--- title: "FAANG-Style SQL Interview Traps (And How to Avoid Them)" description: "SQL interview s at FAANG (Facebook/Meta, Amazon, Apple, Netflix, Google) are not about syntax . They are designed to test logical thinking, edge cases, execution order, and data correctness at scale ...." url: https://srinimf.com/2026/02/09/faang-style-sql-interview-traps-and-how-to-avoid-them/ date: 2026-02-09 modified: 2026-10-10 author: "Srini" image: https://i0.wp.com/srinimf.com/wp-content/uploads/2026/02/faang_sql_feature.png?fit=1200%2C630&ssl=1 categories: ["SQL"] tags: ["FAANG SQL Interview", "SQL Interview Questions"] type: post lang: en --- # FAANG-Style SQL Interview Traps (And How to Avoid Them) [SQL interview](https://srinimf.com/2026/04/26/quick-sql-interview-questions-for-data-engineers-little-tricky/)s at FAANG (Facebook/Meta, Amazon, Apple, Netflix, Google) are **not about syntax**. They are designed to test **logical thinking, edge cases, execution order, and data correctness at scale**. Many strong candidates fail—not because they don’t know SQL, but because they fall into subtle traps. In this blog, we’ll walk through **real FAANG-style SQL traps**, why candidates fail them, and [how to](https://srinimf.com/2017/10/27/sql-deadlock-four-precise-ways-to-avoid-it/) answer like a senior data engineer or analyst. --- ## 1. NOT IN + NULL: The Silent Interview Killer ### Question Find users who never placed an order. ### Common (Wrong) Answer ``` SELECT user_idFROM usersWHERE user_id NOT IN ( SELECT user_id FROM orders); ``` ### Why This Fails If the subquery returns **even one NULL**, the entire query returns **no rows**. This is standard SQL behavior—and FAANG interviewers expect you to know it. ### FAANG-Safe Answer ``` SELECT u.user_idFROM users uWHERE NOT EXISTS ( SELECT 1 FROM orders o WHERE o.user_id = u.user_id); ``` **What they’re testing:** NULL handling and logical correctness. --- ## 2. Top-N Per Group: Ties Break Offers ### Question Find the top 3 salaries per department. ### Trap Answer ``` ROW_NUMBER() OVER (PARTITION BY dept_id ORDER BY salary DESC) ``` ### Why This Fails `ROW_NUMBER()` ignores ties. If two employees share the same salary, one gets excluded. ### Correct Answer ``` DENSE_RANK() OVER (PARTITION BY dept_id ORDER BY salary DESC) ``` **What they’re testing:** Understanding of ranking functions and edge cases. --- ## 3. WHERE vs HAVING: Aggregation Logic ### Question Find users with more than 3 orders in 2024. ### Wrong Logic ``` HAVING COUNT(*) > 3 AND order_date >= '2024-01-01' ``` ### Correct Answer ``` SELECT user_idFROM ordersWHERE order_date >= '2024-01-01'GROUP BY user_idHAVING COUNT(*) > 3; ``` **Key rule:** - `WHERE` filters rows - `HAVING` filters aggregates --- ## 4. LEFT JOIN Turned into INNER JOIN ### Question List all users and their last login date (including users who never logged in). ### Trap Query ``` LEFT JOIN logins l ON u.id = l.user_idWHERE l.login_date >= '2024-01-01' ``` ### Why This Fails The `WHERE` clause removes NULLs—effectively converting the LEFT JOIN into an INNER JOIN. ### Correct Answer ``` LEFT JOIN logins l ON u.id = l.user_id AND l.login_date >= '2024-01-01'; ``` **What they’re testing:** Join behavior and SQL execution order. --- ## 5. MAX(date) with Non-Aggregated Columns ### Question Get the latest order for each user. ### Wrong Answer ``` SELECT user_id, MAX(order_date), amountFROM ordersGROUP BY user_id; ``` ### Why This Fails The `amount` does not correspond to the max date. ### Correct Answer ``` SELECT user_id, order_date, amountFROM ( SELECT *, ROW_NUMBER() OVER ( PARTITION BY user_id ORDER BY order_date DESC ) rn FROM orders) tWHERE rn = 1; ``` **What they’re testing:** Column correlation and data correctness. --- ## 6. Date Filtering Trap (Timestamps) ### Question Fetch orders from January 2024. ### Trap ``` WHERE order_date BETWEEN '2024-01-01' AND '2024-01-31' ``` ### Correct Answer ``` WHERE order_date >= '2024-01-01' AND order_date < '2024-02-01'; ``` **Why FAANG cares:** Production systems store timestamps, not just dates. --- ## 7. DISTINCT Is Not a Band-Aid ### Question Count unique active users per day. ### Trap ``` SELECT DISTINCT date, COUNT(user_id)FROM eventsGROUP BY date; ``` ### Correct Answer ``` SELECT date, COUNT(DISTINCT user_id)FROM eventsGROUP BY date; ``` **What they’re testing:** Understanding row-level vs aggregate-level DISTINCT. --- ## 8. Window Functions in WHERE Clause ### Question Find users with more than 5 orders. ### Trap ``` WHERE COUNT(*) OVER (PARTITION BY user_id) > 5 ``` ### Correct Answer ``` SELECT DISTINCT user_idFROM ( SELECT user_id, COUNT(*) OVER (PARTITION BY user_id) cnt FROM orders) tWHERE cnt > 5; ``` **What they’re testing:** SQL execution order mastery. --- ## 9. Anti-Joins: Correct vs Scalable ### Question Find users with no activity. ### Acceptable but Risky ``` LEFT JOIN events e ON u.id = e.user_idWHERE e.user_id IS NULL; ``` ### FAANG-Preferred ``` WHERE NOT EXISTS ( SELECT 1 FROM events e WHERE e.user_id = u.id); ``` **Why:** Better performance and clearer intent at scale. --- ## How FAANG Evaluates SQL Answers They don’t just look for a working query. They evaluate: - NULL safety - Edge-case handling - Execution order awareness - Performance at scale - Clarity of thought If you explain *why* your solution is safe, you score higher—even if syntax isn’t perfect. --- ## Final Interview Tip Before you say “done,” ask yourself: - What if there are NULLs? - What if there are ties? - What if data volume is huge? 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