Amazon·Software Engineer·Technical Phone Screen·Intermediate

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Jun 2026

Summary

Amazon SWE interview centered on a debugging exercise for their return risk system. You're given a scenario where customers have a risk score based on return history, item value, account age, etc., and the system is supposed to block returns for high-risk cases but isn't working correctly. The task was to find 6 bugs using an AI assistant.

Questions Asked (1)

You're given a customer return risk system that assigns scores based on factors like return history, item value, and account age. High-risk customers should be blocked from returning items, but the system is incorrectly allowing returns in some cases. Using an AI assistant, identify 6 bugs in the system.

Root Cause AnalysisSystem DesignTechnical Trade-offs

Author's notes

This was less of a coding question and more of a structured debugging exercise, which I wasn't expecting.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the system's expected behavior and the observed failure, then systematically trace the scoring and decision logic to identify potential bugs. Use the AI assistant to generate hypotheses about common bug types (e.g., off-by-one, threshold errors, data type issues) and validate them against the code or design. Prioritize bugs that directly cause high-risk customers to be allowed returns.

Pro tip: Demonstrate a structured debugging mindset: always reproduce the issue, isolate variables, and consider both code and configuration errors. Mention that you'd write unit tests for edge cases to prevent regressions.

1. Clarify Requirements and Failure

Confirm the expected behavior: high-risk customers (score above a threshold) should be blocked. Identify specific cases where returns were incorrectly allowed.

2. Review Scoring Logic

Examine how factors like return history, item value, and account age are combined into a score. Look for incorrect weights, missing factors, or normalization errors.

3. Inspect Decision Threshold and Comparison

Check the threshold value and the comparison operator (e.g., > vs >=). Ensure the threshold is correctly applied to block high-risk customers.

4. Validate Data Inputs and Types

Verify that input data (e.g., return history count, item value) are correctly parsed and typed. Look for issues like string-to-number conversion errors or null handling.

5. Check Integration and Edge Cases

Review how the system integrates with other services (e.g., customer database, return processing). Test edge cases like new accounts, high-value items, and multiple recent returns.

Key Points to Mention

Off-by-one errors in threshold comparisons (e.g., using > instead of >=).
Incorrect weighting or missing factors in the risk score calculation.
Data type mismatches (e.g., comparing string to number) leading to unexpected behavior.
Race conditions or stale data when fetching customer return history.
Lack of input validation for null or negative values.
Configuration errors, such as a misconfigured threshold value.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.