18 Microsoft Data Science Interview Questions

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This is a list of 18 questions which has been asked in several Microsoft data science / machine learning interviews. These questions have been compiled from Glassdoor and other sources. We shall be posting a series of related objective questions (capsule) quizzes in very near future.

  1. Can you explain the Naive Bayes fundamentals? How did you set the threshold?
  2. Can you explain SVM?
  3. How do you detect if a new observation is outlier? What is bias-variance trade off ?
  4. Basic statistical questions such as define variance, standard deviation etc
  5. Discuss how to randomly select a sample from a product user population.
  6. Describe how gradient boost works.
  7. What is L1 and L2 Norm? What is the difference between them?
  8. What is central limit theorem? How to determine is the distribution is normal?
  9. What algorithm can be used to summarise twitter feed?
  10. Simple probability questions that dealt with Bayesian equations
  11. What are some of the steps for data wrangling and cleaning before applying machine learning algorithms?
  12. How to deal with unbalanced binary classification?
  13. What is the difference between box plot and histogram?
  14. How do one go about solving the L2-regularised regression problem?
  15. Probability fundamentals
  16. Describe Markov chains?
  17. Describe different regularisation methods such as L1, L2 regularisation?
  18. Neural networks fundamentals



Ajitesh Kumar

Ajitesh Kumar

Ajitesh has been recently working in the area of AI and machine learning. Currently, his research area includes Safe & Quality AI. In addition, he is also passionate about various different technologies including programming languages such as Java/JEE, Javascript and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data etc.

He has also authored the book, Building Web Apps with Spring 5 and Angular.
Ajitesh Kumar

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