Databricks SQL interview questions
Lakehouse and pipeline work — Spark-style transforms and data modeling. Practice reconstructed Databricks-style SQL, Python and pandas interview questions below — run and grade them instantly in your browser.
pandassqlpythoneasy4medium8hard6
typical interview loop
- 1.Recruiter screen
- 2.Technical screen: SQL plus a Python data-manipulation exercise
- 3.Onsite: data modeling / pipeline design, coding, behavioral
- › pandas / PySpark reshaping and joins are central.
- › Be ready to discuss partitioning and incremental loads.
most-tested topics
ratios
3
groupby
2
GROUP BY
2
JOIN
2
CASE
2
filtering
2
Based on 18 reconstructed Databricks-style questions.
practice path
1 free · 17 Protype
level
- 1Average runtime per jobpandaseasy
- 2Delta size per catalogsqleasy
- 3Parse a cluster specpythoneasy
- 4Runs per jobsqleasy
- 5Cluster compute hourssqlmedium
- 6Job failure ratesqlmedium
- 7Retry hotspotspandasmedium
- 8Retry with exponential backoffpythonmedium
- 9Small file ratio per catalogpandasmedium
- 10Spot versus on-demand spendsqlmedium
- 11Stale vacuum auditsqlmedium
- 12Tables needing compactionsqlmedium
- 13Compact small filespythonhard
- 14Cost per successful runsqlhard
- 15Daily pipeline healthpandashard
- 16Recursive lineage descendantssqlhard
- 17Run duration trend per jobsqlhard
- 18Topological order of a pipelinepythonhard
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