OpenAI data interview guide
Usage, tokens and API analytics with production-quality Python.
typical interview loop
- 1.Recruiter screen
- 2.Technical screen: SQL plus a Python data-manipulation exercise
- 3.Onsite: data modeling / pipeline design, coding, behavioral
- › Write readable, well-tested Python.
- › Expect usage aggregation and rate-limit style questions.
most-tested topics
JOIN
3
ratios
3
window functions
2
CASE
2
GROUP BY
2
thresholds
1
Based on 16 reconstructed OpenAI-style questions.
practice path
0 free · 16 Pro- 1Accounts near their token limitsqlMedium
- 2Average latency by modelsqlMedium
- 3Daily spend with running totalsqlHard
- 4Error and rate-limit ratesqlMedium
- 5Estimate token costpythonEasy
- 6Fine-tune success ratesqlMedium
- 7Heaviest request per accountsqlHard
- 8Latency percentiles per modelpandasHard
- 9Output-to-input token ratiosqlHard
- 10Prompt cache hit ratiopythonMedium
- 11Requests per modelsqlEasy
- 12Sliding window rate limiterpythonHard
- 13Spend by organisationpandasMedium
- 14Spend per modelsqlMedium
- 15Status breakdown sharepandasMedium
- 16Total tokens per organisationsqlEasy
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