 1950s and was first coined in the year 1956 by john McCarthy.

Machine learning originated around the late 1950s and was first coined in the year 1959 by Arthur Samuel.

Deep learning originated around the 1970s and was coined in the year 2000 by Igor Aizenberg.

It is a subset of Data Science, that enables us to create intelligent human machines that can imitate human behavior.

It is a subset of AI that learns from past data and experience.

It is a subset of AI and ML, inspired by the human brain cells, called neurons, and imitates the working of the human brain.

AI deals with structured and semi-structured data.

ML deals with structured and semi-structured data.

Deep learning deals with structured and semi-structured data.

It requires a huge amount of data to work

It requires less data as compared with AI and deep learning.

It requires a huge amount as compared to the ML

The aim is to make machines to think like humans

ML aims to enable machines to learn from past experiences

Deep learning aims to solve complex problems using various algorithms





















5. Explain the types of AI?

  • Artificial intelligence can be divided into different types based on capabilities and functionalities.

 Based on capabilities

  • Narrow AI- General Purpose AI, capable of performing some dedicated tasks with intelligence. For example; Siri.
  • General AI- Strong intelligent AI machines, capable of performing any intellectual task with efficiency as a human.
  • Strong AI- AI that possesses the ability to do everything that a human can do and more.


Based with functionalities

  • Reactive machines- They are the basic types of AI, based on present actions, it does not store the previous experiences, to form current decisions and simultaneously update their memory.
  • Limited memory- It can store the data or experience for a limited duration. Self-driving cars are one such example.
  • Theory of mind- An advanced type of AI, capable of understanding emotions, thinking, etc, in the real world.
  • Self-aware AI- AIs that possess human-like consciousness and reactions. Such machines can form self-driven actions.


6. What are the different domains/ subsets of AI?

AI is an umbrella term, covers lots of domains and some main are given below:

  • Machine learning
  • Deep learning
  • Neural Network
  • Expert system
  • Fuzzy logic
  • Natural language processing
  • Robotics
  • Speech recognition


7. How is Machine learning related to AI?

Machine learning is a subset of AI. It is a way of achieving AI. AI focuses on creating intelligent machines, whereas ML is a science of getting computers to act by feeding them data and letting them learn a few tricks on their own, without being explicitly programmed to do so.

Thus, machine learning is a practical implementation of AI.


8. Explain the different types of machine learning?

Machine learning can be explained in three different parts:

  • Supervised learning- Supervised learning is a type of machine learning, in which a machine needs external supervision to learn from data. These models are trained using the labeled dataset. Two main problems, Regression, and classification can be solved with supervised machine learning.
  • Unsupervised learning- It is a type of ML, which does not need any external supervision to learn from the data. Hence called unsupervised learning. The unsupervised models can be trained using the unlabelled dataset. Problems like; association and clustering can be solved.
  • Reinforcement learning- In RL, an agent interacts with the environment by producing actions, learn with the help of feedback. The feedback is given in the form of positive or negative rewards, without any supervision provided to the agent. Q-learning algorithm is used in reinforcement learning.


9. Explain the term “Q- learning”.

Q-learning is a popular algorithm used in reinforcement learning in which agents learn from an optimal policy from its past experiences with the environment. The past experiences of an agent are a sequence of state- action rewards.


10. What is Deep learning? And give relevant applications of the real-world?

Deep learning is a subset of machine learning, which imitates the working of the human brain. It uses human brain cells, called neurons, and works on the concept of neural networks to solved complex problems. It is also known as the deep neural network or learning.

 Some real-world applications of deep learning are:

  1. Computer vision
  2. Text generation
  3. Deep- learning robots, etc.


11. What is an artificial neural network?

An artificial neural network is a statistical model influenced by the functioning of human brain cells called neurons. It includes various AI technologies like deep learning and machine learning.


12. Name some commonly used artificial neural networks?

  • Feedforward neural network
  • Convolution Neural network
  • Recurrent 
  • Neural Network- Long short term Memory
  • Autoencoders


13. Which programming language is used for AI?

There are top five programming languages that are widely used for Artificial intelligence:

  • Python
  • Java
  • Lisp
  • R
  • Prolog

Among the top five, Python is used widely for AI development due to its easy to use approach and availability of various libraries such as NumPy, Pandas, etc.


14. Which programming language is not used in AI and why?

Per programming language is not used in AI, due to its scripting language


15. Explain the assessment that is used to test the intelligence of a machine?

In artificial intelligence (AI), A turning test is used as a method of inquiry to determine whether a computer is capable to think like a human being or not.


16. Explain Intelligent Agents in AI, and where are they used?

The intelligent agent can be any autonomous entity that explores its environment through the sensors and act accordingly using the actuators for achieving its goal.

 These intelligent agents in AI are used in various applications:

  • Information access and navigations such as Search engine
  • Repetitive Activities
  • Domain experts
  • Chat boats, etc.


17. What is Markov’s decision process?

The solution to a reinforcement learning problem is using the Markov decision process or MDP. Hence, MDP is used to formalize the RL problem. In other words, it is a mathematical approach to solve a reinforcement learning problem. The main aim is to achieve maximum rewards by choosing the right optimal policy.

It has four elements, which are:

  • A set of finite states S
  • A set of infinite actions A
  • Rewards
  • Policy Pa


18. What do you understand by reward maximization?

The term reward maximization is used in reinforcement learning and which is also a goal of reinforcement agents. In RL, a reward is a positive feedback, which is given during a transition process from one state to another. If the agent performs a good action by applying optimal policies, he gets a reward, and if he performs bad action, one reward will be subtracted. In short, the agent's main aim is to maximize these rewards by applying optimal policies, which is termed as reward maximization.


19. What are parametric and non- parametric models?

In machine learning, there are two types of models, parametric and non-parametric.

The explanation is given below:

  • Parametric Model- Parametric model uses a fixed number of parameters to create the ML model. It considers strong assumptions about the data. It is computationally faster and requires fewer data. Common examples include; Logistic regression Naïve Bayes models
  • Non- parametric model- Non-parametric model uses a flexible number of parameters. It considers a few assumptions about the data. These models are computationally slower and require more data. Common examples include KNN, Decision tree models.


20. What is the difference between model parameters and hyperparameters?

Model Parameters


Model parameters are the features of training data that will learn automatically

Model hyperparameters are the parameters that determine the entire training process.

For example, weights and biases,

Split points in the decision tree

For example, the learning rate

Hidden layers

Hidden units

They are internal to the model and their value is based upon data. 

They are external and their value cannot be estimated from data. 













21. What are hypermeters in deep neural network learning?

Hypermeters are variables used to define a network. They are used to define several hidden layers that must be present in a network, more hidden layers ensure the safety of the network, whereas lesser units make it less safe.


22. Explain the different algorithms used for hyperparameter optimization?

  • Grid search

Grid search is used to train the network for every combination by using the two set of hyperparameters, Learning rate, and numbers of layers.

  • Random search

It randomly samples the search space and evaluates sets from a particular probability distribution.

  • Bayesian optimization

BO includes fine-tuning the hyperparameters by enabling automated model tuning. The model used for approximating the objective function is called the surrogate model (Gussian process). It uses the GP function to get posterior functions to make predictions based on prior functions.


23. Explain the hidden Markov model?

The Hidden Markov model is a statistical model in which the system being modeled is assumed to be a Markov process with unobserved (i.e. Hidden) states. The HMM models are mostly used for temporal data and are used in various applications such as reinforcement learning, temporal pattern recognition, etc.


24. What is overfitting? How can it be overcome in machine learning?

Overfitting is one of the main issues in machine learning, when the ML algorithm tries to capture all the data points, it captures noise also, and then overfitting occurs in the model. Due to this overfitting issue, the algorithm shows a low bias, but a high variance in the output.

Methods to avoid overfitting in ML:

  • Cross-validation
  • Training with more data
  • Regularization
  • Ensembling
  • Removing Unnecessary features
  • Early stopping the training


25. Name one technique to avoid overfitting in neural networks?

Dropout Technique- The dropout technique is one of the trustworthy techniques to avoid overfitting in the neural network models.


26. Define NLP? And explain its various components of NLP?

NLP stands for Natural language processing, is a branch of Artificial intelligence. It empowers machines to understand, interpret, and manipulate the human language.

Components of NLP

NLP is mainly divided into two components, which are given below:

  • Natural language understanding (NLU):

It involves the below tasks:

  1. To map the input to useful representation.
  2. To analyze the different aspects of the language.
  • Natural language Generation (NLG)
  1. Text planning
  2. Sentence planning
  3. Text realization


27. What are the different components of the expert system?

An expert system mainly contains three components:

  1. User interface- It enables a user to interact or communicate with the expert system to find a solution for a problem.
  2. Inference Engine- It is known as the main processing unit of the brain system. It applies different inference rules to the knowledge base to conclude it.


28. Explain the use of computer vision in AI?

Computer vision is a part of AI and is used to train the computers so that they can interpret and obtain information from the visual world such as images. Hence,

It uses AI technology to solve complex problems such as image processing, object detections, etc.


29. Explain the minimax algorithm along with different terms

Minimax algorithm is a recursive algorithm used for decision making in game theory. It selects an optimal move for a player assuming that the other player is also playing optimally. This algorithm works on two players, one is called MAX, and the other is called MIN.

  Following terminologies are used in the Minimax Algorithm:

  • Game tree- A tree structure with all possible moves.
  • Initial State- The initial state of the board.
  • Terminal state- position of the board where the game finishes
  • Utility function- the function that assigns a numeric value for the outcome of the game.


30. Explain Game theory? And how is it important in AI?

Game theory is the logical and scientific study that forms the framework of the possible interactions between two or more rational players. Here, rational means that each player believes that others are just as rational and have similar knowledge and skills.

In-game theory, players are provided a given set of options in a multi-agent situation, where the choice of one player affects the choice of the opponent players.

Game theory and AI are related and important to each other. In AI, the game theory is widely used to enable some of the key responsibilities required in the multi-agent environment, in which multiple agents try to interact with each other to achieve a goal. For example- poker, chess, etc. are logical games with specified rules.


31. What are some common misconceptions related to AI?

AI is surrounded by lots of misconceptions since its inception. Some of these misconceptions are given below:

  • Absence of Human Presence - The most common misconception is AI doesn’t require humans. But that, not the case, it needs human support and requires human gathered data to learn about the data.
  • Dangerous for Humans- AI is looked at as inherently dangerous for humans, but it is not because no technology can be harmful if it’s not misused.
  • Snatch Away our Jobs- It is also one of the biggest misconceptions that AI will take our jobs, but in reality, it is responsible for creating more employment opportunities.


32. What are the eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are the two main components of linear algebra.

  • Eigenvectors are unit vectors with a magnitude equal to 1.0.
  • Eigenvalues are the coefficients that are applied to the eigenvectors, or magnitudes by which the eigenvector is scaled.


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

  • Partial Keys- A set of attributes that uniquely identifies weak entities, related to the same owner
  • Alternate Keys- all candidate keys are known as alternate keys, except the primary key.
  • Compound Key- A set of multi fields that helps a user to recognize a specific record.
  • Artificial Keys- It is the extra attribute added to the table, in the absence of stands alone or compounds key.


34. What is a Chat Boat?

A chat boat is Artificial intelligence software designed to simulate a conversation with the end-users using natural language processing. These are also known as digital assistants, ready to answer any query 24/7, via an application, or app, or even website.


35. What is knowledge representation in AI?

Knowledge representation is a part of AI, concerned with the thinking of AI agents. It is used for AI agents to make them aware of the real world, so, that they can gain an understanding of solving complex problems in AI.


36. Name the elements of knowledge provided to the AI agent?

Following are the elements of knowledge that are provided to the AI agent;

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


37.What are the various techniques of knowledge representation in AI?

Following are the Knowledge representation techniques:

  • Logical
  • Semantic Network Representation
  • Frame Representation
  • Production Rules


38. Explain reinforcement learning?

Reinforcement learning is a type of machine learning, in which an agent explores its environment by producing actions, and learns, with the help of feedback. The feedback is based on performance and earned in the form of positive and negative rewards. There is no labeled data or supervision is provided to the agent.

Following are the popular learning algorithms;

  • Q-learning
  • SARSA(State action reward state action)
  • Deep Q neural Network


39. How does RL(Reinforcement Learning) work?

Generally, reinforcement learning is composed of these components:

  • Agent- the Agent is the AI program that is sensors and actuators to explore the environment
  • An environment- An environment is the surrounding of the agent, a space to explore and act upon.
  • State- it is the situation that is returned by the environment to the agent.
  • Reward- the feedback provided to the agent after doing each action.


40. Name some areas where AI has created an impact?

Following are some areas where AI has a great impact;

  • Autonomous transportation
  • Education-facilities powered by AI
  • Healthcare facilities
  • Predictive policing
  • Space exploration
  • Entertainment, etc.


41. Name some different software platforms for AI development?

  • Google Cloud AI
  • Microsoft azure
  • IBM Watson
  • Tensor flow
  • Infosys Nia
  • Rainbird
  • Dialog flow


42. Explain some ways to evaluate the performance of the ML model?

Following are some popular ways to evaluate the performance of the ML model:

  • Confusion Matrix- It is an N*N table with different sets of values, used to determine the performance of the classification model in ML.
  • F1 score- It is the harmonic mean of precision and recall, used as one of the best metrics to evaluate the ML model
  • Gain and Lift charts- it is used to determine the rank ordering of the probabilities.
  • AUC-ROC curve- the AUC-ROC is another performance metric, and is the plot between the sensitivity.
  • Gini coefficient- It is used in the classification problems, also known as the Gini index. The high value represents a good model.


43. Explain rational agents and rationality?

A rational agent is one with clear preferences, model uncertainty, and performs the right action always. A rational agent is capable to take the best action in any situation.

Rationality is a status of being reasonable and sensible with a good sense of judgment.


44. What is tensor flow, how it is used in AI?

Tensor Flow is the open-source library platform initially developed by the Goggle brain team for use in machine learning and neural network research. It is a math library used for several machine learning applications. With tensor flow, we can easily train and deploy the machine learning models in the cloud.


45. Why image recognition a key function in AI?

Humans are visual and AI is designed to imitate human brains. Therefore, teaching machines to recognize and categorize images is a crucial part of AI.


46. What is automatic planning?

Automatic planning means is describing what a program should do, and then implementing the AI system ‘write’ the program.


47. Explain Bayesian Network? And how it is related to AI?

A Bayesian network is a graphical model for probabilistic relationships amidst a set of variables. It mimics the human brain in processing variables.


48. Explain how AI is used in fraud detection?

Artificial intelligence is used in fraud detection problems by applying Machine learning algorithms for detecting anomalies and hidden patterns in data.


49. What are Constant satisfaction problems?

Constraint satisfaction problems are mathematical problems defined as a set of objects, the state of which must meet several constraints. CSPs are useful for AI because the regularity of their formulation offers similarity for analyzing and solving problems.


50. What is the inference engine, and why it is used in AI?

In AI, the inference engine is the part of an intelligent system that derives new information from the knowledge base by implementing some logical rules.

 It broadly works in two modes:

  • Backward Chaining- it begins with a goal and proceeds backward to deduce the facts that support the goal.
  • Forward Chaining- It begins with new facts, and asserts new facts.


With this, we conclude this blog. We hope these top 50 questions related to Artificial intelligence will prove beneficial to crack the interview. Check out our Artificial Intelligence & Data Science Certification Courses.

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