palantir

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 classic market-sizing question with Palantir's twist: the estimate is the warm-up, and the scored move is the verification plan. Two independent decompositions, a cross-check, and the datasets that settle it.

The Deployment Strategist data-modeling case. It looks like schema design; it's graded as ontology thinking, entities with identity, events that link them in time, documents as evidence, plus privacy designed in, not bolted on.

The verbatim Deployment Strategist case. The trap is accepting the premise, 'best for whom?' is the first scored move, and the second is designing a natural experiment from public data instead of hand-waving at 'analytics.'

The Palantir decomposition round has no right answer, it has a right METHOD, and it's the most-failed interview in the FDE loop. Here is the four-move rubric interviewers actually score: clarify, model, spine, iterate.

The classic Palantir decomposition prompt. The winning answer hinges on one asymmetry candidates rarely surface unprompted, and on starting with something embarrassingly simpler than an ML model.

The most open-ended prompt in the Palantir canon, no scale given, no user given, no disaster given. What separates passing answers is one design assumption about the data that almost nobody states out loud.

The purest data-modeling prompt Palantir asks, the whole interview lives in the entity-resolution edge cases. Twins, maiden names, shared insurance IDs... and one architectural decision about merging that candidates get fatally wrong.

Looks like an algorithms question; it's actually an operations question wearing one. The candidates who fail reach for 'solve VRP'; the ones who pass design for the 2pm reality that invalidates every morning plan.

The decomposition prompt we use to teach the skill that actually decides these interviews: graceful iteration under perturbation. Your score isn't your design; it's the delta when the design gets punched.

A live-reported Palantir FDSE decomp with a built-in twist: after you design for the driver, the interviewer flips you to Uber's side. The expected-value-per-hour model, the tip-prediction ethics trap, and how to survive the flip.

A reported Palantir FDSE decomposition where the fatal move is putting triage inside an algorithm. The bed-capability data model, the visibility-before-optimization v1, and the conflicting-priorities answer that keeps clinicians in charge.

A port is three coupled scheduling problems in a single prompt: where ships dock, which cranes work them, and where containers land in the yard. The strong answer decomposes them, names where they couple, and refuses to solve the whole thing as one giant optimizer.

The most-reported decomposition prompt in the FDE canon. Almost every candidate builds a routing model, and almost every candidate is optimizing the wrong term of the equation.

The number 500 is the trap. Candidates hear it as a scaling problem and design a pipeline factory, when the interviewer is watching to see whether you refuse to ingest 500 things at all.

A dataset lands in your lap with a one-week deadline attached. Most candidates start exploring the data. The ones who pass start by working out which questions this data physically cannot answer.

A genuine filter, not small talk, Palantir interviewers have heard every dodge. The answer that works engages the controversy directly, with a position you can defend under two rounds of pushback.

Candidate reports describe a Palantir round where you are handed unfamiliar material, given time with it, and asked to explain it. It is the job in miniature: an FDE learns a customer's domain in a week and has to be useful by Thursday. The scoring is on what you chose to teach, and on what you admitted you did not learn.