Crack Campus Placements with SQL

This path is built for students and freshers with no work experience. It goes from your first SELECT to the questions placement panels actually ask: second highest salary, duplicates, employees earning more than their manager, department-wise top earners, plus the DBMS theory (keys, normalization, ACID) that fills the viva. Every module links to a live lesson you can run in your browser, so you practise instead of just reading.

1. 1

   SELECT, WHERE & Filtering

   30 min

   Every SQL answer starts here: choose columns with SELECT, rows with WHERE, and combine conditions with AND, OR, IN, BETWEEN and LIKE. Panels often test NULL handling here: = NULL never matches anything, so you need IS NULL.

   SELECT name, department_id, salary
   FROM employees
   WHERE salary BETWEEN 50000 AND 90000
     AND name LIKE 'A%'
     AND manager_id IS NOT NULL;

   Say your reasoning out loud in the interview: "I'm filtering first, then I'll sort." Panels grade your thinking, not just the final query.
   Lesson: SELECT Statements Lesson: WHERE Clause Lesson: NULL & Logical Operators
2. 2

   Sorting, DISTINCT & Limiting Results

   20 min

   ORDER BY sorts (ascending by default), DISTINCT removes duplicate rows from the result, and LIMIT / TOP / FETCH FIRST return only the first N rows. Knowing the syntax differences between MySQL, SQL Server and Oracle is a common viva question.

   SELECT DISTINCT department_id
   FROM employees
   ORDER BY department_id;
   
   SELECT name, salary
   FROM employees
   ORDER BY salary DESC
   LIMIT 3;          -- SQL Server: SELECT TOP 3 ...  Oracle: FETCH FIRST 3 ROWS ONLY

   Never rely on row order without ORDER BY. Without it the database may return rows in any order, even if it looks sorted today.
   Lesson: ORDER BY Lesson: DISTINCT Blog: LIMIT vs TOP vs FETCH FIRST vs ROWNUM
3. 3

   GROUP BY, Aggregates & HAVING

   30 min

   Aggregate functions (COUNT, SUM, AVG, MIN, MAX) summarize groups created by GROUP BY. WHERE filters rows before grouping; HAVING filters groups after. "WHERE vs HAVING" is one of the most asked fresher questions.

   SELECT department_id, COUNT(*) AS employees, AVG(salary) AS avg_salary
   FROM employees
   WHERE hire_date >= '2021-01-01'
   GROUP BY department_id
   HAVING COUNT(*) >= 2;

   Every non-aggregated column in the SELECT must appear in GROUP BY. Forgetting this is the most common error in online tests.
   Lesson: GROUP BY Lesson: HAVING Clause Blog: SELECT vs WHERE vs HAVING
4. 4

   JOINs: Inner, Left, Right, Self

   40 min

   JOINs combine tables on a related column. INNER JOIN keeps only matches; LEFT JOIN keeps every row from the left table, with NULLs where there's no match. A self join joins a table to itself, which is the classic way to compare an employee with their manager.

   -- Classic: employees who earn more than their manager
   SELECT e.name AS employee, e.salary, m.name AS manager, m.salary AS manager_salary
   FROM employees e
   JOIN employees m ON m.id = e.manager_id
   WHERE e.salary > m.salary;

   Draw the two tables and the matching rows on paper before writing a join in an interview. It takes 20 seconds and prevents most join mistakes.
   Lesson: JOINs Blog: INNER vs LEFT vs RIGHT JOIN Tool: SQL Joins Visualizer
5. 5

   Subqueries & the Nth Highest Salary

   35 min

   A subquery is a query inside another query. It's the traditional answer to placement favourites like "find the second highest salary" and "employees earning above the average". Know at least two ways to solve each; panels often ask "can you do it another way?".

   -- Second highest salary (handles duplicates of the top salary)
   SELECT MAX(salary) AS second_highest
   FROM employees
   WHERE salary < (SELECT MAX(salary) FROM employees);
   
   -- Employees earning more than the company average
   SELECT name, salary
   FROM employees
   WHERE salary > (SELECT AVG(salary) FROM employees);

   Mention edge cases unprompted: "What if two people share the top salary? What if there's only one employee?" That's what separates a selected candidate from a rejected one.
   Lesson: Subqueries Blog: Subquery interview questions Blog: Correlated subqueries explained
6. 6

   Window Functions: RANK, DENSE_RANK, ROW_NUMBER

   40 min

   Window functions calculate across related rows without collapsing them. They're now standard in placement interviews for product and analytics companies. The top question: "highest paid employee in each department", solved with DENSE_RANK() partitioned by department.

   SELECT department_id, name, salary
   FROM (
       SELECT department_id, name, salary,
              DENSE_RANK() OVER (PARTITION BY department_id ORDER BY salary DESC) AS rnk
       FROM employees
   ) t
   WHERE rnk = 1;

   Be ready to explain the difference with ties: salaries 100, 100, 90 get ROW_NUMBER 1,2,3, RANK 1,1,3 and DENSE_RANK 1,1,2.
   Lesson: Window Functions Blog: ROW_NUMBER vs RANK vs DENSE_RANK Tool: SQL Window Visualizer
7. 7

   Classic Placement Questions

   40 min

   A small set of problems appears in placement tests year after year: find duplicate rows, delete duplicates keeping one, count employees per department including empty departments, find departments with no employees, and display the Nth highest salary. Practise until you can write each in under three minutes.

   -- Find duplicate emails
   SELECT email, COUNT(*) AS copies
   FROM customers
   GROUP BY email
   HAVING COUNT(*) > 1;
   
   -- Departments with no employees
   SELECT d.name
   FROM departments d
   LEFT JOIN employees e ON e.department_id = d.id
   WHERE e.id IS NULL;

   After writing a query, trace it on 3-4 sample rows out loud. Interviewers often hand you a tiny table precisely to see if you check your own work.
   Blog: Tricky SQL interview questions Blog: Output-based interview questions Blog: SQL mistakes in interviews
8. 8

   DBMS Theory: Keys, Normalization, ACID, DDL vs DML

   35 min

   The technical viva goes beyond queries. Expect: primary vs foreign vs unique key; DDL vs DML vs DCL vs TCL; DELETE vs TRUNCATE vs DROP; 1NF, 2NF and 3NF with an example; ACID properties of transactions; and what an index is and when not to use one.

   Prepare one short, concrete example for each theory topic. "A student table storing multiple phone numbers in one column breaks 1NF" lands far better than reciting a definition.
   Lesson: Normalization (1NF–3NF) Lesson: Transactions & ACID Blog: DELETE vs TRUNCATE vs DROP