Product design question
How should Yelp identify closed restaurants?
- Yelp
- Product design
- Medium
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
Product improvement thinking with a data quality angle: can you design a system to detect stale listings without over relying on manual reports.
How to approach it
- State the problem: closed restaurants staying listed hurts user trust and wastes visits.
- List signals of closure: no new reviews or check ins for months, phone number disconnected, business hours never updated, and user reported closures.
- Combine passive signals (activity data) with active ones (a simple is this still open prompt to recent visitors) rather than relying on one source.
- Weight signals by reliability, since a single user report is weaker evidence than three independent reports plus no recent activity.
- Design the resolution flow: auto flag for review at a threshold, then verify before fully delisting to avoid wrongly closing an active business.
- Define success as time to detect a closure and false closure rate.
What a strong answer includes
- Combines multiple weak signals into a confidence score rather than trusting any single source alone.
- Protects against false positives, since incorrectly marking a business closed causes real harm.
- Picks a concrete detection time metric, like median days from closure to flag.
Common mistakes
- Relying only on user reports, which are sparse and easy to abuse.
- No safeguard against false positives that could hurt a still open business.
Likely follow-up questions
- How would you prevent a competitor from falsely reporting a business as closed?
- What would you do differently for a high traffic city versus a rural area?
More product design questions
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- How would you add a food item to a menu in a restaurant?Yelp · Product design · Easy
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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