Product design question
How would you improve Facebook’s search monetization?
- Meta
- Product design
- 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
Product design for a monetization surface, balancing ad revenue growth against user search experience and trust.
How to approach it
- Confirm scope: search ads specifically, distinct from feed or Stories ads.
- Identify the tension: more ad inventory in search results risks degrading result relevance and user trust.
- Segment intent: users searching for people or content versus users searching for products or businesses.
- Prioritize monetizing commercial intent searches, such as product or business queries, where ads are more relevant to the user's goal.
- Propose clearly labeled sponsored results shown only for commercial intent queries, not personal searches.
- Define success with ad revenue per search alongside a guardrail on search satisfaction and click through on organic results.
What a strong answer includes
- Separates commercial intent searches from personal ones, monetizing only where ads add relevant value.
- Names a concrete guardrail, organic result click through rate, so monetization is not measured in isolation from user trust.
- Proposes a specific mechanism, clearly labeled sponsored results limited to commercial queries, not blanket ad insertion.
- Sets a measurable target, ad revenue per search, tied directly to the monetization goal.
Common mistakes
- Proposes inserting ads into all search results regardless of intent, risking user trust broadly.
- Has no guardrail metric to catch a decline in search satisfaction.
Likely follow-up questions
- How would you detect commercial intent reliably from a search query?
- How would you price these ads relative to Meta's existing feed ad inventory?
- How would you handle advertiser complaints about low visibility if the ad load stays conservative?
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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