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Why These 25?

Hand-curated for maximum interview ROI.

Subqueries First, CTEs Second

A deliberate inversion of how most courses teach this. You start with EXISTS, NOT IN, scalar and correlated subqueries — then graduate to single CTE → multi-CTE → multi-step business scorecards. You learn why CTEs win.

Multi-CTE Business Scorecards

The capstone stage drills the 2–4 CTE multi-step pipelines that appear in real DS/DA onsite questions — CTE + window function combos, set intersection/difference, multi-tier classification. Industry-grade schemas, real ambiguity, the actual interview shape.

Both Dialects, Same Editor

Toggle SQLite and PostgreSQL on any problem. CTE syntax is portable, but the small dialect quirks (aggregation in HAVING, type coercion in scalar subqueries) catch candidates off guard in real interviews — you see them side-by-side here.

Skill Coverage

How the 25 problems distribute across SQL topics.

Scalar Subqueries (WHERE/HAVING)
5
EXISTS / NOT EXISTS
4
NOT IN Subquery
1
Single-CTE Reference Values
4
Two-CTE Pipelines
6
Multi-CTE (3+) Pipelines
2
CTE + Window Function Combos
2
Multi-CTE Classification Scorecards
1

FAQ

Helpful but not required.

If you can write basic SELECT/JOIN/GROUP BY and have seen the WITH keyword in passing, start here.

Stage 1 begins with simple scalar subqueries to ease you in.

Ready to Master SQL?

Start with Stage 1 — graded instantly in your browser.

Start Problem 1Browse All Problems

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LDS SQL CTE & Subqueries 25 — 25 Curated SQL Problems

A 5-stage progression from scalar subqueries and EXISTS / NOT EXISTS through single-CTE reference-value patterns up to the multi-CTE business scorecard you'll write at staff level. Twenty-five problems on 15 production-grade schemas — the query shape that turns a raw fact table into a board-ready metric in one statement.

Problems included in LDS SQL CTE & Subqueries 25

  1. Campaigns Above Average Spend
  2. Portfolios Above Average Value
  3. Restaurants Above Average Rating
  4. Merchants Above Average Charge Amount
  5. Properties Priced Above Average
  6. Providers with Denied Claims
  7. Guests Booking Direct and OTA
  8. Riders Who Used Economy and Premium
  9. Watchlisted but Never Watched
  10. Plans Never Subscribed
  11. Properties Above Average Revenue
  12. Public Posts Above Average Reactions
  13. Customers With Above-Average Daily Usage
  14. Securities Above Sector Average P/E
  15. Categories With Above-Average Revenue
  16. Delinquent Customers With No Suspicious Activity
  17. Users With Positions But No Recent Trades
  18. Refund Reason Analysis
  19. Products Outperforming Average Revenue
  20. High Ticket Volume Customers
  21. Campaign ROAS by Attribution Model
  22. Property Investment Scorecard
  23. Revenue Summary With Rank via CTE
  24. Warehouse Revenue With Rank via CTE
  25. User Engagement Score Pipeline