Amazon
E-commerce and cloud computing giant with AWS, the world's leading cloud platform.
5 Rounds
~28 Days
Very Hard
The Interview Loop
Recruiter Screen (30 min)
Standard fit check, behavioral questions, and resume overview.
Technical Loop (3-4 Rounds)
Deep dive into domain knowledge, coding, and system design.
Interview Question Bank
Data Scientist
•
Technical
•
hard
Explain gradient boosting. How does XGBoost differ from a standard gradient boosting machine?
#Gradient Boosting
#XGBoost
Data Scientist
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Technical
•
medium
How does a Random Forest work? What are its hyperparameters and how do you tune them?
#Random Forest
#Hyperparameter Tuning
Data Scientist
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Technical
•
medium
What is regularization? Explain L1 vs L2 regularization and their effects.
#Regularization
#L1
#L2
Data Scientist
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Technical
•
medium
How do you handle class imbalance in a classification problem?
#Imbalanced Data
#SMOTE
Machine Learning Engineer
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Technical
•
hard
Explain how you would fine-tune a Large Language Model for Amazon's customer service chatbot (Rufus) using LoRA. What are the mathematical and computational trade-offs compared to full fine-tuning?
#LLMs
#PEFT
#LoRA
#NLP
Machine Learning Engineer
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Technical
•
medium
You are building a click-through rate (CTR) prediction model for Amazon Sponsored Ads. The baseline click rate is less than 0.5%. What loss function and evaluation metrics would you use, and why?
#CTR Prediction
#Imbalanced Data
#Evaluation Metrics
#Loss Functions
Machine Learning Engineer
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Technical
•
medium
How does the self-attention mechanism work in Transformer models? Explain the mathematical operations involving Query, Key, and Value matrices.
#Deep Learning
#Transformers
#Attention Mechanism
#Mathematics
Difficulty Radar
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Senior EngineerFocuses on core competencies, system constraints, and clear communication.
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Think Out Loud
Always explain your thought process before writing code or drawing architecture.