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

Q61. What adoption problems typically occur in AI products and how do you navigate them?

• Type: Case | Difficulty: Medium
• What they're testing: Whether you know AI-specific adoption failure modes vs generic engagement problems.
• Framework hint:
  • First-use failure — one bad output kills the account
  • Trust decay as edge cases emerge
  • Prompt anxiety
  • Novelty vs habit — 20% use once, 2% integrate
  • Better onboarding, scaffolding, quality gates

Q63. Users are afraid the AI will make mistakes. How do you design for trust?

• Type: Case | Difficulty: Medium
• What they're testing: Trust design as a first-class product problem, not a marketing message.
• Framework hint:
  • Show confidence differently by level
  • Reversibility of every AI action
  • Explainability — "AI suggested this because..."
  • User control: accept, edit, reject
  • Never claim 100% accuracy

Q64. Your AI feature launched with 30% adoption week 1, dropped to 8% by week 6. What's happening and what do you do?

• Type: Case | Difficulty: Hard
• What they're testing: Diagnosing AI-specific drop-off patterns. Watch for candidates who default to generic engagement analysis.
• Framework hint:
  • Novelty effect — 20-30% try once
  • Cohort analysis for power users
  • First-use quality correlation with churn
  • Trust erosion over time
  • Fix onboarding, first output, failure salience

Q74. Your AI feature caused a public incident (wrong output at scale). Walk me through the first 48 hours.

• Type: Case | Difficulty: Hard
• What they're testing: Incident response for AI at scale. Reveals how many times they've done it.
• Framework hint:
  • Hour 0-2: kill switch, blast radius, user comm
  • Hour 2-12: root cause, internal + exec brief
  • Hour 12-24: public statement, outreach, remediation
  • Hour 24-48: fix rollout, post-mortem, monitoring
  • Long-term: evals, guardrails, review changes

🔓 16 more questions in this segment alone — including enterprise trust rollout, regulatory patchwork, and hostile-user adoption stories.