Palantir Coding & DSA interview questions

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.

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.

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.

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 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 follow-up to number-of-islands, and the question where candidates reveal whether they understand WHY BFS finds shortest paths. Plus the two variants (weights, eliminating obstacles) that decide hard-screen outcomes at xAI.

The third member of the grid trilogy, and the one that tests backtracking discipline: marking, unmarking, and the early-exit pruning that turns a timeout into a pass.

The Palantir coding-screen classic. It looks like merge-intervals but needs a different weapon, the sweep line, and the boundary-tie detail decides whether your answer is right or off by a planeload.

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.

Every data migration at every customer site needs this exact tool, which is why Palantir and Retool keep asking it. The diff is a dict comparison; the grade hides in keys, duplicates, and column drift.

Palantir's bread and butter: 'Acme Corp.' and 'ACME Corporation, Inc.' are the same customer; prove it in code. Normalization does more work than the clever algorithm, and blocking is what makes it run before the heat death of the universe.

Kahn's algorithm gets you level one. The round is won at level three, where 'run up to K tasks at once' breaks naive topo sort, and the indegree bookkeeping you chose at L1 either saves you or sinks you.

Palantir's signature coding genre isn't an algorithm; it's working from unfamiliar documentation: a graph API, a custom serialization spec, a mini query language. The behaviors graders score, plus the BFS-over-an-opaque-client worked example.

The build behind every access policy and routing rule a customer wants to edit without a deploy. The trap is operator precedence: candidates who tokenize fine still get `a or b and c` wrong, and that's the whole grade.

Executing a query is the easy half; deciding HOW to execute it is the staff-level half. The interesting failure is choosing an index that returns 90% of the table, where a full scan is genuinely faster, and a planner that knows that beats one that always uses the index.

The function reads a file, parses it, computes a metric, and prints, all tangled together. The move interviewers want is not prettier code, it's pulling pure logic out from side effects so each piece can be tested in isolation.

A classic FDE take-home shape: messy real documents in, structured index out. The grade is in the seams between reader, extractor, and writer, and in what happens when one file is garbage.

All-pairs comparison is O(n^2) and dies long before 1TB. The senior move is MinHash plus LSH: hash documents so only likely-similar pairs ever land in the same bucket, then verify just those.

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.

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.

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 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.

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 coding question that doubles as a RAG-fundamentals check. Greedy packing is easy; the oversized-sentence case, the overlap-progress trap, and the tests you write unprompted are what AI-lab interviewers actually score.