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.

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

### 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.

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

#### Latest posts by Ajitesh Kumar (see all)

- What, When & Why of Regularization in Machine Learning? - June 2, 2019
- Unit Tests & Data Coverage for Machine Learning Models - May 11, 2019
- ML Models Confusion Matrix Explained with Examples - March 30, 2019