Product Sense and Decision Making: Street View Images and Fair Delivery
You are in a Product Manager interview. Work through two separate product prompts and make your assumptions explicit.
Constraints & Assumptions
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For the Street View prompt, assume vehicle images are historical Street View imagery, not live surveillance.
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Assume standard privacy protections such as face and license-plate blurring are already in place, but still discuss data minimization and user trust.
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For the delivery prompt, assume a 3-sided marketplace with customers, couriers, and restaurants.
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You may choose a specific geography or launch segment if it helps make the MVP concrete.
Clarifying Questions to Ask
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What company goal is the interviewer optimizing for: revenue, user growth, operational efficiency, equity, or trust?
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Are there legal, policy, or data-use constraints that rule out certain Street View applications?
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For the delivery platform, which side of the marketplace is currently most constrained?
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Are we designing a new product from scratch or improving an existing platform?
Part 1 - Street View Car Images
If you had access to all car images captured by Google Maps Street View, what new product or feature would you build?
Describe the target users, value proposition, MVP scope, launch plan, success metrics, and ethical or privacy considerations.
What This Part Should Cover
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A specific product idea, not just a broad use of computer vision.
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A clear user segment and painful problem.
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Why Street View vehicle imagery is uniquely useful for the idea.
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MVP scope, including first geography, minimum model/data requirements, and what is intentionally excluded.
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Metrics for adoption, utility, accuracy, trust, and privacy risk.
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Privacy, fairness, consent, retention, and misuse considerations.
Part 2 - Fair Food-Delivery Platform
Create a food-delivery platform that is fair to both couriers and customers.
Define what "fair" means, outline the product vision, describe marketplace mechanics, propose incentive structures, and give a phased rollout plan.
What This Part Should Cover
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A concrete fairness definition for couriers and customers, including trade-offs when their goals conflict.
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Core user journeys for customers, couriers, and restaurants.
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Marketplace mechanics for matching, batching, pricing, cancellations, support, and dispute resolution.
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Incentives that avoid pushing hidden costs onto any one side of the marketplace.
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Launch sequencing, experiments, guardrails, and long-term health metrics.
What a Strong Answer Covers
A strong answer frames the problem, chooses concrete users, makes principled trade-offs, and explains how the PM would validate the product with data. It should connect strategy to execution: MVP, metrics, operational risks, trust and safety, and phased rollout.
Follow-up Questions
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How would your Street View product change if the model accuracy were only 80%?
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What privacy review would you require before launch?
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If courier fairness improves but customer ETAs get worse, how would you decide whether to ship?
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Which metric would you make the north star for the delivery platform, and why?