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How To Crack Data Science Interview At Microsoft

How To Crack Data Science Interview At Microsoft


While preparing for a data science job interview at Microsoft, candidates need to prepare about topics ranging from analytical thinking to problem-solving. In this article, we list down some of the top data science questions to ace your interview as well as some important tips to prepare for D-day.

In a blog post by Microsoft, the recruiters shared that they expect the candidates to have a passion for coding, solving problems and the incredible things technology can do for people around the world. Every interview looks for something specific depending upon a particular job profile. 



Technical Interview

A technical interview varies depending upon the team and the role which the candidate is going to apply for. Some of the important points to be noted while appearing in a data science interview are:

  • A Neat Resume: In an interview, a resume acts as a mirror for your experiences and achievements. A resume must be neat enough to understand. Also, the candidate must include real-life examples, projects, and experiences in the resume by depicting its impact as well as how it benefits the end-users. Before an interview, one must also go through all the topics which are mentioned in a resume.
  • Communication Skills: Interviewers are no mentalists. This is a general point where strong communication is needed while appearing for an interview. Also, while responding to any questions, the candidate must understand what is being asked by the interviewers and respond accordingly.
  • Delving Deep Into The Basics: In a data science interview, a candidate must dive deep into topics such as the basics of machine learning algorithms, statistics, linear algebra, probability, among others. There is a plethora of online courses where one can easily brush up the basic concepts beforehand.
  • The Code To Code: While appearing in a personal interview round, the interviewer might ask you to solve a coding problem. The right way to approach it is to ask any doubt or questions related to the problem and not just directly rush into solving the problem. Following this rule will not only help you to complete the task with a more accurate answer but also it will showcase your communication skills and the way of understanding and solving a problem.   

Solve The Questions

Now, we will take a look at the questions which are usually asked in a data science interview at the tech giant and preparing them will surely help you to ace your data science interview.

See Also

  • Given a string like “fun1(fun2(a,b) ,c ,fun2(a),fun3(c,d))” how many unique function signatures are there.
  • You have a bag with 6 marbles. One marble is white.  You reach the bag 100 times. After taking out a marble, it is placed back in the bag. What is the probability of drawing a white marble at least once?
  • Given a box of dimensions W, H, and coordinates of points inside that box. Find the largest area that is free of any of these points.
  • Why neural network work and why is it a booming field?
  • What’s power? How to explain it to a non-statistics person? what’s a false positive and false negative?
  • How Random Forest, Lasso and Ridge Regression work? Difference between lasso and ridge.
  • Write a function to check whether a particular word is a palindrome or not.
  • Find the maximum of subsequence in an integer list.  
  • Generate a fair coin from a biased one.
  • Generate 7 integers with equal probability from a function which returns 1/0 with probability p and (1-p).
  • What are the ROC curve and the meaning of sensitivity, specificity, confusion matrix?
  • Given a time series dataset, how will you predict future value?
  • How to explain a deep learning model to customers? 
  • What is the definition of a P-value? How to explain p-value to customers.
  • How can you compute an inverse matrix faster by playing with some computation tricks?
  • Describe how gradient boost works.
  • Describe the steps for data wrangling and cleaning before applying machine learning algorithms.
  • How to deal with unbalanced binary classification?
  • How do you detect if a new observation is an outlier? What is the bias-variance trade-off?
  • Explain the Support Vector Machine (SVM).

Wrapping Up

The interview questions above have been cited from various sources, comments and reviews about data science interviews at Microsoft. Also, we can see that the questions are mostly related to probability, basics of machine learning algorithms, and other techniques of extracting data in an ML method. Thus, a candidate with a good grip on these topics will surely ace the data science interview.


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