Amazon Data Science Engineer Intern Interview Questions

Technical Questions (7)

1. 1.Can you explain the difference between supervised and unsupervised learning and provide examples of when each would be used?

   easy~2 min

   What interviewers look for

   • Clearly define supervised learning as using labeled data to train models for prediction tasks
   • Explain unsupervised learning as finding hidden patterns in unlabeled data without target variables
   • Provide relevant business examples for each type, ideally relating to e-commerce or tech applications
   • Mention specific algorithms or techniques for each category and their appropriate use cases

   Likely follow-ups

   • Can you give me an Amazon-specific example where we might use supervised vs unsupervised learning?
   • What challenges might arise when transitioning from supervised to unsupervised approaches?
   • How would you determine which approach to use for a new business problem?

   Company context

   Amazon employs both supervised learning (recommendation systems, fraud detection) and unsupervised learning (customer segmentation, anomaly detection in AWS) extensively. This fundamental question tests whether candidates understand core ML concepts that underpin Amazon's data science applications and aligns with 'Learn and Be Curious' leadership principle.

2. 2.What are the steps involved in the typical lifecycle of a data science project?

   easy~2 min

   What interviewers look for

   • Outline key phases: problem definition, data collection/exploration, data preprocessing, model development, evaluation, and deployment
   • Emphasize iterative nature and importance of stakeholder communication throughout the process
   • Mention monitoring and maintenance post-deployment, including model performance tracking and updates
   • Include documentation and reproducibility considerations at each stage

   Likely follow-ups

   • Which phase do you think is most commonly underestimated in terms of time and effort?
   • How do you ensure stakeholder alignment during the project lifecycle?
   • What happens after model deployment - how do you maintain and monitor production models?

   Company context

   Amazon's data science projects span from customer recommendations to supply chain optimization, requiring systematic project management approaches. This question evaluates understanding of end-to-end project execution that aligns with Amazon's 'Ownership' and 'Deliver Results' leadership principles, ensuring candidates can manage projects from conception to production.

3. 3.What is the difference between training data, validation data, and test data?

   easy~2 min

   What interviewers look for

   • Clearly explains that training data is used to train the model parameters, validation data is used for hyperparameter tuning and model selection during development, and test data provides unbiased evaluation of final model performance
   • Discusses the importance of data independence and avoiding data leakage between sets, including proper temporal splitting for time-series data and stratified sampling considerations
   • Explains typical split ratios (e.g., 70/15/15 or 80/10/10) and factors that influence these decisions such as dataset size and model complexity
   • Addresses cross-validation techniques and when to use k-fold validation versus holdout validation, especially in scenarios with limited data
   • Mentions production considerations like monitoring for data drift and the need for ongoing validation with fresh data in live systems

   Likely follow-ups

   • How would you handle data splitting when working with time-series data or when you have class imbalance?
   • What would you do if your validation performance is much better than your test performance?
   • How do you decide on the appropriate split ratios for different types of machine learning problems?

   Company context

   Amazon emphasizes this fundamental concept because proper data splitting is crucial for building reliable models that serve millions of customers. With Amazon's focus on Operational Excellence and Customer Obsession, models must perform consistently in production. The company's scale means that poorly validated models can impact customer experience significantly, making robust evaluation practices essential.

4. 4.How do you handle missing or inconsistent data in a dataset before building a model?

   medium~3 min

5. 5.How do you evaluate the performance of a machine learning model, and which metrics do you select for different types of problems such as classification and regression?

   medium~3 min

6. 6.What is overfitting, and what techniques can be used to prevent it?

   medium~3 min

7. 7.How do you determine which features are important for your model?

   medium~3 min