Microsoft Data Science/AI Interview Questions — Acing the AI Interview

Microsoft seeks to weave its Artificial Intelligence and Core Windows OS components into a single team.

Source: TNW

Microsoft’s dominance in the Enterprise is well known. Microsoft has ridden the cloud-computing wave. In the fiscal first quarter, its Azure services and Office 365 online-productivity business — saw revenue soar 90% and 42%, respectively.


MyAI Interview Questions articles for Google, Amazon, Apple, Facebook, Salesforce, Uber, LinkedIn have been very helpful to the readers. As a followup, next couple of articles were on how to prepare for these interviews split into two parts, Part 1and Part 2. If you want to find suggestions on how to showcase your AI work please visit Acing AI Portfolios. For Career Insights check out the interview I did with Jesse. Now onto the Microsoft Data Science Questions…


In a recent letter by the CEO Satya Nadella to all Microsoft employees, there are two new teams formed in Microsoft, Intelligent Cloud and Intelligent Edge to shape the next phase of innovation. This announces the profound shift to weave Artificial Intelligence in to all that Microsoft does. Needless to say Microsoft following this announcement may increase the AI related hires to the company.

Interview Process

Microsoft has a typical interview process like most other companies who hire Engineers. The Data Science roles usually have a process tweaked a little which reflects the importance of different aspects under the umbrella of Data Science. There are usually phone interviews(involve coding) followed by onsite interviews. Onsite there are about 4–5 interviews. There might be 2–3 of them really going deep on Data Science related questions, research and models. The remaining ones are aimed to test the coding skills.

Important Reading
Source: Microsoft Blog

Like Google, Microsoft has its own version of the AI School which was released very recently. Its core AI platform is sliced into three components, service, infrastructure and tools.

  1. Microsoft AI School: Different Learning Paths
  2. AI Demos(Showcases the Data Presentation and Visualization): AI Demos
  3. Microsoft Azure AI Solutions (Similar to Amazon AWS): Projects
  4. Microsoft Research Podcast: Research Podcast (Courtesy: Petercooper via HackerNews)
AI/Data Science Related Questions
  • Merge k (in this case k=2) arrays and sort them.
  • How best to select a representative sample of search queries from 5 million?
  • Three friends in Seattle told you it’s rainy. Each has a probability of 1/3 of lying. What’s the probability of Seattle is rainy?
  • Can you explain the fundamentals of Naive Bayes? How do you set the threshold?
  • Can you explain what MapReduce is and how it works?
  • Can you explain SVM?
  • How do you detect if a new observation is outlier? What is a bias-variance trade off ?
  • Discuss how to randomly select a sample from a product user population.
  • How do you implement autocomplete?
  • Describe the working of gradient boost.
  • Find the maximum of sub sequence in an integer list.
  • What would you do to summarize a twitter feed?
  • Explain the steps for data wrangling and cleaning before applying machine learning algorithms.
  • How to deal with unbalanced binary classification?
  • How to measure distance between data point?
  • Define variance.
  • What is the difference between box plot and histogram?
  • How do you solve the L2-regularized regression problem?
  • How to compute an inverse matrix faster by playing around with some computational tricks?
  • How to perform a series of calculations without a calculator. Explain the logic behind the steps.
  • What is a difference between good and bad Data Visualization?
  • How do you find percentile? Write the code for it.
  • Find max sum subsequence from a sequence of values.
  • What are the different regularization metrics L1 and L2?
  • Create a function that checks if a word is a palindrome.
Reflecting on the Questions

Microsoft interviews have a lot of open ended questions where the solutions are open to interpretation. Many questions are also based on data presentation and visualization. This is different from the other companies we have looked at previously. Data Presentation and Visualization is explained in this article where I talk about how to prepare of such interviews.



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