Build a feature-flag engine: deterministic percentage rollouts, targeting rules, and monotonic ramp-up
A 10% rollout where the same user flips on and off between requests is worse than no rollout. The whole problem is determinism: hash the user into a stable bucket so ramping 10% to 25% only ever adds users, never reshuffles them.
Updated Aug 2026 · Grounded in real Forward Deployed Engineer interview loops and written to a senior-engineer editorial bar.
A 10% rollout where the same user flips on and off between requests is worse than no rollout. The whole problem is determinism: hash the user into a stable bucket so ramping 10% to 25% only ever adds users, never reshuffles them.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
