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How would you craft a recommendation service that reflects a user's auditory preferences, and what historical listening details would you leverage?

Elaborate on the differences between the tabular and multidimensional model, highlighting the one that Azure analysis services endorse?

Share with me an instance of failure at your work.

Why do we face the curse of dimensionality problem in high-dimensional data? Can you give an example of how you would address this problem?

How do you handle high-dimensional data sets in machine learning challenges?

How do the summarizations of Logistic Regression and SVM deviate from each other?

How does the R-square measure fall short in providing a complete picture of linear regression model performance? What does it imply about the linear regression model when you encounter a low R-square?

Could you walk me through the process of training a machine learning model and then deploying it?

Would you elucidate the meaning of training and loss graphs pertaining to neural networks? How does the choice of loss function impact the shape of the loss graph, and what are some extensively used ones? How do the training and loss graphs differ, and what role do they play in optimizing and maximizing model performance?

How would you compare and contrast the parametric and non-parametric statistical approaches, and why might one approach be preferred over the other in certain research situations?