4 free questions from a 20-question segment. Get the full 100 →

Q5. How do you decide when NOT to use AI, even if it's technically possible?

• Type: Case | Difficulty: Medium
• What they're testing: Judgment about AI's limits. Great PMs know when the answer is don't use AI here.
• Framework hint:
  • Deterministic requirement — use rules
  • High-stakes reversibility
  • Small segment doesn't justify cost
  • Simple UX solves 80%
  • Explainability legally required

Q11. A PM proposes adding AI summarization to our email product. What questions do you ask before saying yes?

• Type: Case | Difficulty: Easy
• What they're testing: Whether you challenge AI feature requests through user-value first, or approve because AI = good.
• Framework hint:
  • User problem validated?
  • Frequency of long emails?
  • Failure cost of missed key point?
  • AI better than existing solutions?
  • Cost/latency vs value?

Q14. You have 3 use cases where AI could help — a chatbot, a recommender, and a summarizer. Which do you build first?

• Type: Case | Difficulty: Medium
• What they're testing: Applied prioritization with concrete options. Watch for candidates who pick based on cool not on impact/feasibility.
• Framework hint:
  • User pain × frequency × feasibility × data
  • Recommender wins on data flywheel
  • Summarizer lowest risk
  • Chatbot highest visibility but hardest
  • Justify with framework, ask for more data

Q17. Your CEO wants to add AI to the product. How do you push back or scope it?

• Type: Case | Difficulty: Medium
• What they're testing: Stakeholder management + product judgment under exec pressure to ship AI theatre.
• Framework hint:
  • Never say no flat
  • Understand underlying driver
  • Propose 2-3 ranked opportunities
  • One pilot with clear success + kill criteria
  • Set realistic timeline expectations

🔓 16 more questions in this segment alone — including agentic workflow design, sunsetting AI features, and 10x vs 10% opportunity framing.