1. Will I be tested on SQL? What will be the structure of the rounds - coding vs ML vs design? 2. Are questions based on data structures (LC style) or data manipulation based (numpy pandas etc)? 3. I am comfortable with basics of ML - regression, clustering, regularization, overfitting, bias variance trade-off. What else do I need to study? 4. How important is system design? I am not a new grad.
Interview rounds depends on team. Typically, LC medium coding, ML and stats basics and ML system design. Haven’t heard any teams ask SQL or numpy, pandas style q. Best to ask recruiter/ hiring manager.
What does ML basics include? Classic ML like Linear regression, Logistic regression, SVM, neural net, k means clustering, bias variance? Or also Random forest, xg boost, naive bayes, stacking models
All of the above
Lol, use it for practice and prepare for ML engineering role at FAANG. Avoid that place, as far as I know, most good folks have left or are on their way out given its internal struggle with employee logistics and culture.
Still good to know Walmart’s process