Summary
My friend recently interviewed at Info Edge, facing the "Top K Frequent Elements" problem. Although he provided a correct min-heap solution, the interviewer pushed for a more optimal bucket sort approach, which he hadn't prepared for.
Full Experience
My friend recently interviewed at Info Edge, where he encountered a challenging data structures and algorithms question. The interviewer asked him to find the k most frequent elements in an array. My friend quickly devised a solution using a HashMap to count frequencies and then a min-heap of size k to extract the top elements, achieving an O(n log k) time complexity. He explained his approach clearly and coded it without issues, feeling confident in his answer.
However, the interviewer wasn't entirely satisfied and challenged him to optimize further, asking if he could achieve a better time complexity than O(n log k). My friend paused, as he believed his solution was already optimal. The interviewer then offered a crucial hint: "Think about the constraints. What's the maximum frequency an element can have?" This pointed towards a bucket sort approach, a technique my friend admitted he hadn't practiced. Ultimately, he couldn't come up with the optimal solution, highlighting a significant gap in an otherwise strong performance.
Interview Questions (1)
Preparation Tips
This experience taught me a vital lesson: we often overlook 'simple' algorithms like bucket sort, counting sort, and radix sort during interview preparation, believing them to be less important than more complex data structures. However, this interview clearly demonstrated that these fundamental techniques can be the crucial differentiator between a good solution and an optimal one, especially when leveraging problem constraints for efficiency. It underscores the importance of a comprehensive preparation that includes mastering all foundational algorithms.