1582 articles
Find All Anagrams in a String combines the frequency-count pattern with a fixed sliding window — a compound technique that Google and Amazon use to filter candidates who understand both string hashing and window management. The "match counter" optimization is the key to an elegant O(n) solution.
Random Pick with Weight teaches weighted random sampling — a foundational technique in machine learning, A/B testing, and traffic routing. Google and Meta use this problem to assess whether candidates understand prefix sums and binary search well enough to implement probability distributions from scratch.
Brick Wall teaches the insight of counting the complement — instead of minimizing bricks crossed, maximize gaps hit. Google uses this problem to test whether candidates can reframe a minimization problem as a maximization problem and solve it in O(n) with a frequency map.
Unique Number of Occurrences teaches the double-hash technique: build a frequency map, then check if all frequency values are distinct. This two-layer hashing pattern appears in data validation, duplicate detection, and constraint checking problems at every major company.
Count Wonderful Substrings combines bitmask XOR with prefix parity tracking to count substrings where at most one character has an odd frequency. This advanced hashing problem teaches the bit-manipulation pattern that Google and Meta use to filter candidates for senior-level roles.