SQL Interview Questions AI Can Solve in Seconds

Why SQL Interviews Are Still Hard in 2026

SQL has been around for over 50 years, yet it remains one of the most tested skills in technical interviews. Data engineering, analytics, backend development, and even product management roles routinely include SQL assessments. And candidates still struggle with them.

The difficulty comes from three factors. First, SQL is declarative. Unlike Python or JavaScript, you describe what you want rather than how to get it. This requires a different mental model that trips up candidates used to imperative programming. Second, interview questions combine multiple concepts at once. A single question might require a self-join, a window function, a CASE WHEN expression, and a HAVING clause, all in one query. Third, the pressure of a timed interview makes it harder to think through edge cases like NULL handling, duplicate rows, and off-by-one errors in date ranges.

The result: even experienced developers fail SQL interviews they could solve given an extra 10 minutes. AI tools change this equation, not by replacing your knowledge but by giving you a way to practice faster and check your thinking against a correct solution.

In this guide, we walk through 10 real SQL interview questions, show the AI-generated solution from AI2SQL, and break down the reasoning behind each answer.

10 Common SQL Interview Questions with AI-Generated Solutions

Question 1: Find Customers Who Placed Orders (INNER JOIN)

Problem: Given a customers table and an orders table, list all customers who have placed at least one order. Include the customer name, email, and total number of orders.

AI2SQL prompt: "List customers who placed at least one order, with their name, email, and total order count"

SELECT
    c.customer_name,
    c.email,
    COUNT(o.id) AS total_orders
FROM customers c
INNER JOIN orders o ON c.id = o.customer_id
GROUP BY c.customer_name, c.email
ORDER BY total_orders DESC;

Explanation: The INNER JOIN ensures only customers with matching orders appear. COUNT(o.id) counts the number of orders per customer. The GROUP BY groups results by customer so the COUNT aggregation works correctly. This is one of the most fundamental interview patterns: prove you understand the difference between INNER JOIN (only matching rows) and LEFT JOIN (all rows from the left table).

Question 2: Find Customers with No Orders (LEFT JOIN + NULL)

Problem: Find all customers who have never placed an order.

AI2SQL prompt: "Find customers who have never placed any order"

SELECT
    c.customer_name,
    c.email
FROM customers c
LEFT JOIN orders o ON c.id = o.customer_id
WHERE o.id IS NULL;

Explanation: This is the inverse of Question 1 and a classic interview trap. The LEFT JOIN keeps all customers even if they have no matching orders. When there is no match, the order columns are NULL. Filtering with WHERE o.id IS NULL isolates customers with zero orders. Interviewers use this to test whether you understand LEFT JOIN behavior and NULL semantics.

Question 3: Products Ordered More Than 5 Times (GROUP BY + HAVING)

Problem: Find products that have been ordered more than 5 times. Show the product name, category, and total quantity ordered.

AI2SQL prompt: "Show products ordered more than 5 times with product name, category, and total quantity"

SELECT
    p.product_name,
    p.category,
    SUM(oi.quantity) AS total_quantity
FROM products p
JOIN order_items oi ON p.id = oi.product_id
GROUP BY p.product_name, p.category
HAVING SUM(oi.quantity) > 5
ORDER BY total_quantity DESC;

Explanation: The critical distinction here is WHERE versus HAVING. WHERE filters rows before aggregation. HAVING filters after aggregation. Since we need to filter on SUM(oi.quantity), which is an aggregate, we must use HAVING. Using WHERE with an aggregate function is a syntax error. Interviewers specifically test this because many candidates confuse the two.

Question 4: Top 3 Highest-Paid Employees per Department (Window Functions)

Problem: For each department, find the top 3 highest-paid employees. Include ties.

AI2SQL prompt: "Get top 3 highest-paid employees per department, including ties, with department name and salary"

SELECT
    department_name,
    employee_name,
    salary,
    salary_rank
FROM (
    SELECT
        d.department_name,
        e.employee_name,
        e.salary,
        DENSE_RANK() OVER (
            PARTITION BY e.department_id
            ORDER BY e.salary DESC
        ) AS salary_rank
    FROM employees e
    JOIN departments d ON e.department_id = d.id
) ranked
WHERE salary_rank <= 3
ORDER BY department_name, salary_rank;

Explanation: This question tests three things: window functions, PARTITION BY, and the difference between RANK, DENSE_RANK, and ROW_NUMBER. DENSE_RANK is used here because the problem says "including ties." If two employees earn the same salary, they get the same rank, and the next rank is not skipped. ROW_NUMBER would assign unique numbers (breaking ties arbitrarily). RANK would skip numbers after ties (1, 1, 3 instead of 1, 1, 2). The subquery pattern is necessary because you cannot use a window function directly in a WHERE clause.

Question 5: Employees Earning Above Department Average (Subquery)

Problem: Find employees whose salary is above the average salary of their department.

AI2SQL prompt: "Find employees earning more than the average salary in their department"

SELECT
    e.employee_name,
    e.salary,
    d.department_name,
    dept_avg.avg_salary
FROM employees e
JOIN departments d ON e.department_id = d.id
JOIN (
    SELECT
        department_id,
        AVG(salary) AS avg_salary
    FROM employees
    GROUP BY department_id
) dept_avg ON e.department_id = dept_avg.department_id
WHERE e.salary > dept_avg.avg_salary
ORDER BY d.department_name, e.salary DESC;

Explanation: This can be solved with a correlated subquery or a derived table join. The derived table approach (shown above) is generally more performant because the subquery runs once to compute all department averages, then joins back to the employees table. A correlated subquery would recalculate the average for each row. Interviewers look for your ability to reason about performance differences between these approaches.

Question 6: Month-over-Month Revenue Growth (CTE)

Problem: Calculate the month-over-month revenue growth rate for the past 12 months.

AI2SQL prompt: "Calculate month-over-month revenue growth percentage for the last 12 months"

WITH monthly_revenue AS (
    SELECT
        DATE_TRUNC('month', order_date) AS month,
        SUM(total_amount) AS revenue
    FROM orders
    WHERE order_date >= CURRENT_DATE - INTERVAL '12 months'
    GROUP BY DATE_TRUNC('month', order_date)
)
SELECT
    month,
    revenue,
    LAG(revenue) OVER (ORDER BY month) AS prev_month_revenue,
    ROUND(
        (revenue - LAG(revenue) OVER (ORDER BY month))
        / LAG(revenue) OVER (ORDER BY month) * 100,
        2
    ) AS growth_pct
FROM monthly_revenue
ORDER BY month;

Explanation: This question tests CTEs and the LAG window function. The CTE first summarizes revenue by month. Then LAG retrieves the previous month's revenue for each row. The growth percentage formula is (current - previous) / previous * 100. Note that the first month will have NULL for growth_pct because there is no previous month to compare against. Interviewers value CTEs here because they make the query readable compared to nested subqueries.

Question 7: Find Employees and Their Managers (Self-Join)

Problem: List each employee along with their manager's name. Include employees who do not have a manager.

AI2SQL prompt: "List all employees with their manager name, including those without a manager"

SELECT
    e.employee_name AS employee,
    e.title,
    m.employee_name AS manager
FROM employees e
LEFT JOIN employees m ON e.manager_id = m.id
ORDER BY m.employee_name NULLS LAST, e.employee_name;

Explanation: A self-join is when a table joins to itself. The employees table appears twice: once as e (the employee) and once as m (the manager). LEFT JOIN is used instead of INNER JOIN because the CEO or top-level employees have no manager (manager_id is NULL). Without LEFT JOIN, those employees would be excluded from the results. This is a foundational interview pattern that tests your understanding of table aliasing and hierarchical data.

Question 8: Categorize Orders by Size (CASE WHEN)

Problem: Categorize each order as "Small" (under $100), "Medium" ($100-$500), or "Large" (over $500). Show the count and average value for each category.

AI2SQL prompt: "Categorize orders as Small (under 100), Medium (100-500), Large (over 500) and show count and average value per category"

SELECT
    CASE
        WHEN total_amount < 100 THEN 'Small'
        WHEN total_amount BETWEEN 100 AND 500 THEN 'Medium'
        ELSE 'Large'
    END AS order_size,
    COUNT(*) AS order_count,
    ROUND(AVG(total_amount), 2) AS avg_value
FROM orders
GROUP BY
    CASE
        WHEN total_amount < 100 THEN 'Small'
        WHEN total_amount BETWEEN 100 AND 500 THEN 'Medium'
        ELSE 'Large'
    END
ORDER BY avg_value;

Explanation: CASE WHEN is SQL's conditional logic, similar to if-else in other languages. The important detail here is that the CASE expression must be repeated in the GROUP BY clause. Most databases do not allow grouping by a column alias defined in the SELECT. Some candidates try GROUP BY order_size, which fails in PostgreSQL and SQL Server (though it works in MySQL). Interviewers test this to see if you know SQL evaluation order: FROM, WHERE, GROUP BY, HAVING, SELECT, ORDER BY.

Question 9: Users Who Logged In 3 Consecutive Days (Advanced Window Function)

Problem: Find users who logged in for at least 3 consecutive days.

AI2SQL prompt: "Find users who logged in for at least 3 consecutive days from the logins table"

WITH login_days AS (
    SELECT DISTINCT
        user_id,
        DATE(login_time) AS login_date
    FROM logins
),
grouped AS (
    SELECT
        user_id,
        login_date,
        login_date - INTERVAL '1 day' * ROW_NUMBER() OVER (
            PARTITION BY user_id
            ORDER BY login_date
        ) AS grp
    FROM login_days
)
SELECT DISTINCT user_id
FROM grouped
GROUP BY user_id, grp
HAVING COUNT(*) >= 3;

Explanation: This is one of the hardest interview patterns. The trick is the "date minus row_number" technique. For consecutive dates, subtracting an incrementing row number produces the same value. For example, dates Jan 3, Jan 4, Jan 5 with row numbers 1, 2, 3 produce Jan 2, Jan 2, Jan 2 as the group key. Non-consecutive dates produce different group keys. The first CTE deduplicates multiple logins on the same day. The second CTE creates the grouping key. The final query filters groups with 3 or more consecutive days. This pattern appears frequently in interviews at companies like Meta, Amazon, and Stripe.

Question 10: Running Total of Sales (Cumulative Sum)

Problem: Calculate a running total of daily sales for the current month.

AI2SQL prompt: "Calculate running total of daily sales for the current month"

SELECT
    order_date,
    SUM(total_amount) AS daily_sales,
    SUM(SUM(total_amount)) OVER (
        ORDER BY order_date
        ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
    ) AS running_total
FROM orders
WHERE order_date >= DATE_TRUNC('month', CURRENT_DATE)
GROUP BY order_date
ORDER BY order_date;

Explanation: The nested SUM(SUM(...)) pattern is what makes this question tricky. The inner SUM aggregates daily sales via GROUP BY. The outer SUM is a window function that computes the running total over the ordered results. The ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW frame tells the window function to include all rows from the start up to the current row. While this is the default frame for ORDER BY in most databases, explicitly stating it shows the interviewer you understand window frames.

How to Use AI2SQL to Practice SQL Interview Questions

AI2SQL is not just a query generator. It is a study tool. Here is how to use it effectively for interview preparation:

Step 1: Attempt the Problem Yourself

Write your answer on paper or in a text editor before using AI. This forces you to think through the logic. Time yourself. Most interview questions expect a solution within 10-15 minutes.

Step 2: Generate the AI Solution

Type the problem into AI2SQL in plain English. Compare the generated query with yours. Look for differences in approach: did the AI use a CTE where you used a subquery? Did it handle NULL cases you missed?

Step 3: Use the Explain SQL Tool

Paste any query into AI2SQL's explain_sql feature and it breaks down each clause in plain English. This is especially useful when you encounter a pattern you do not recognize. The explanation covers what each JOIN does, why specific filters exist, and what the output will look like.

Step 4: Modify and Re-Generate

Change the problem constraints and re-generate. "Top 3 per department" becomes "Top 5 per region." "Consecutive 3 days" becomes "Consecutive 7 days." This builds pattern recognition rather than rote memorization.

Step 5: Practice Across Dialects

Switch between PostgreSQL, MySQL, and SQL Server in AI2SQL to see how syntax differs. DATE_TRUNC in PostgreSQL becomes DATEPART in SQL Server. LIMIT becomes TOP. Knowing dialect differences is a common follow-up question in interviews.

The most effective practice loop is: attempt, compare, understand, vary. AI2SQL accelerates each step because you get instant feedback instead of searching Stack Overflow for 20 minutes.

SQL Interview Patterns Cheat Sheet

Here is a quick reference for the patterns that appear most often in SQL interviews:

Pattern	Key Concept	Common Mistake
INNER vs LEFT JOIN	INNER = only matches. LEFT = all from left table.	Using INNER JOIN when NULLs matter
GROUP BY + HAVING	HAVING filters after aggregation	Using WHERE with aggregate functions
Window Functions	PARTITION BY = per-group. ORDER BY = sort within group.	Using window function in WHERE clause
RANK vs DENSE_RANK vs ROW_NUMBER	RANK skips, DENSE_RANK does not, ROW_NUMBER is unique	Choosing the wrong function for tie handling
Self-Join	Table joins to itself with aliases	Forgetting LEFT JOIN for NULLable FK
CTE (WITH clause)	Named subquery for readability	Not knowing CTE is not materialized by default
Consecutive Days	date - ROW_NUMBER() grouping trick	Not deduplicating multiple logins per day
CASE WHEN in GROUP BY	Must repeat full CASE expression in GROUP BY	Using column alias in GROUP BY (fails in most DBs)

Should You Use AI in Real SQL Interviews?

This is a question worth addressing directly.

For practice and preparation: absolutely yes. Using AI2SQL to study SQL is no different from using a textbook, a course, or a solution manual. You see the correct answer, understand why it works, and build your own mental models. This is how learning works.

During a live interview: no, unless explicitly permitted. Most technical interviews expect you to solve problems without external tools. Using AI during a proctored assessment without disclosure is dishonest. It defeats the purpose of the assessment, which is to evaluate your reasoning, not your ability to type a prompt.

That said, the line is shifting. More companies are adopting "open book" or "tool-assisted" interview formats. Some explicitly allow candidates to use AI tools as part of the assessment, testing judgment and prompt engineering rather than memorization. If the company says tools are allowed, using AI2SQL is fair game.

The pragmatic advice: use AI to learn faster, then prove what you learned without AI. If you practice enough with AI2SQL, you will internalize the patterns. The consecutive-days trick, the DENSE_RANK versus ROW_NUMBER distinction, the HAVING versus WHERE rule. These become second nature through repeated exposure. When the interview comes, you will not need the tool because the knowledge is already yours.

There is also a workplace reality to consider. After you get the job, you will almost certainly use AI tools daily. The interview tests whether you can reason about SQL. Your actual job will reward you for getting correct results efficiently, regardless of how you got there.

Frequently Asked Questions

Can AI solve SQL interview questions accurately?

Yes, modern AI SQL tools like AI2SQL handle standard interview questions with high accuracy, including JOINs, window functions, GROUP BY, subqueries, and CTEs. They generate correct, well-formatted SQL in seconds. However, you should always understand the logic behind the solution rather than memorizing AI output.

Is it cheating to use AI to practice SQL interview questions?

Using AI for practice is not cheating. It is similar to using a textbook solution manual. The key is to attempt each problem yourself first, then use AI2SQL to check your answer, understand alternative approaches, and learn from the explanation. Using AI during a live interview without disclosure would be unethical.

What SQL topics are most common in interviews?

The most frequently tested SQL topics in interviews are JOINs (INNER, LEFT, SELF), GROUP BY with HAVING, window functions (ROW_NUMBER, RANK, LAG/LEAD), subqueries and CTEs, CASE WHEN expressions, and aggregation functions. Most companies test a combination of these in practical scenario-based questions.

How should I prepare for SQL interviews in 2026?

Start by practicing common patterns: JOINs, aggregations, window functions, and CTEs. Use AI2SQL to generate solutions and explanations for problems you find difficult. Focus on understanding why each clause exists in the query, not just memorizing syntax. Practice with real-world scenarios rather than abstract puzzles.

Can AI2SQL explain SQL queries step by step?

Yes, AI2SQL includes an explain_sql tool that breaks down any SQL query into plain English. It explains each clause, why specific JOINs are used, how filtering works, and what the output will look like. This makes it an effective study tool for interview preparation.