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Got it working in about 15 minutes and it passed their tests.
Start by clarifying the requirements and constraints of the A/B testing algorithm, such as the input format, expected output, and any assumptions about the available functions. Then, outline a high-level design that covers assignment, bucketing, and metrics collection, and finally implement the core logic using the provided functions, ensuring modularity and testability.
Pro tip: Demonstrate product thinking by discussing how the algorithm supports experimentation velocity and data-driven decisions, and mention edge cases like user consistency and sample ratio mismatch.
Ask questions to understand the scope: what are the available functions? What are the inputs and outputs? Are we assigning users to variants, collecting metrics, or both? What are the constraints (e.g., deterministic assignment, scalability)?
Outline a high-level approach: use a hash function to deterministically assign users to buckets, ensure even distribution, and handle multiple experiments. Consider how to integrate with the provided functions for logging and metrics.
Write pseudocode or actual code for the assignment mechanism, using consistent hashing or modulo operations. Ensure that the same user always gets the same variant for a given experiment.
Discuss how to handle edge cases like new users, experiment changes, and sample ratio mismatch. Include validation to ensure the algorithm works as expected, such as unit tests for distribution.
Describe how you would test the algorithm with simulated data, measure performance, and iterate based on results. Mention any trade-offs made and potential improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.