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

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