Algorithmic Problem Solving Fundamentals Questions

Core foundation for solving entry level algorithmic problems. Focuses on arrays, strings, basic mathematics and number theory problems, simple bit manipulation, basic linked list and tree operations, stacks and queues, basic sorting and searching algorithms, simple recursion, and use of hash based data structures for counting and lookup. Emphasizes understanding asymptotic time and space complexity, selecting appropriate data structures for a task, and clear step by step problem solving including writing a brute force solution and analyzing correctness.

HardTechnical

60 practiced

Design and implement a running median data structure in Python that accepts an incoming stream of numbers and returns the median at each insertion. Use two heaps (max-heap and min-heap) for O(log n) insert and O(1) median retrieval. Discuss memory and performance trade-offs for high-throughput SRE telemetry.

HardTechnical

41 practiced

Given the root of a binary tree (not necessarily a BST), implement a Python function to find the lowest common ancestor (LCA) of two given nodes. Describe the recursive strategy and explain complexity. This problem helps reason about dependency trees and fault-domain isolation in SRE work.

MediumTechnical

49 practiced

Implement a per-user sliding-window rate limiter in Python: given a stream of (user_id, timestamp) events, allow up to N events per user within any rolling M-second window. Provide a data structure that supports high request rates and discuss memory/performance trade-offs for SRE deployment.

MediumTechnical

46 practiced

Rotate an array of integers to the right by k steps in-place, using O(1) extra space. Implement a Python function that works for any k (k may be larger than array length). Discuss time complexity and provide an example: nums=[1,2,3,4,5,6,7], k=3 -> [5,6,7,1,2,3,4].

MediumTechnical

47 practiced

Given a list of intervals representing scheduled maintenance windows (each interval is [start, end]), merge all overlapping intervals and return the merged list. Implement in Python and discuss O(n log n) sorting step plus linear merge. Example: [[1,3],[2,6],[8,10],[15,18]] -> [[1,6],[8,10],[15,18]].

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