For analysts, data & backend engineers
SQL Interview Help — AI for Joins, Window Functions & Optimization
Free real-time AI for SQL interviews. Joins, GROUP BY and HAVING, window functions, subqueries and CTEs, ranking and running totals, and query optimization. Works live on CoderPad, HackerRank, and DataLemur, and stays screen-share-safe on Zoom, Teams and Google Meet.
The SQL question types you'll face
Most SQL screens recycle a handful of patterns. CoPilot Interview recognizes the pattern and returns a correct query with a one-line explanation.
1. Joins & set logic
INNER vs LEFT vs FULL joins, anti-joins (NOT EXISTS / LEFT JOIN ... IS NULL), and self-joins for hierarchies. The classic trap: counting rows after a one-to-many join. The AI flags fan-out and suggests COUNT(DISTINCT) or pre-aggregation.
2. Aggregation: GROUP BY & HAVING
Per-group metrics, filtering aggregates with HAVING, conditional aggregation (SUM(CASE WHEN ...)), and pivoting. The AI reminds you that WHERE filters rows before grouping and HAVING filters groups after.
3. Window functions
The single highest-signal SQL skill: ROW_NUMBER/RANK/DENSE_RANK for top-N-per-group, LAG/LEAD for period-over-period, and SUM() OVER (...) for running totals. The AI writes the correct PARTITION BY ... ORDER BY frame.
4. Subqueries & CTEs
Readable multi-step logic with WITH clauses, correlated subqueries, and recursive CTEs for hierarchies. The AI refactors a nested mess into named CTEs the interviewer can follow.
5. Optimization & explain
"Make this faster." Indexing, avoiding SELECT *, sargable predicates, and reading a query plan. The AI surfaces the likely bottleneck and the index that fixes it.
Patterns the AI recognizes instantly
| Pattern | Prompt sounds like | The move |
|---|---|---|
| Top-N per group | "2nd highest salary per dept" | ROW_NUMBER() OVER (PARTITION BY dept ORDER BY salary DESC) |
| Period over period | "month-over-month growth" | LAG(metric) OVER (ORDER BY month) |
| Running total | "cumulative revenue" | SUM(x) OVER (ORDER BY date) |
| Retention / cohort | "users active in month N" | self-join or date-bucketed CTEs + DISTINCT users |
| Anti-join | "customers with no orders" | LEFT JOIN ... WHERE o.id IS NULL |
Why CoPilot Interview fits SQL rounds
SQL interviews reward pattern recognition under time pressure — and the gap between a brute-force self-join and a clean window function is exactly what separates a pass from a fail. CoPilot Interview names the pattern, writes the correct frame, and reminds you of the trap (join fan-out, NULL handling, WHERE vs HAVING). It works directly in CoderPad, HackerRank, and DataLemur-style editors, and stays out of your shared screen so you can focus on explaining the query.
Common SQL interview questions
Six prompts that surface in almost every SQL screen, with the move that turns each from a brute-force attempt into a clean, correct query.
- Find the second-highest salary (or the Nth-highest per department). Reach for
ROW_NUMBER()orDENSE_RANK() OVER (PARTITION BY dept ORDER BY salary DESC)and filter the rank —DENSE_RANKif ties should share a place, and mention the empty-result edge case. - What's the difference between an
INNERand aLEFT JOIN, and where does fan-out bite?INNERkeeps only matches;LEFTkeeps all left rows with NULLs for non-matches. The trap is counting after a one-to-many join inflates totals — fix withCOUNT(DISTINCT)or pre-aggregation. - When does
WHEREapply versusHAVING?WHEREfilters individual rows before grouping;HAVINGfilters groups after aggregation. So a condition onSUM()orCOUNT()must live inHAVING, notWHERE. - Compute a running total or a month-over-month change. Use windowed aggregates:
SUM(amount) OVER (ORDER BY date)for a cumulative total, andLAG(metric) OVER (ORDER BY month)to reference the prior period for a delta. - Find customers with no orders. Anti-join it:
LEFT JOIN orders o ... WHERE o.id IS NULL, orNOT EXISTS. AvoidNOT INon a nullable column — a single NULL makes the whole predicate return no rows. - This query is slow — how do you make it faster? Read the query plan first, then target the bottleneck: add an index on the filter/join column, drop
SELECT *, and keep predicates sargable (e.g.col >= '2026-01-01'instead of wrappingcolin a function).
How to prepare for a SQL interview
- Drill window functions until
PARTITION BY ... ORDER BYframes are automatic — ranking, running totals, and period-over-period cover the majority of medium/hard prompts. - Rehearse the trap checklist out loud: join fan-out,
WHEREvsHAVING, and NULL behavior inNOT IN, so you catch them before the interviewer does. - Practice reading an
EXPLAINplan and reasoning about indexes, since "optimize this query" is a near-guaranteed follow-up. - Round out data-role prep with our data science interview help, and run a timed dry run on the AI mock interview to practice talking through each query.
FAQ
Yes - window functions are the highest-signal SQL skill, and CoPilot Interview writes the correct PARTITION BY / ORDER BY frame for top-N-per-group (ROW_NUMBER/RANK), period-over-period (LAG/LEAD), and running totals (SUM() OVER). You explain the logic; it gets the syntax right fast.
Yes. It runs as a separate desktop app, so it works alongside any browser-based SQL editor (CoderPad, HackerRank, DataLemur) and stays off the screen you share.
Yes. For 'make this faster' prompts it surfaces the likely bottleneck - missing index, non-sargable predicate, SELECT *, or a join fan-out - and suggests the fix, plus how to read the query plan.
Yes. It flags the classics: counting rows after a one-to-many join (fan-out), WHERE vs HAVING, NULL behavior in NOT IN, and using COUNT(DISTINCT) when needed.
Yes. SQL questions are well within the free Llama/Qwen models, which respond in 3-5 seconds. The paid tier mainly helps for harder mixed coding + system design loops.
Practice SQL rounds with the free tier
Permanent free tier, no credit card. Windows and macOS. Real-time, screen-share-safe help on Zoom, Teams, Google Meet and more.
Download free