Qube Research & Technologies Interview Questions

Reported as a Qube Research & Technologies interview question, this problem challenges your understanding of fundamental machine learning algorithms by requiring you to implement K-Nearest Neighbors (KNN) classification entirely from scratch. You'll be provided with training data and test data, and your goal is to predict the labels for the test set without relying on any pre-built KNN libraries. This task assesses your ability to translate algorithmic principles into code, including distance calculations and majority voting for classification. It's a crucial test for aspiring data scientists and ML engineers. The full problem and model solution require a subscription.

Managing I/O requests efficiently at the hardware level requires smart scheduling algorithms that minimize mechanical movement. Featured in quantitative research interviews at Qube Research and Technologies, this algorithmic task requires you to simulate a disk head servicing pending requests using the shortest seek time strategy. You must dynamically select the closest track from the current position while properly resolving ties and tracking cumulative head travel distance. Review the complete problem statement, underlying algorithmic principles, and optimized solution code by obtaining a full subscription.