data modeling

The dimensional-modeling staple. The trip fact table is the easy part, interviewers score grain declaration, the surrogate-key rationale, and how you handle the rider who changes cities. Here's the full shape.

Everyone can define SCD2; very few can write the MERGE that maintains it correctly. The expire-and-insert dance, the one-statement trick, and the late-arriving-change trap that breaks naive implementations.

The question where data engineering meets the AI-native FDE job. Conversations, turns, LLM calls and tool calls each have their own grain, model them wrong and neither the cost dashboard nor the eval set can be built.

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.

SCD2 tracks when an attribute was true; bitemporal also tracks when you knew it. The question separates engineers who can audit a number from those who quietly overwrite history when a correction arrives.

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.

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.

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.

The only round where you choose the problem, which sounds like a gift and is where most candidates lose it. Four things get graded in one flow, and the fourth is the one engineers skip.