NVIDIA's data teams live inside telemetry and supply-chain firehoses — its interviews screen for exactly that. Practice rolling averages, percentile latency, top-N per product line, and clean time-series SQL on runnable datasets with AI tutoring.
23 challenges
NVIDIA-pattern set
Real datasets
Telemetry-shaped tables
AI tutor
Step-by-step hints
What this page is: we have no dated public source for how NVIDIA 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 NVIDIA works with. What is ours: the questions are SQL Quest challenges and the topic emphasis is our editorial judgement, not a measured breakdown of NVIDIA's interview. Pages with dated sources say so in this spot.
The 23 challenges tagged NVIDIA in the SQL Quest bank, with every raw challenge tag resolved to the 9 canonical skills. Each share is the portion of those 23 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 NVIDIA’s interview.
These are SQL Quest challenges chosen because their SQL matches the work — Querying Basics, Window Functions, Subqueries & CTEs. They are not questions NVIDIA 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 NVIDIA and not a description of its process. We have no dated, citable source for how NVIDIA 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 NVIDIA set leans on, one at a time: Window function practice · CTE practice · GROUP BY exercises · JOIN practice · CASE WHEN practice — or browse every SQL practice question.
Every question in the NVIDIA set, one page each with the schema and a hint: Your First Window Function · RANK vs DENSE_RANK Side-by-Side · Top 3 Salary Tiers (DENSE_RANK) · Most Recent Order Per Customer (ROW_NUMBER) · Second-Highest Earner Per Department (ROW_NUMBER) · Salary vs Department Average (PARTITION BY) · Running Total of Orders · Salary Rank Within Department · Running Total Revenue · Year-over-Year Growth · 7-Day Rolling Revenue Average · Salary Percentile Ranking · Moving Average with Dynamic Window · Nth Highest Salary per Department · Salary Lead-Lag Gap Within Department · 3-Movie Rolling Average Revenue · Top-N Products per Category · Median Salary Without PERCENTILE · Sliding Window Max Revenue · Month-over-Month Revenue Growth · Department Salary Percentile Buckets · Second Highest Salary per Department · Year-over-Year Movie Rating Trends.
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Interviewing at more than one company? The same patterns carry: Snowflake · Databricks · Tesla — or the full company-by-company interview guide.