1024 Kegs 5 Servants 3 Months
Identifying poisoned keg with 5 testers is a hard quant interview question on Brain Teasers, reported to have been seen at Goldman Sachs, Jane Street, Old mission and Two Sigma.
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This brain teaser is about using limited, risky tests over time to uniquely identify one dangerous item among many safe ones. It appears in hard quant interviews and forces candidates to think of experiments as information channels, not just yes-or-no checks. The twist with timing and multiple participants turns a simple poisoning story into a compact model of how to extract maximal information from constrained resources.
It trains your ability to encode and decode information through discrete outcome patterns and to quantify how many distinct possibilities a given experimental design can separate. In quant prep terms, it builds intuition for state spaces, combinatorics, and treating observed outcomes as symbols in a coding system rather than isolated events.
This matters in quant interviews because real trading, risk, and research problems often boil down to designing clever "experiments" under cost, time, and risk constraints. Interviewers want to see whether you can structure uncertainty, measure informational capacity, and reason from abstract principles instead of brute forcing.
What it tests
This problem class is governed by the principle of information encoding through distinguishable test outcomes. When you have multiple testers (like servants) and multiple rounds (months), each tester's possible sequence of outcomes (alive or dead in each month) can be mapped to a digit in a positional numeral system whose base equals the number of distinct states per tester. The total number of uniquely identifiable items is then the base raised to the number of testers. This holds because each unique combination of outcomes across all testers corresponds to a unique item tested, allowing you to reconstruct which item was responsible for the observed pattern. The power of this approach comes from maximizing the information extracted from each tester by leveraging all possible outcome states, not just binary alive/dead but also the timing of death.
Practise this question with written feedback, or hear it in a spoken mock interview.
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