Ml Engineer Interview Questions

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ML Engineer -1 @Groww Time - 75 mins (50 mins ML case study + 25 mins coding) Round 1 : Brief discussion of my ML work experience and scaling challenges, then two coding problems(must use Python) , followed by a case study(ML based) - Given a list of strings , output the common prefix characters for all of them. Eg - [flow,flower] , output - flow. Given a function 'f' which returns a random integer between 1 to 7. Now you have to build a function using this function that can output a random integer between 1 to 10. - (probability/normalisation based) Question 3 was an ML case study, open end discussion - Suppose you are the owner of early stage Zomato. And you have to handle the legitimate and illegitimate order refunds. How would you tackle this as - a) A business owner b) An ML engineer The goal is to identify all possible use cases and think critically how to categorise the order refunds. Identify the variables and raw training data set and which algorithms would you use to solve this problem at a scale of order of 10+ million orders day to day basis. Discussion on Classification, K-means Clustering and Label/Hot Encoding challenges.
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ML Engineer

Interviewed at Groww

3.5
Oct 22, 2024

ML Engineer -1 @Groww Time - 75 mins (50 mins ML case study + 25 mins coding) Round 1 : Brief discussion of my ML work experience and scaling challenges, then two coding problems(must use Python) , followed by a case study(ML based) - Given a list of strings , output the common prefix characters for all of them. Eg - [flow,flower] , output - flow. Given a function 'f' which returns a random integer between 1 to 7. Now you have to build a function using this function that can output a random integer between 1 to 10. - (probability/normalisation based) Question 3 was an ML case study, open end discussion - Suppose you are the owner of early stage Zomato. And you have to handle the legitimate and illegitimate order refunds. How would you tackle this as - a) A business owner b) An ML engineer The goal is to identify all possible use cases and think critically how to categorise the order refunds. Identify the variables and raw training data set and which algorithms would you use to solve this problem at a scale of order of 10+ million orders day to day basis. Discussion on Classification, K-means Clustering and Label/Hot Encoding challenges.

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