SQL Practice Problems

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Author: Sylvia Moestl Vasilik
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Pages: 120

🚀 SQL Practice Problems: 57 Beginning, Intermediate & Advanced Challenges to Master Databases with a Learn-by-Doing Approach 💻📊

🌍 Introduction

Structured Query Language (SQL) remains one of the most powerful and essential tools in modern engineering, data science, and software development. Whether you are building enterprise systems in the United States, designing fintech platforms in the United Kingdom, optimizing logistics in Canada, supporting mining operations in Australia, or working on AI pipelines across Europe, SQL is at the heart of data-driven systems.

This article provides 57 carefully structured SQL practice problems — divided into beginner, intermediate, and advanced levels — using a learn-by-doing engineering methodology.

The goal is not just to read SQL.
The goal is to engineer solutions.

This guide is written for:

  • 🎓 University students studying computer science, engineering, or data analytics

  • 🧑‍💻 Junior developers learning backend systems

  • 📊 Data analysts preparing for interviews

  • 🏢 Professionals upgrading their database skills

By the end of this guide, you will:

  • Understand SQL theory deeply

  • Solve progressively complex problems

  • Apply SQL to real-world engineering systems

  • Avoid common professional mistakes

  • Gain confidence for technical interviews

Let’s build real database skills — step by step.

📚 Background Theory

🧠 Why SQL Matters in Modern Engineering

Almost every modern system stores structured data:

  • Banking transactions

  • E-commerce orders

  • Hospital records

  • IoT sensor logs

  • Manufacturing data

  • Government databases

Behind these systems are Relational Database Management Systems (RDBMS) like:

  • MySQL

  • PostgreSQL

  • Microsoft SQL Server

  • Oracle Database

SQL allows engineers to:

  • Create data structures

  • Insert and manage data

  • Retrieve information

  • Perform calculations

  • Build analytical reports

  • Secure sensitive information

🔬 Relational Model Fundamentals

The relational model is based on:

  • Tables (Relations)

  • Rows (Tuples)

  • Columns (Attributes)

  • Primary Keys

  • Foreign Keys

Example:

Table: Customers

CustomerID Name Country
1 John USA
2 Emma UK
3 Lucas Canada

Primary Key: CustomerID

🏗 Core SQL Categories

SQL operations fall into categories:

1️⃣ Data Definition Language (DDL)

  • CREATE

  • ALTER

  • DROP

2️⃣ Data Manipulation Language (DML)

  • INSERT

  • UPDATE

  • DELETE

3️⃣ Data Query Language (DQL)

  • SELECT

4️⃣ Data Control Language (DCL)

  • GRANT

  • REVOKE

Understanding these layers is critical for engineers building scalable systems.

🔎 Technical Definition

📘 What is SQL?

SQL (Structured Query Language) is a standardized programming language used to manage and manipulate relational databases.

It enables:

  • Structured data storage

  • Efficient retrieval

  • Data consistency

  • Transaction control

  • Query optimization

In engineering terms:

SQL is the interface layer between application logic and structured data storage systems.

🛠 Step-by-Step Explanation: The Learn-by-Doing Framework

We will use a fictional Engineering Company Database:

Tables:

  • Employees

  • Departments

  • Projects

  • Salaries

  • Orders

  • Customers

Each level contains increasing complexity.

🟢 LEVEL 1: 20 BEGINNER SQL PRACTICE PROBLEMS

🟢 Beginner Problem 1 – Select All Employees

Task: Display all employees.

🟢 Beginner Problem 2 – Select Specific Columns

Retrieve employee names and salaries.

🟢 Beginner Problem 3 – Filter by Country

Show employees located in the USA.

🟢 Beginner Problem 4 – Salary Greater Than Condition

Find employees earning more than $50,000.

🟢 Beginner Problem 5 – Sort Results

Sort employees by salary descending.

🟢 Beginner Problem 6 – Count Employees

Count total number of employees.

🟢 Beginner Problem 7 – Average Salary

Calculate average salary.

🟢 Beginner Problem 8 – Minimum & Maximum Salary

Find lowest and highest salaries.

🟢 Beginner Problem 9 – DISTINCT Countries

Show unique countries.

🟢 Beginner Problem 10 – LIKE Operator

Find employees whose name starts with “A”.

🟢 Beginner Problem 11 – BETWEEN

Find salaries between 40,000 and 70,000.

🟢 Beginner Problem 12 – IN Clause

Find employees in USA, UK, Canada.

🟢 Beginner Problem 13 – IS NULL

Find employees without assigned department.

🟢 Beginner Problem 14 – Simple JOIN

Join employees with departments.

🟢 Beginner Problem 15 – LEFT JOIN

List all employees including those without projects.

🟢 Beginner Problem 16 – GROUP BY

Count employees per department.

🟢 Beginner Problem 17 – HAVING

Departments with more than 5 employees.

🟢 Beginner Problem 18 – Simple INSERT

Insert new employee.

🟢 Beginner Problem 19 – UPDATE

Increase salary by 10%.

🟢 Beginner Problem 20 – DELETE

Delete employees who resigned.

🟡 LEVEL 2: 20 INTERMEDIATE SQL PRACTICE PROBLEMS

🟡 Intermediate Problem 21 – Multi-table JOIN

Combine Employees, Departments, and Projects.

🟡 Intermediate Problem 22 – Subquery

Find employees earning above company average.

🟡 Intermediate Problem 23 – Correlated Subquery

Find employees earning above department average.

🟡 Intermediate Problem 24 – CASE Statement

Classify salaries as Low, Medium, High.

🟡 Intermediate Problem 25 – Aggregate with Multiple Conditions

Calculate total salary by country.

🟡 Intermediate Problem 26 – INNER vs LEFT JOIN Comparison

Compare outputs.

🟡 Intermediate Problem 27 – Date Functions

Find employees hired in 2023.

🟡 Intermediate Problem 28 – Window Function (ROW_NUMBER)

Rank employees by salary.

🟡 Intermediate Problem 29 – Partition By

Rank employees within departments.

🟡 Intermediate Problem 30 – CTE (Common Table Expression)

Use WITH clause for readability.

🟡 Intermediate Problem 31 – Index Concept

Explain indexing benefits.

🟡 Intermediate Problem 32 – UNION

Combine employees and contractors.

🟡 Intermediate Problem 33 – INTERSECT

Find common records.

🟡 Intermediate Problem 34 – EXISTS

Check if employees assigned to projects.

🟡 Intermediate Problem 35 – Stored Procedure Basics

Create procedure for bonus calculation.

🟡 Intermediate Problem 36 – Trigger Example

Audit salary changes.

🟡 Intermediate Problem 37 – Transactions

Rollback on failure.

🟡 Intermediate Problem 38 – Constraints

Add NOT NULL, UNIQUE.

🟡 Intermediate Problem 39 – Composite Keys

Use multiple columns as key.

🟡 Intermediate Problem 40 – View Creation

Create summary view.

🔴 LEVEL 3: 17 ADVANCED SQL PRACTICE PROBLEMS

🔴 Advanced Problem 41 – Recursive CTE

Organizational hierarchy.

🔴 Advanced Problem 42 – Performance Optimization

Explain execution plan.

🔴 Advanced Problem 43 – Index Optimization

Composite vs single index.

🔴 Advanced Problem 44 – Query Refactoring

Optimize nested subqueries.

🔴 Advanced Problem 45 – Window Functions (Advanced)

Running totals.

🔴 Advanced Problem 46 – Data Warehousing Query

Monthly sales aggregation.

🔴 Advanced Problem 47 – Pivot Tables

Convert rows to columns.

🔴 Advanced Problem 48 – JSON Handling

Store semi-structured data.

🔴 Advanced Problem 49 – Database Security

Role-based access.

🔴 Advanced Problem 50 – Deadlock Scenario

Explain and prevent.

🔴 Advanced Problem 51 – Normalization Analysis

1NF, 2NF, 3NF.

🔴 Advanced Problem 52 – Denormalization Tradeoff

Performance vs integrity.

🔴 Advanced Problem 53 – Replication Concepts

Read replicas.

🔴 Advanced Problem 54 – Sharding Strategy

Horizontal partitioning.

🔴 Advanced Problem 55 – Big Data SQL

Using SQL in distributed systems.

🔴 Advanced Problem 56 – ACID Properties

Consistency engineering.

🔴 Advanced Problem 57 – System Design Question

Design database for global e-commerce platform.

📊 Comparison Table

Level Focus Area Skills Developed
Beginner Basics Syntax mastery
Intermediate Logic Analytical thinking
Advanced Architecture Engineering design

📐 Diagrams

🔷 Simple Join Diagram

Employees
↓
Departments

Foreign Key → DepartmentID

🔷 Query Execution Flow

Client → SQL Engine → Optimizer → Execution Plan → Result

🧩 Detailed Example

Problem: Employees Above Department Average

Steps:

  1. Calculate department average

  2. Compare employee salary

  3. Return filtered results

Engineering reasoning:

  • Break into logical sub-problems

  • Optimize using CTE

🌎 Real World Application in Modern Projects

SQL is used in:

  • Banking fraud detection

  • E-commerce recommendation systems

  • Manufacturing data tracking

  • Government census systems

  • AI training datasets

Companies across:

  • USA Silicon Valley startups

  • UK fintech companies

  • Canadian healthcare systems

  • Australian mining technology

  • European logistics automation

All rely heavily on SQL engineering.

⚠️ Common Mistakes

  • Not indexing properly

  • Using SELECT * in production

  • Ignoring NULL behavior

  • Poor normalization

  • Missing transactions

🧱 Challenges & Solutions

Challenge Solution
Slow queries Add indexes
Deadlocks Proper transaction order
Data inconsistency Use constraints
Poor scalability Partition tables

📘 Case Study

Engineering Data Platform Migration

A European logistics firm migrated from legacy database to PostgreSQL.

Challenges:

  • 10M+ records

  • Slow queries

  • Reporting delays

Solutions:

  • Index optimization

  • Query refactoring

  • Partitioning

Result:

  • 60% faster queries

  • 40% reduced infrastructure cost

💡 Tips for Engineers

  • Practice daily

  • Analyze execution plans

  • Normalize carefully

  • Learn indexing deeply

  • Build mini projects

  • Read real production schemas

❓ FAQs

1️⃣ How long does it take to master SQL?

3–6 months with daily practice.

2️⃣ Is SQL enough for data science?

Often combined with Python.

3️⃣ Which database is best?

Depends on project scale.

4️⃣ Are window functions important?

Yes, especially in analytics.

5️⃣ Do engineers still need SQL with AI tools?

Absolutely — data still lives in databases.

6️⃣ Is SQL used in cloud platforms?

Yes (AWS, Azure, GCP).

7️⃣ Can SQL handle big data?

With distributed systems, yes.

🎯 Conclusion

SQL is not just a language.
It is a core engineering skill.

By solving these 57 problems — from beginner to advanced — you develop:

  • Logical thinking

  • System design skills

  • Performance optimization knowledge

  • Real-world database engineering capability

For students and professionals in the USA, UK, Canada, Australia, and Europe, mastering SQL means unlocking opportunities in:

  • Software engineering

  • Data science

  • DevOps

  • AI

  • Fintech

  • Government systems

Practice consistently.
Think like an engineer.
Query with purpose.

Your database mastery journey starts now. 🚀

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