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Youtube·Product Manager·Onsite - Product Sense / Strategy·Senior

Senior
Jun 2026

Summary

YouTube PM interview with a monetization policy question. One question, pretty open-ended, more about how you think through enforcement tradeoffs than having the right answer.

Questions Asked (1)

Q1

You're a PM at YouTube and you've decided to ban explicit language videos from monetization. Walk through how you'd actually implement and enforce that decision.

Pricing & MonetizationProduct StrategyProduct Sense & Ideation
Author's notes

The question sounds like a policy question but it's really a product execution question.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the goal and scope of the ban, then outline a phased implementation plan covering policy definition, detection, enforcement, and appeals. Emphasize cross-functional collaboration and metrics to measure success and mitigate unintended consequences.

Pro tip: Acknowledge the tension between brand safety and creator trust, and propose a transparent, tiered enforcement system with clear communication to creators. This shows you understand platform dynamics and stakeholder management.

1. Define Policy and Scope

Clearly define what constitutes 'explicit language' and which videos are affected (e.g., all videos, only new uploads, or specific categories). Consider edge cases like educational or artistic content.

2. Develop Detection Mechanisms

Leverage automated tools (AI/ML for audio transcription and content analysis) combined with human review for accuracy. Ensure scalability and continuous improvement.

3. Design Enforcement and Appeals

Implement a tiered enforcement system (e.g., demonetization, warnings, strikes) with a clear appeals process for creators. Communicate policy changes well in advance.

4. Roll Out and Monitor

Launch in phases, starting with a pilot to test detection and enforcement. Monitor key metrics like false positive rates, creator impact, and advertiser satisfaction.

5. Iterate and Communicate

Use data to refine policies and tools. Maintain open communication with creators and advertisers to build trust and adapt to feedback.

Key Points to Mention

  • Cross-functional collaboration with legal, policy, engineering, and creator support teams
  • Balancing advertiser brand safety with creator fairness and platform trust
  • Use of machine learning for scalable detection and human review for nuance
  • Importance of a transparent appeals process to reduce creator backlash
  • Metrics to track: false positive/negative rates, creator churn, advertiser retention, and revenue impact
  • Phased rollout to test and learn before full enforcement

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.