Sierra Coding & DSA interview questions

This is the shape of the TypeScript debugging round at the agent companies: a forty-line stream handler that mostly works. Each bug has a one-line demonstration, and none of them is in the model. One is a stale closure, one is a decoder splitting a multibyte character, and one is a render per token.

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.

The interval pattern shows up everywhere in FDE loops: calendars, flight segments, log windows. Master the sort-then-sweep idiom here and three other interview questions fall out for free.

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 gentlest 'design a class' question in FDE screens, and the warm-up interviewers use before rate limiters and sessionization. The deque trick is easy; the API and time-window follow-ups are where the signal is.

The canonical grid question, and the template for every flood-fill, shortest-path, and region-labeling variant FDE loops throw at you. BFS vs DFS vs union-find: here's which to write and which to merely mention.

The quintessential FDE practical: messy real-world input, grouping, ranking, and a hidden rubric line for how you treat malformed data. Most candidates parse happily and lose the round on the lines that don't parse.

The reported ElevenLabs live round, written in a shared Google Doc, no execution, no autocomplete. Inherited folder permissions plus permission-change timestamps, and the two-axis resolution rule that keeps the code to thirty lines.

Everyone knows the sort + two-pointer outline. The interview is actually about duplicate handling: three separate skip conditions that candidates routinely botch live. Here's the clean version and the narration that goes with it.

The follow-up interviewers reach for when merge-intervals goes too smoothly. The three-phase scan is elegant, but only if you've internalized the overlap condition most candidates have to re-derive under pressure.

Anthropic-style screens open with deceptively simple builds like this, then extend them three times. The grade isn't the counting; it's whether your first version survives the extensions without a rewrite.

A verbatim-reported multi-part at OpenAI and Anthropic. Each level breaks the previous design on purpose: the burst-at-the-boundary flaw is planted, and naming it before the interviewer does is how you win the round.

The assign-then-update loop is ten lines; the signal is whether you handle the three things that bite in production: initialization, an empty cluster, and a real convergence test instead of a fixed iteration count.