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I'm interested in your understanding of MapReduce and its inner workings. Could you elaborate?

In your ML projects, how have you leveraged TensorFlow or PyTorch?

Considering a grid with each position assigned a real number, what strategy ensures the highest cumulative value when moving from the top-left to the bottom-right corner, restricted to down or right movements?

In your experience, have you found that using an LRU cache improves performance? How so?

How would you go about writing a function that determines the RMSE for two provided arrays?

Could you explain the importance of training and loss graphs for neural networks? How do various loss functions impact the shape of the loss graph, and what are some commonly known ones? What is the contrast between the training and loss graphs, and how do they assist in optimizing and maximizing model performance?

Tell me about a performance rating that you disagreed with.

What would you do to improve SAP Concur if you were hired for Data Scientist?

Explain what heteroscedasticity means?

How do you weigh the advantages against the disadvantages of SVM in machine learning?