The open-endedness tripped me up more than the actual logic.
Clarify the rules and input format first, then outline a simulation algorithm that iteratively applies match outcomes. Discuss data structures and edge cases, and finally analyze time/space complexity.
Pro tip: Demonstrate adaptability by acknowledging ambiguity in the rules and proposing a flexible, modular design that can easily accommodate rule changes. This shows you can handle real-world uncertainty while still delivering a working solution.
Ask questions to understand the ranking system, match rules, and expected output format. Confirm edge cases like ties or byes.
Choose a data structure (e.g., queue, array) to represent players and simulate matches round by round. Define how winners advance based on the rules.
Consider scenarios like odd number of players, invalid rankings, or rule variations. Explain how your algorithm adapts.
Determine time and space complexity of your approach. Discuss potential optimizations if needed.
Walk through a small example to verify correctness. Mention how you would test the solution.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.