[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:-
- What is overfitting? Describe how models actually overfit using a scenario.
- What is gradient descent? Difference between gradient descent and backpropagation?
- Is the gradient a vector or a scaler?
- Bias-Variance Tradeoff
- Working of LDA using an example.
- How Infersent generates sentence embedding (Working of the entire architecture).
- How would you do NER from scratch?
- 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?
- 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:-
- Describe the sequential minimal optimization(SMO) algorithm.
- 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?
- Recent trends in NLP
- 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.