Amazon·Software Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Amazon SWE interview with an AI-assisted debugging round focused on a return-eligibility service that was incorrectly approving high-risk return requests. The problem was interesting but the breadth of what they expected was a lot to cover in one session.

Questions Asked (1)

You're working on an e-commerce return-eligibility service that uses a risk score to decide whether items can be returned. The system has recently been approving returns it shouldn't. Walk through how you'd investigate this, and identify six concrete categories of defects that could cause false approvals, covering detection, fix, and prevention for each.

Root Cause AnalysisSystem DesignTechnical Trade-offs

Author's notes

This one took me a second to orient.

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AI HintsAI Generated

Suggested Approach

Start by outlining a systematic investigation: confirm the symptom, gather data, trace the decision path, and identify root causes. Then, for each of six defect categories, explain how you would detect, fix, and prevent it, emphasizing monitoring, testing, and safeguards.

Pro tip: Prioritize defects by business impact and likelihood, and propose a short-term mitigation (e.g., tightening thresholds) while working on long-term fixes to show pragmatism.

1. Confirm and Scope the Issue

Verify the false approvals with concrete examples, quantify the impact, and determine when the issue started.

2. Trace the Decision Path

Follow the risk score calculation from data inputs through model inference to the final approval decision, logging intermediate values.

3. Identify Root Causes

Analyze the traced data to pinpoint defects in data, model, code, configuration, integration, or monitoring.

4. Categorize Defects and Address Each

For each defect category, define detection methods, implement fixes, and establish preventive measures.

5. Validate and Monitor

Test fixes with backtesting and canary releases, and set up ongoing monitoring to catch regressions.

Key Points to Mention

Data quality issues: stale, missing, or incorrect input features leading to skewed risk scores.
Model drift or training-serving skew: model performance degrading over time or mismatched feature engineering.
Code bugs: logic errors in threshold comparison, off-by-one errors, or incorrect handling of edge cases.
Configuration errors: misconfigured thresholds, feature flags, or environment-specific settings.
Integration failures: API timeouts, fallback to default approval, or incorrect error handling.
Monitoring gaps: lack of alerting on approval rates, score distributions, or business metrics.

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