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For a dataset with 4 behavioral variables and 20 contextual attributes that needs normalization, what would be your approach for building an unsupervised learning model that can detect outliers? Also, which evaluation metrics and visualizations would you prefer to measure the model's performance?

Can you identify several optimization algorithms you have come across in your experience? How do you approach optimization when faced with a dataset containing a million variables? How is the simplex method applied to find solutions to optimization problems?

Can you explain the differences between tabular and multidimensional models, and which among the two is the supported model for Azure analysis services?

What are the key features of a minimum height tree, and how can one be effectively built?

Can you propose a method to systematically interpret a string that includes nested parentheses?

How do you ensure that the data you are feeding into machine learning algorithms is representative and unbiased?

How do you quantify the factors affecting the ride requests of the company?

How would you describe k-means clustering, and is it capable of reaching a global optimum? Also, code a k-means clustering function for an N*D matrix.

How can you manually calculate the length of a sorted list with -1 as an out-of-range signal, avoiding the use of built-in commands?

Can you propose a structure for a model that excels in object detection within visual frames?