Product design question
Redesign the city parking experience.
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
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What this question tests
Tests broad product design on an infrastructure-scale problem, requiring the candidate to narrow scope before proposing a solution.
How to approach it
- Narrow scope: focus on finding and paying for street parking in a dense downtown, not garage design or urban planning.
- Segment on daily commuters circling for spots versus occasional visitors unfamiliar with the area.
- Identify the pain point: drivers can't see real-time spot availability and waste time circling, adding congestion and emissions.
- Design a real-time availability app using sensor or crowd-sourced data, with pre-booking for a fee and dynamic pricing by demand.
- Define success as average time-to-park, reduction in circling-related traffic, and revenue captured through dynamic pricing.
What a strong answer includes
- Grounds the design in real precedent, SFpark's dynamic pricing pilot, to show feasibility.
- Picks a measurable proxy for the real problem, time-to-park, instead of a vague better-parking answer.
- Addresses the city and driver both, revenue for the city, time saved for the driver.
Common mistakes
- Trying to redesign all of urban parking infrastructure instead of scoping to one solvable slice.
- Ignoring the equity concern of pricing out lower-income drivers from prime spots.
Likely follow-up questions
- How would you get accurate real-time occupancy data without expensive sensors everywhere?
- How would you handle the equity concern of dynamic pricing?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 7: AI UX and human oversight: design for a system that is wrong sometimes
- Chapter 14: Get the job: the AI PM interview loop