Strategy question
How would you source the data for potholes on the road to improve the driving experience? Any requirements on how fresh the data needs to be?
- Strategy
- Medium
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What this question tests
Tests data-strategy thinking: identifying sourcing options and reasoning about the right freshness-versus-cost trade-off.
How to approach it
- Clarify the use case: real-time driving alerts versus longer-term road-maintenance reporting, since needs differ.
- List sourcing options: crowdsourced driver reports, sensor or dashcam-based detection, municipal public works data.
- Weigh trade-offs: crowdsourced is fast and cheap but sparse and unverified; sensor detection is accurate but costlier.
- Recommend combining sources: sensor-based passive detection cross-validated by crowdsourced reports for coverage.
- Tie freshness to the use case: driving alerts need data days old at most; maintenance planning can use weekly data.
- Define a quality guardrail: false-positive rate, since bad pothole data erodes trust in the alert fast.
What a strong answer includes
- Ties the freshness requirement explicitly to the use case instead of picking an arbitrary number.
- Proposes a specific, realistic combination of sourcing methods rather than one alone.
- Names a concrete quality guardrail, since bad data is worse than no data for this feature.
Common mistakes
- Giving a freshness number with no reasoning tied to how the data will be used.
- Relying on a single data source without weighing its weaknesses.
Likely follow-up questions
- How would you validate crowdsourced reports are accurate?
- How would you prioritize which roads to detect first?
- What would you do about potholes that are fixed but still show in the data?
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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 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 14: Get the job: the AI PM interview loop