Amazon·Software Engineer·Onsite - Coding / Algorithms·Intermediate

Intermediate

Apr 2026

Summary

Amazon SWE coding round with an AI-assisted debugging twist. You get a full-stack movie recommendation project and have to track down a bug causing the homepage recommendations to not render, but the built-in AI tool can only look at one file at a time, so you're doing most of the detective work yourself.

Questions Asked (1)

You're given a full-stack movie recommendation project. There's a bug somewhere in the codebase causing the recommendation section on the user's homepage to not display. Find and fix it. You have access to an AI assistant, but it can only analyze one file at a time.

Root Cause AnalysisTechnical Trade-offsSystem Design

Author's notes

This one messed with me more than I expected.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the system architecture and data flow for the recommendation section, then systematically narrow down the failure point using the AI assistant to inspect one file at a time. Prioritize high-signal files (entry points, API contracts, error handling) and verify each hypothesis with targeted checks before moving on.

Pro tip: Treat the AI assistant as a focused pair programmer: give it precise questions about each file's role and expected behavior, and always validate its suggestions against the actual runtime behavior or logs. This shows you can leverage tools without blindly trusting them.

1. Clarify the system and symptom

Ask clarifying questions about the tech stack, how the recommendation section is rendered, and what 'not displaying' means (blank, error, missing data). Identify the expected data flow from backend to frontend.

2. Form hypotheses and prioritize files

Based on the data flow, list likely failure points (API endpoint, data fetching logic, component rendering, state management). Rank files by probability and impact to decide which to analyze first with the AI assistant.

3. Analyze files one by one with the AI

For each file, ask the AI to summarize its purpose, identify potential bugs related to the recommendation feature, and suggest specific checks. Use the AI's output to refine your hypotheses and decide the next file.

4. Verify and fix the root cause

Once a suspicious file is identified, confirm the bug by tracing the logic or simulating the failure. Propose a minimal fix that addresses the root cause without introducing regressions, and explain how you would test it.

5. Communicate and prevent recurrence

Summarize the root cause, the fix, and any follow-up actions (e.g., adding tests, monitoring). Discuss how you would prevent similar issues in the future.

Key Points to Mention

Systematic debugging approach: start from symptoms, form hypotheses, and narrow down using data flow.
Effective use of the AI assistant: ask targeted questions, cross-validate its suggestions, and avoid blind acceptance.
Prioritization: focus on high-risk files (entry points, API contracts, error handling) first.
Root cause vs. symptom: ensure the fix addresses the underlying issue, not just the visible failure.
Testing and validation: describe how to verify the fix (unit tests, integration tests, manual checks).
Communication: clearly explain the bug, the fix, and any trade-offs or preventive measures.

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