Scale.ai·Software Engineer·Take-home Assignment·Intermediate

Intermediate

Jun 2026

Summary

Scale.ai SWE take-home where you're handed a broken pipeline and told to fix it. Pretty hands-on debugging exercise, nothing abstract about it.

Questions Asked (1)

You're given a project-assignment pipeline where a record-filter step is using the wrong field key, causing unrelated records to slip through. Trace the data flow, fix the bug, write a regression test, and confirm downstream logic only sees the correct rows.

Root Cause AnalysisAPI & IntegrationsTechnical Trade-offs

Author's notes

The actual bug was pretty small once you found it, a single wrong field name in the filter.

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AI HintsAI Generated

Suggested Approach

Start by tracing the data flow from ingestion to the filter step, identifying where the wrong field key is used and how it causes unrelated records to pass. Then fix the key, write a regression test that reproduces the bug and verifies the correct filtering, and confirm downstream logic receives only the intended rows. Finally, discuss how you would prevent similar issues, such as adding validation or monitoring.

Pro tip: Demonstrate a systematic debugging approach by first reproducing the bug with a minimal test case, then fixing it, and finally verifying the fix with the regression test and downstream checks. This shows you not only solve the immediate problem but also prevent future regressions.

1. Trace the data flow

Map the pipeline from source to filter, identifying the schema and the exact field key used in the filter condition. Compare with expected key to spot the mismatch.

2. Reproduce and isolate the bug

Create a minimal test case or use existing data to reproduce the issue, confirming that unrelated records pass due to the wrong key. Isolate the faulty filter logic.

3. Fix the bug

Correct the field key in the filter step, ensuring it matches the intended schema. Consider adding validation or logging to catch similar mismatches.

4. Write a regression test

Develop a test that fails with the old code and passes with the fix, covering edge cases like missing keys or unexpected values. Ensure it runs in CI.

5. Verify downstream logic

Confirm that after the fix, only correct rows reach downstream steps. Check logs, metrics, or run integration tests to ensure no unintended side effects.

Key Points to Mention

Root cause analysis: identifying the wrong field key and its impact on filtering.
Data flow tracing: understanding the pipeline stages and schema contracts.
Regression testing: writing a test that specifically targets the bug and prevents recurrence.
Downstream validation: ensuring the fix doesn't break or alter expected behavior in later stages.
Preventive measures: adding schema validation, type checks, or monitoring to catch similar issues.
Communication: documenting the fix and sharing learnings with the team to avoid similar mistakes.

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