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Nvidia Interview Experience for QA SDET Intern (On-Campus)

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  • Difficulty Level : Expert
  • Last Updated : 28 Jul, 2022
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Interview process held in Nov 2021 for internship duration of 5 months starting from Jan to Jun. Hackerrank test consists of 2 coding problems, OS, computer fundamental, and logical reasoning.

Shortlisted about 16-20 students. The interview duration was 1 Hr. there were two interviewers on MS Teams. One was asking CPP questions and the other was asking python 

  • Questions were based on Project
  • What is the difference between lemming vs stemming
  • What are things from NLP you used?
  • Do you know any text classification algorithms?
  • Define a string of length l in python
  • Tell me about dynamic memory allocation
  • What is the difference between LIFO vs FIFO
  • Create a new list of words from the given list where the substring ‘ant’ is present in the word
  • Asked me to code for max min element from the array
  • There were a question  about hardware 
  • What is the difference between SSD and HDD?
  • Types of SSD?
  • Tell me CPU parts.
  • How computer boots, bios?
  • What is blod, and how did it has occurred?
  • Which games you played did yed what game settings you change (FPS, resolution)
  • How to disable/enable storage devices from bios?
  • How to block any service or app at startup
  • They asked quean stion on ML also as there was opening for ML tool developer.
  • Working and equation of SVM regressor ?
  • Situation-based q on ML algorithm to choose.
  • What is convolution ?
  • Difference between logistic and linear regression
  • Whado t is neural network
  • Which graphics card you know which is latest GPU
  • Hopefully I was able to answer the moan and st of the questions and I got the offer for internship.
  • IMP topics to study
    • Davisualizationion and data cleaning
    • Different types of ML models and how they work
      For Eg: regression and types of regression and how they work (algorithm)
    • If you have studied DNNs then
      Back and forward propagation, model training, neurons, and DNN layers, gradient descent algorithm, cost optimization
      These are just the basic things expected to have
    • Then you have your model analysis part
      Errors (rmse, r-squared, least squares, etc)
      Then there is model metrics (accuracy, precision, etc)
      This much if you can cover it would cover most the things that can be asked
My Personal Notes arrow_drop_up
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