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AI Product Strategy and Model Choice

AI Data Strategy and Data Moats Interview Questions

Why data decisions are product decisions: paying for annotation, building a data flywheel, and judging whether proprietary data is really a moat. Relevant to AI PMs, data scientists and strategy roles.

  • 23 questions with model answers
  • 1 foundational · 9 intermediate · 13 advanced

Foundational AI Data Strategy and Data Moats Questions

  1. Explain why the data you collect today determines the products you can build in two years.Concept

Intermediate AI Data Strategy and Data Moats Questions

  1. What is a data flywheel and what are its preconditions?Concept
  2. Your company has ten years of unstructured documents. Is that an asset? Interrogate the claim.Case
  3. What are the product implications of not owning your own data?Concept
  4. Describe the difference between data volume, data quality and data relevance for AI products.Concept
  5. What data would you need to collect before you could personalize an AI feature?Case
  6. What is the product argument for paying for human annotation?Concept
  7. Explain how data strategy differs when you are prompting rather than training.Concept
  8. Describe the minimum eval dataset you need before shipping anything.Concept
  9. What data would you stop collecting, and why?Concept

Advanced AI Data Strategy and Data Moats Questions

  1. Describe how you would instrument a product to generate training or eval data as a byproduct.Case
  2. How do you evaluate whether proprietary data is actually a moat?Concept
  3. How would you build consent and licensing into a data collection strategy from day one?Case
  4. Explain the risk of a feedback loop where the model's outputs become its own training data.Concept
  5. Design a labelling strategy for a feature launching in eight weeks.Case
  6. How do you prioritize data investment against feature investment on a roadmap?Case
  7. Describe how synthetic data changes the calculus of a cold-start problem.Concept
  8. How do you handle a customer who wants their data excluded from all improvement loops?Case
  9. What does a data moat look like in an era of general-purpose models?Concept
  10. How do you measure whether your data flywheel is actually turning?Concept
  11. Write the section of a strategy doc that argues for a data investment with no immediate feature payoff.Artifact critique
  12. Explain the tension between privacy commitments and model improvement.Concept
  13. Tell me what data a company I name should be collecting today and is probably not.Interview

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