Product Manager question bank
100 questions. Filter by level and skill, track what you've solved, and see how many people get each one right.
- A stakeholder brings a feature idea
- What is an MVP
- North Star metric
- Writing a good user story
- MoSCoW prioritisation
- Spot the vanity metric
- What a product manager owns
- A good customer interview question
- Product manager or project manager
- A good problem statement
- The core idea of Jobs to be Done
- Acceptance criteria format
- Definition of done
- Why refine the backlog
- The sprint goal
- What story points measure
- The Scrum Master role
- Calculating conversion rate
- Calculating churn
- DAU/MAU stickiness
- The AARRR funnel
- What activation means
- Leading and lagging indicators
- Using support tickets
- Testing before building
- The control group
- Writing a hypothesis
- Why use feature flags
- Why run a beta
- Competitive analysis
- Customer segments
- Calculating NPS
- MRR and ARR
- A now, next, later roadmap
- Comparing features with RICE
- Growing users, falling retention
- What a good PRD contains
- A big client demands a custom feature
- Kano model categories
- Defining success before launch
- An outcome-based roadmap
- A sudden drop in sign-ups
- Working out RICE scores
- Working out ICE scores
- Working out WSJF
- Cost of delay
- Kano categories change over time
- Weighted scoring
- Calculating lifetime value
- CAC payback period
- LTV to CAC ratio
- Reading retention curves
- Finding the leakiest funnel step
- Opportunity solution tree
- Which assumption to test first
- A fake door test
- What a p-value means
- Detecting a smaller effect
- Guardrail metrics
- Checking results every day
- An uneven 50/50 split
- Writing a key result
- Splitting a large story
- Why list non-goals
- Choosing a value metric
- How big a launch
- Design and engineering disagree
- Sales wants an exact date
- The CEO's urgent feature
- A winning test with a hidden cost
- Engineers want time for tech debt
- Sales promised a feature you have not planned
- Choosing a metric for saved searches
- Launching with a known bug
- Daily active users are falling
- Leadership wants an AI chatbot
- Do the wins add up
- Up overall, down everywhere
- Twenty metrics, one winner
- Testing in a two-sided marketplace
- When a target stops working
- A new plan that eats revenue
- Build, buy or partner
- Removing a little-used feature
- A price increase
- From monthly to annual churn
- Growth accounting
- Is the AI feature ready to launch
- Margins on an AI feature
- A growth idea that tricks users
- Two big customers, opposite asks
- What is a real moat
- Reversible and irreversible decisions
- A positive but uncertain result
- How long will the test take
- Retiring an old API version
- A suspicious jump in active users
- Free users and the sales team
- More notifications, more users
- A launch corrupts some customer data