Metrics question
Design a COVID App that helps people with all their needs related to COVID with all the possible Use Cases.
- Metrics
- Hard
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
Prioritization and metrics discipline on a sprawling, easily scope-creeping problem that invites an unfocused answer.
How to approach it
- Push back on 'all use cases' first, stating you'd scope to the two or three highest-impact use cases rather than build an unfocused everything-app.
- Propose candidates: symptom-checking guidance, vaccination or test record verification, and local case-rate or exposure alerts.
- Prioritize by public-health impact and urgency: exposure awareness and verified health-status credentials as the top two.
- For each chosen use case, define its own metric: exposure-alert use, symptom-checker adoption, and verified-credential usage.
- Define an overall north star as adoption and active-use rate as a proxy, since direct transmission attribution is hard to measure.
- Add a guardrail: privacy and trust metrics, opt-out rate and data-deletion requests, since success depends entirely on public trust and voluntary adoption.
What a strong answer includes
- Explicitly refuses the sprawling 'all use cases' framing and scopes down, the key judgment call this ambiguous prompt rewards.
- Names concrete, real-world-grounded use cases, exposure alerts and verified credentials, similar to real COVID apps that existed.
- Adds privacy and trust as an explicit guardrail, since these apps' real-world failure mode was mostly low adoption due to distrust.
Common mistakes
- Trying to design and metric every conceivable use case, producing an unfocused answer.
- Ignoring privacy and trust, the actual reason many real-world COVID apps saw low adoption.
Likely follow-up questions
- How would you convince a skeptical public to adopt this app?
- Which single metric would you report weekly to a public health official?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 14: Get the job: the AI PM interview loop