TOP 99 Best Artificial Intelligence Interview Questions & Answers In July, 2022

Top 99 Artificial Intelligence Interview Questions & Answers

Top 99 Best Artificial Intelligence Questions and Answers


Watch this 18 series Video on AI Interview Q&A below first:



Below video is just for Brain teaser.


What is AI?



Artificial intelligence (AI) is the intelligence exhibited by machines or software. It is also the name of the academic field of study which studies how to create computers and computer software that are capable of intelligent behavior.


What is the difference between strong AI and weak AI?


                                                                                          source: Andrew McKeever youtube

Strong AI (a machine with consciousness, sentience, and mind): The idea behind Strong AI is that the machines could represent human minds in the future. If that is the case, those machines will have the ability to reason, think and do all functions that a human is capable of doing.  Nanobots, which can help us fight diseases and also make us more intelligent, are being designed. The development of an artificial neural network, which can function as a proper human being, is being looked at as a future application of Strong AI.

Weak AI or “narrow” AI: The idea behind Weak AI is the fact that machines can be used to act as if they are intelligent. For example, when a human player plays any game against a computer, the human player may feel as if the computer is actually making impressive moves. But the game application is not thinking and planning at all. All the moves it makes are previously fed into the computer by a human and that is how it is ensured that the software will make the right moves at the right times.


What are the different Applications where AI can be utilized?


Here are some.

game playing

speech recognition

understanding natural language

computer vision

expert systems

heuristic classification etc


Which Language is Best for Artificial Intelligence?


Python. Python is one of the most widely used programming languages in the AI field because of its simplicity. …

Java. Java is also the best choice.







How do I start learning Artificial Intelligence from scratch?


I would suggest to follow this link you can learn in 6 Easy steps.



How do I Learn Machine Learning in 90 Days?


I would suggest to follow this link you can ML learn in 90 days.




What Skills are Required for Machine Learning Jobs?


Check here list of 25 Key Skills required for Machine Learning Jobs.



Machine Learning Engineer interview questions


  • Does the model make any important assumptions about the data? And What types of data can the model handle?
  • What’s your favorite algorithm, and can you explain to a layperson?
  • How do you determine “k” for k-means clustering? Or, how do you determine the number of clusters in a data set?
  • what is the difference between Naive Bayes and logistic regression?
  • Can the model handle missing data? What could we do if we find missing fields in our data?
  • What is “bias-variance trade-off” and why it is basic to machine learning?
  • What’s the difference between probability and likelihood?
  • When should you use classification over regression?
  • What’s the difference between a generative and discriminative model?
  • How can we update the model without retraining it from the beginning?
  • Explain with examples of data cleaning techniques you have used?
  • What is an EM algorithm?
  • Pick your favourite algorithm you like and walk me through the math and then the implementation of it, in pseudo-code.
  • What are Gaussian Mixture Models?
  • What is ‘Training set’ and ‘Test set’?
  • How to assess the quality of clustering, especially to know when you have the right number of clusters.
  • Name some feature extraction techniques used for dimensionality reduction.
  • How do you ensure you’re not overfitting with a model?
  • What cross-validation technique would you use on a time series dataset?
  • Why data cleaning plays a vital role in analysis?
  • Does the model you used had any meta-parameters and thus required tuning? If so, how did you do?
  • How do we perform feature selection that do not involve exhaustive search?
  • What tools and environments have you used to train and assess models in your previous organization?
  • Why is finite precision a problem in machine learning?
  • What is ROC curve?
  • How is True Positive Rate and Recall related? Write the equation?
  • How do you handle missing or corrupted data in a dataset?
  • How is a decision tree pruned?
  • Explain the difference between MLE and MAP inference.
  • Define precision and recall.
  • Where do you usually source datasets?
  • What is Bayes’ Theorem? How is it useful in a machine learning context?
  • What is the difference between Markov networks and Bayesian networks?
  • What could you do if we find missing fields in your data?
  • Name some feature extraction techniques used for dimensionality reduction.
  • How will you test Machine Learning Algorithms for accuracy?
  • Which is more important to you – model accuracy, or model performance?
  • What is loss function in a Neural Network?
  • How do you think Uber is training data for self-driving cars?
  • Which type of machine learning algorithm used in “People who bought this, also bought….” recommendations on Amazon?
  • What approach will you follow to suggest followers on Twitter?
  • How will you design a spam filter?
  • How will you tell if a song in our catalogue is a duplicate or not?
  • What type of data does the machine learning model handle –categorical, numerical, etc.?
  • Which was your last machine learning papers you’ve read?


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