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24 questions

  1. 1BiLSTM vs TransformerMediumData· Amazon
  2. 2RAG vs fine-tuningMediumData· APAI PM/DS interview prep
  3. 31M users drop off at 6 monthsMediumData· Netflix
  4. 4Cumulative weekly retention (SQL)MediumData· Pinterest
  5. 5Know your own project inside-outMediumData· Duolingo
  6. 6Measure engagement for a productivity appMediumData· Netflix
  7. 7Urgent decision with incomplete information (Bias for Action)MediumData· Amazon
  8. 8Your ML approach didn't workMediumData· Microsoft
  9. 9Cohort funnel in pandasMediumData· PDPinterest DS take-homes
  10. 10Merge intervals plus ML fundamentals probeMediumData· Pinterest
  11. 11Parse data and compute statisticsMediumData· Scale AI
  12. 12Prompt vs fine-tune vs RAG for a search-and-answer systemMediumData
  13. 13Push notifications before conversionMediumData· Uber
  14. 14Stadium with 3+ consecutive high-attendance days (SQL)MediumData· Netflix
  15. 15Third transaction of every user (SQL)MediumData· Uber
  16. 16Top 3 unique salaries per department (SQL)MediumData· Netflix
  17. 17Two SQL plus one Python on trust & safety dataMediumData· Pinterest
  18. 18Cut inference cost by 60%MediumData
  19. 19Defend your RAG choice and your hallucination evalMediumData
  20. 20Fix a hallucinationMediumData
  21. 21Implement a simple retrieverMediumData
  22. 22AI-assisted coding (MLE 2.0 round)MediumData
  23. 23Logistic regression from scratch (Python)MediumData
  24. 24OOP toy system (MLE 2.0 round)MediumData