Product design question

Design a personalized radio station for YouTube Music.

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Product design for a personalization feature, defining inputs, the experience, and how success is measured.

Clarify the goal, passive discovery for casual listeners versus deep personalization for music enthusiasts.
Identify inputs, listening history, likes and skips, time of day, and possibly video watch behavior since this is YouTube Music.
Design the core experience, an endless auto-playing station that starts from a seed song, artist, or mood and adapts based on skip behavior.
Differentiate from a static playlist, the station should evolve in real time rather than being a fixed list.
Address cold start, for new users with little history, seed the station from onboarding genre picks or trending tracks.
Define success metrics, average session length on the station and skip rate as a proxy for recommendation quality.

Uses YouTube-specific signals, watch history and engagement on official audio and music videos, not just generic listening data.
Addresses the cold start problem explicitly with an onboarding-based seed, rather than assuming rich history exists for every user.
Treats skip rate as a real-time feedback signal for personalization, not just a vanity metric to report later.
Sets a clear success metric, session length or return rate to the station, tied directly to the personalization goal.

Chapters of the AI PM course that teach what this question tests.