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Top 20 AI Interview Questions With Answers

Hello guys, if you are preparing for AI developer interview and looking for AI Interview questions and answers then you have come to the right place. Earlier, I have shared Python Interview Questions and Machine Learning Interview Questions and in this article, I am going to share 20 common Artificial Intelligence Interview Questions with answers for 1 to 2 years experienced professionals. If you have worked in the field of AI then you most likely know the answers of all of these questions but if you cannot answer then you can always check these best AI Courses to learn and improve your AI fundamental concepts. 

Do you need questions that will open your eyes as far as AI interview is concerned? Say no more because you are in the right place at the right time. Those questions that you need in order to adequately prepare for your interview are right here. 

All your concerns are now sorted and after going through this article you will be more than ready. Take a look at the following top 20 AI interview questions with answers.


20 Artificial Intelligence Interview Questions and Answers

Without wasting any more of your time, here is a list of 20 common AI Interview Questions with answers. These questions covers essential AI concepts and you can quickly revise them by going through these questions. 


1. What are the different types of AI?
Answer:
  • Limited Memory AI
  • Self Aware AI
  • Reactive Machines AI
  • Theory of Mind AI
  • Artificial Superhuman Intelligence (ASI)
  • Artificial Narrow Intelligence (ANI)
  • Artificial General Intelligence (AGI)

2. What are the different domains of Artificial Intelligence?
Answer:
  • Neural Networks
  • Machine Learning
  • Expert Systems
  • Natural Language Processing
  • Robotics
  • Fuzzy Logic Systems

3. What is Artificial Intelligence?
Answer: Artificial Intelligence is basically computer science technology which emphasizes the creation of intelligent machine that is able to mimic human behavior.

Top 20 AI Interview Questions With Answers
 


4. What are the real-world applications of AI?
Answer:
  • Ridesharing Applications
  • Spam Filters in Email
  • Google Search Engine
  • Social Networking
  • Product Recommendations

5. Explain the different algorithms used for hyperparameter optimization.
Answer:
  • Grid Search- Grid search trains the network for every combination by using the two set of hyperparameters, learning rate and the number of layers. Then evaluates the model by using Cross Validation techniques.
  • Random Search- It randomly samples the search space and evaluates sets from a particular probability distribution. For example, instead of checking all 10,000 samples, randomly selected 100 parameters can be checked.
  • Bayesian Optimization- This includes fine-tuning the hyperparameters by enabling automated model tuning. The model used for approximating the objective function is called surrogate model (Gaussian Process). Bayesian Optimization uses Gaussian Process (GP) function to get posterior functions to make predictions based on prior functions.
6. What are the different components of the Expert System?
Answer: An expert system mainly contains three components:
  • User Interface: It enables a user to interact or communicate with the expert system to find the solution for a problem.
  • Inference Engine: It is called the main processing unit or brain of the expert system. It applies different inference rules to the knowledge base to draw a conclusion 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 the domain-specific and high-quality knowledge.

7. What is a Chatbot?

Answer: A chatbot is Artificial intelligence software or agent that can simulate a conversation with humans or users using Natural language processing. The conversation can be achieved through an application, website, or messaging apps. These chatbots are also called as the digital assistants and can interact with humans in the form of text or through voice.

8. What is knowledge representation in AI?

Answer: Knowledge representation is the part of AI, which is concerned with the thinking of AI agents. It is used to represent the knowledge about the real world to the AI agents so that they can understand and utilize this information for solving the complex problems in AI. The following are elements of Knowledge that are represented to the agent in the AI system:

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

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

Answer: Knowledge representation techniques are given below:

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

10. How can AI be used in fraud detection?

Answer: The artificial intelligence can be broadly helpful in fraud detection using different machine learning algorithms, such as supervised and unsupervised learning algorithms. The rule-based algorithms of Machine learning helps to analyze the patterns for any transaction and block the fraudulent transactions. Below are the steps used in fraud detection using machine learning:

  • Data extraction: The first step is data extraction. Data is gathered through a survey or with the help of web scraping tools. The data collection depends on the type of model, and we want to create. It generally includes the transaction details, personal details, shopping, etc.
  • Data Cleaning: The irrelevant or redundant data is removed in this step. The inconsistency present in the data may lead to wrong predictions.
  • Data exploration & analysis: This is one of the most crucial steps in which we need to find out the relation between different predictor variables.
  • Building Models: Now, the final step is to build the model using different machine learning algorithms depending on the business requirement. Such as Regression or classification.

11. What is a market-basket analysis?

Answer: The market-basket analysis is a popular technique to find the associations between the items. It is frequently used by big retailers in order to get maximum profit. In this approach, we need to find combinations of items that are frequently bought together.

12. What does the language of FOPL consists of?

Answer:

  • A set of constant symbols
  • A set of variables
  • A set of predicate symbols
  • A set of function symbols
  • The logical connective
  • The Universal Quantifier and Existential Qualifier
  • A special binary relation of equality

13. Which algorithm is used for solving temporal probabilistic reasoning?

Answer: To solve temporal probabilistic reasoning, HMM (Hidden Markov Model) is used, independent of transition and sensor model.

14. Which algorithm inverts a complete resolution strategy?

Answer: ‘Inverse Resolution’ inverts a complete resolution, as it is a complete algorithm for learning first order theories.

15. How can logical inference be solved in Propositional Logic?

Answer: In Propositional Logic, Logical Inference algorithm can be solved by using

  • Logical Equivalence
  • Validity
  • Satisfying ability

16. What does a production rule consist of?

Answer: The production rule comprises of a set of rule and a sequence of steps.

17. How is Machine Learning related to Artificial Intelligence?

Answer: Artificial Intelligence is a technique that empowers machines to understand human behavior. Machine Learning is nothing but a subset of Artificial Intelligence. It is basically the science of getting computers to act by providing them data and letting them act upon it on their own, without being explicitly programmed to do so.

18. What are Bayesian Networks?

Answer: A Bayesian network is a probabilistic graphical model that represents a set of variables and their conditional dependencies by using a directed acyclic graph.

19. What Are Intelligent Agents, and How Are They Used in AI?

Answer: Intelligent agents are autonomous entities that use sensors to know what is going on, and then use actuators to perform their tasks or goals. They can be simple or complex and can be programmed to learn to accomplish their jobs better.

20. What Is Automatic Programming?

Answer: Automatic programming is describing what a program should do, and then having the AI system “write” the program.

After going through the above questions with answers, the ball is now in your court. You have now to be confident enough that you will make it great on the interview day. This is now your opportunity to showcase your experience to the level that the interview panel will like it. I wish you good luck in your interview.

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