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[D] Interview Questions

So, recently I was interviewed for the position of Data Scientist The interview went into two stages with one being a telephonic round which ended in 35-40 minutes and the other being a Hangout call which ended up in 50-60 minutes. The interviewer was very good and asked a lot of amazing questions mostly focusing on the fundamentals. Here is the list of questions that were asked to me:-

  1. What is overfitting? Describe how models actually overfit using a scenario.
  2. What is gradient descent? Difference between gradient descent and backpropagation?
  3. Is the gradient a vector or a scaler?
  4. Bias-Variance Tradeoff
  5. Working of LDA using an example.
  6. How Infersent generates sentence embedding (Working of the entire architecture).
  7. How would you do NER from scratch?
  8. In AllenNLP, one of the models which it uses to do NER is based on ELMO. Given a piece of text (say, “Jack is playing football), how would ELMO go on about doing tagging Jack to PER?
  9. Given a piece of text (say, “Jack and Mary had been married for a long time but gradually drifted apart until they separated.”) how would you do relation extraction from scratch? The outcome should be: Jack – Married_To – Mary

Other questions from my previous interviews:-

  1. Describe the sequential minimal optimization(SMO) algorithm.
  2. Suppose there are four persons, each one is standing at the corner of a square table. The probability of any one of them moving in either direction (clockwise/anticlockwise) is 1/2. If all of them started moving together at the same time at the same speed, what is the probability that none of them will collide?

General Questions:-

  1. Recent trends in NLP
  2. Data structures – coding questions

I hope this helps anyone who is preparing for there interviews. I will keep on updating this, meanwhile, I also request others to please do share your interview experience and put forward some questions which you faced in your interview.


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