Everyone spots that the customer records need resolving. The failure that actually sinks this project is quieter: the word fraud means three different things in the three datasets, so the union of their labels trains a model that is confidently wrong.
Updated Aug 2026 · Grounded in real Forward Deployed Engineer interview loops and written to a senior-engineer editorial bar.
Everyone spots that the customer records need resolving. The failure that actually sinks this project is quieter: the word fraud means three different things in the three datasets, so the union of their labels trains a model that is confidently wrong.
Candidates reliably identify entity resolution because it is visible in the schema. Almost nobody interrogates the label, which is invisible: each bank had its own confirmation process, its own write-off threshold, and its own lag between a transaction and a chargeback, so a 1 in one system is not a 1 in another. Training on the union produces a model whose errors are systematically worse for whichever bank had the strictest definition, and nobody finds out until it declines that bank's customers. Naming this before being prompted is what separates the strong answers.
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