hashmap

The most common screen opener, and interviewers use it to check whether hashmap thinking is reflexive. Here's the one-pass answer, the narration that earns points, and the duplicate-handling edge case most candidates fumble.

A 5-minute warm-up that quietly tests the most useful idea in practical coding: choosing a canonical key. The sorted-string vs character-count tradeoff is exactly what interviewers want to hear you reason about.

Top-K is the most reused primitive in FDE interviews: it reappears inside log parsers, analytics questions, and retrieval ranking. Three solutions exist; knowing which one to lead with is the real test.

Looks like a sliding-window problem; isn't one. The prefix-sum + hashmap trick that solves it is the same idea behind sessionization and cumulative-metrics questions later in the loop: learn it once, reuse it three times.

The single most-asked design-a-data-structure question in FDE loops. There's an interview-legal Python shortcut and a from-scratch version, and knowing when to offer which is half the grade.

The whole question is the representation: store only the nonzeros as index→value, then either two-pointer over sorted indices or hash-join. The follow-up that decides the design is what happens when one vector is dense.

A sliding window with a last-seen index map, O(n) in one pass. The whole interview turns on one subtlety: when you hit a repeat, the window's left edge must jump forward and never slide backward, which is exactly the bug most candidates ship.

The clean answer hinges on one insight: preorder names the root, inorder splits left from right. The trap is the O(n squared) version that slices arrays and scans for the root; the O(n) version uses a hashmap of inorder indices and passes bounds instead.