Master the SQL patterns Databricks asks across Data Engineer, Analytics Engineer, and Solutions Engineer roles. Practice Spark SQL, partition strategy, and ETL patterns with real datasets and AI tutoring.
20 challenges
Databricks patterns
Real datasets
ETL & event streams
AI tutor
Step-by-step hints
What this page is: we have no dated public source for how Databricks runs its SQL round, so this page does not state one — no duration, no platform, no stage list. What follows is general SQL interview practice on the kind of data Databricks works with. What is ours: the questions are SQL Quest challenges and the topic emphasis is our editorial judgement, not a measured breakdown of Databricks's interview. Pages with dated sources say so in this spot.
The 20 challenges tagged Databricks in the SQL Quest bank, with every raw challenge tag resolved to the 9 canonical skills. Each share is the portion of those 20 challenges that exercise the skill — a challenge exercises several, so the shares do not sum to 100%. This is the composition of the practice set on this page, not a measurement of Databricks’s interview.
These are SQL Quest challenges chosen because their SQL matches the work — Querying Basics, Aggregation & Grouping, Subqueries & CTEs. They are not questions Databricks has asked, and this page does not claim to know its questions. All of the six play free; each card opens the challenge itself.
General guidance about analytics SQL work — not sourced from Databricks and not a description of its process. We have no dated, citable source for how Databricks runs its SQL round, so this page states none.
Skillmap
Ten questions, no signup. You get a readiness score weighted to the SQL this page covers, your Skillmap across joins, window functions, aggregation and the rest, and the weakest skill to practise first.
Drill the skills the Databricks set leans on, one at a time: GROUP BY exercises · CTE practice · Window function practice · JOIN practice · CASE WHEN practice — or browse every SQL practice question.
Every question in the Databricks set, one page each with the schema and a hint: Pivot: Order Status by Country · Below Department Average · UNION ALL Dedup: Cross-Dataset Search · Conditional Counting with CASE · Month-over-Month Customer Growth · Year-over-Year Growth · 7-Day Rolling Revenue Average · First and Last Order per Customer · Moving Average with Dynamic Window · Recursive Team Size Rollup · Salary Lead-Lag Gap Within Department · Genres in Common (INTERSECT) · 3-Movie Rolling Average Revenue · Engagement Streaks (3+ Orders, ≤7-Day Gaps) · Anti-Join Pipeline: Unmatched Records · Sliding Window Max Revenue · Recursive Org Chart Traversal · Month-over-Month Revenue Growth · Island Length Classification · Year-over-Year Movie Rating Trends.
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Interviewing at more than one company? The same patterns carry: Snowflake · Tesla · NVIDIA — or the full company-by-company interview guide.