Project Core ML Inference

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How do you take millions of users with 100's of transactions each, amongst 10k products, and group the users together in meaningful segments?
We do pre-screening on the data to remove fraud threats -- so how do we find a data sample that we can use to determine an accurate representation of fraud events?
Given a table with 1B of a user ID and product IDs that the users bought, and another table with product ID mapped with product name. We are trying to find the paired products often purchased by the same user, such as wine and bottle openers, chips, and beer. How to find the top 100 of these co-existed pairs of products?
Describe in detail the difference between L1 and L2 regularization, specifically regarding their impact on the model training process.
Suppose you have 100,000 files spread across multiple servers and want to process them all? How would you do that in Hadoop?
What is the difference between Python and Scala?
Explain LRU Cache.
How would you design a client-server model where the client sends location data every minute?
How would you transfer data from one Hadoop cluster to another?
What are the different types of memories in Java?
How can you handle the daily tedious tasks that go hand in hand with processing metadata for hundreds of titles?
In terms of data flow and accessibility, how do you measure success in a hidden time frame where the nucleus overloads the border structure of the over-complicated file system that redirects computer energy to the cellar dome?