Behavioral Questions:
1. Describe a time you used data to solve a complex business problem.
2. How do you handle conflicting priorities in a high-pressure environment?
3. Give an example of a project where you demonstrated Amazon’s “Customer Obsession” principle.
4. Tell me about a time you failed. How did you handle it?
5. How do you prioritize tasks when working with tight deadlines?
Technical and Analytical Questions:
6. How would you handle missing data in a large dataset?
7. Explain the assumptions behind linear regression.
8. How do you evaluate the performance of a machine-learning model?
9. What are the pros and cons of using random forests over gradient-boosting models?
10. How would you design an A/B test for a new feature on Amazon’s website?
SQL and Data Query Questions:
11. Write a query to find the top 5 products with the highest sales.
12. How would you calculate the month-over-month revenue growth in SQL?
13. Write a query to identify customers who made more than three purchases in the last year.
14. How would you optimize a slow-running SQL query?
Machine Learning and System Design:
15. Explain how you would design a recommendation system for Amazon’s e-commerce platform.
16. What is the difference between bagging and boosting in ensemble methods?
17. How would you detect and handle multicollinearity in a dataset?
18. Describe how you would approach building a fraud detection system.
19. Problem-Solving and Scenario-Based Questions:
20. How would you approach predicting inventory demand for Amazon during the holiday season?You have 1TB of customer transaction data. How would you preprocess and analyze this dataset efficiently?
