Real-World SQL Questions Every Data Analyst Should Master: From Beginner Logic to Industry-Level Problem Solving

When I first started helping learners prepare for SQL interviews and project work, I noticed a pattern. Most people knew the syntax, but they struggled to apply SQL in real business scenarios. Over the years, through training sessions, freelance consulting, and hands-on corporate projects, I have gathered a list of real-world SQL questions that truly test problem-solving skills, not just memorisation.

These are the kinds of questions hiring managers use to separate someone who can write a SELECT statement from someone who can build production-ready logic. Many of these problems came from actual business cases I have worked on, while others have been curated from industry experts and widely shared interview experiences.

If you are preparing for a data analyst role, improving your SQL problem-solving skills, or simply exploring the practical side of databases, this curated list will help you grow faster than any textbook example.

Why Real-World SQL Questions Matter

Unlike academic problems, real-world SQL questions often involve messy data, incomplete information, time-based logic, or multi-step thinking. During my tutoring and consulting sessions, I spend a lot of time helping learners think like analysts—understanding the business behind the query.

That mindset is what makes these questions valuable. They help you develop the intuition that companies expect from strong SQL candidates.

Real-World SQL Scenarios and Practice Questions

Below are some of the most relevant SQL questions used in interviews and real projects. Each one tests a specific concept that you will use repeatedly in data analytics, reporting, and backend logic.

Customers Who Purchased Exactly Two Different Products in a Month

Tables: Orders (order_id, customer_id, product_id, order_date)

This problem teaches grouping, counting unique values, and time-based filtering. Companies often use this logic to understand repeat behaviour or product engagement.

Customers Who Haven’t Purchased in the Last 6 Months

Tables: Customers (customer_id, customer_name), Orders (order_id, customer_id, order_date)

Customer inactivity detection is a core part of retention analytics, marketing automation, and churn prediction.

Percentage of Orders Delivered Late

Tables: Orders (order_id, order_date, expected_delivery_date, actual_delivery_date)

Almost every business that delivers physical goods uses SQL to track on-time delivery metrics, supply chain reliability, and vendor performance.

Top 3 Products by Revenue in Each Category

Tables: Products (product_id, category), Sales (sale_id, product_id, amount)

This question teaches ranking functions, which are essential in real dashboards and BI reports.

Customer Lifetime Value (CLV) Calculation

Tables: Customers (customer_id), Orders (order_id, customer_id, order_date, amount)

CLV is one of the most important metrics in retail, SaaS, and subscription businesses. SQL powers most CLV pipelines.

Employees Who Changed Departments More Than Twice

Tables: Employee_Dept_History (employee_id, department, start_date, end_date)

A classic organisational analytics question involving row counts and historical tracking.

Users Whose First Purchase Happened During a Promotion

Tables: Users (user_id), Orders (order_id, user_id, order_date, promo_applied)

This scenario appears in marketing analytics, especially when evaluating the impact of campaigns.

Days Where Sales Dropped More Than 20 Percent

Tables: Daily_Sales (date, total_sales_amount)

Time-series analysis is one of the strongest indicators of SQL proficiency, especially in business forecasting settings.

Products That Were Never Out of Stock

Tables: Products (product_id, name), Inventory (product_id, inventory_date, stock_available)

Inventory-based SQL questions are common in supply chain and warehouse management roles.

Compare Average Order Value for New vs Returning Customers

Tables: Orders (order_id, customer_id, order_amount, order_date)

Segmentation is central to understanding customer behaviour and building targeted strategies.

Additional High-Value SQL Interview Questions

These questions are widely asked during SQL interviews because they test fundamentals as well as advanced thinking.

Second-Highest Salary

A classic problem that introduces ranking and subqueries.

Highest Salary per Department

This question evaluates window functions and grouping.

Consecutive Login Streaks

This scenario tests date arithmetic and gap detection, often used in product analytics.

Customers Who Have Never Ordered

Perfect for LEFT JOIN and NULL filtering practice.

Running Totals in a Sales Table

A core requirement in dashboards and financial reporting.

Nth Highest Salary (Generalised)

A ranking function staple in SQL interviews.

Find Duplicate Records

Useful for identifying data quality issues.

Delete Duplicates and Keep Only One Record

Shows understanding of row selection and CTE usage.

Products That Were Never Sold

Tests anti-joins, a fundamental concept.

Top 3 Selling Products per Category

Another must-know ranking question.

Most Frequently Ordered Product

Demonstrates grouping and ordering.

Find Gaps in a Sequence (Missing IDs)

Important in auditing and data validation cases.

Rolling 7-Day Average Sales

A window function challenge for time-series analytics.

Users Who Ordered Every Month of a Year

Tests grouping, date extraction, and conditional logic.

Employees Who Earn More Than Their Manager

A staple in advanced JOINs and self-referencing queries.

Why These Questions Matter in Interviews and Projects

In my experience teaching SQL to learners and working with corporate teams, these questions aren’t just exercises—they represent exactly how SQL is used in daily data operations. These scenarios show up in dashboards, business reports, performance tracking, forecasting, and automation pipelines.

If you are preparing for an interview or planning to switch into a data role, practising these problems builds the confidence companies expect. Recruiters often tell me that candidates who understand real-world SQL stand out immediately.

If you ever need help working through these queries or want guidance tailored to your career goals, I often mentor learners and support teams through hands-on training and consulting sessions.

Final Thoughts

SQL becomes far more powerful when you step beyond textbook examples and work on real data situations. The questions above represent the exact type of thinking that makes analysts, engineers, and BI developers truly valuable.

Spend time practising them, understanding the business behind each scenario, and building your logic step by step. Over time, this approach shapes your SQL intuition, which is ultimately what companies look for in data professionals.

Applied Analytics by Rakesh Men