Why advanced window functions matter in SQL - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the running total of sales ordered by date.

SQL

SELECT date, sales, SUM(sales) OVER (ORDER BY [1]) AS running_total FROM sales_data;

Drag options to blanks, or click blank then click option'

Adate

Bsales

Ccustomer_id

Dregion

Attempts:

3 left

💡 Hint

Common Mistakes

Ordering by sales instead of date causes incorrect running totals.

Using a non-date column for ordering breaks the time sequence.

The running total should be ordered by the date column to accumulate sales over time.

2fill in blank

medium

Complete the code to assign a rank to each employee based on their sales within each region.

SQL

SELECT employee_id, region, sales, RANK() OVER (PARTITION BY [1] ORDER BY sales DESC) AS sales_rank FROM employee_sales;

Drag options to blanks, or click blank then click option'

Aregion

Bsales

Cemployee_id

Ddate

Attempts:

3 left

💡 Hint

Common Mistakes

Partitioning by employee_id results in each employee being in their own group.

Not partitioning causes ranking across all employees ignoring regions.

Partitioning by region groups employees so ranks are calculated within each region separately.

3fill in blank

hard

Fix the error in the code to calculate the difference between current and previous sales values ordered by date.

SQL

SELECT date, sales, sales - LAG([1]) OVER (ORDER BY date) AS sales_diff FROM sales_data;

Drag options to blanks, or click blank then click option'

Aregion

Bsales

Cdate

Demployee_id

Attempts:

3 left

💡 Hint

Common Mistakes

Using date inside LAG causes a type error when subtracting.

Using a non-numeric column inside LAG breaks the calculation.

The LAG function should refer to the sales column to get the previous sales value for subtraction.

4fill in blank

hard

Fill both blanks to calculate the cumulative average sales per region ordered by date.

SQL

SELECT region, date, sales, AVG([1]) OVER (PARTITION BY [2] ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS cumulative_avg FROM sales_data;

Drag options to blanks, or click blank then click option'

Asales

Bdate

Cregion

Demployee_id

Attempts:

3 left

💡 Hint

Common Mistakes

Using date instead of sales inside AVG causes wrong results.

Partitioning by date instead of region mixes data groups.

The cumulative average is calculated on the sales column, partitioned by region to group data correctly.

5fill in blank

hard

Fill all three blanks to calculate the difference between current sales and the average sales of the previous 3 days within each region.

SQL

SELECT region, date, sales, sales - AVG([1]) OVER (PARTITION BY [2] ORDER BY [3] ROWS BETWEEN 3 PRECEDING AND 1 PRECEDING) AS diff_from_avg_prev_3_days FROM sales_data;

Drag options to blanks, or click blank then click option'

Asales

Bregion

Cdate

Demployee_id

Attempts:

3 left

💡 Hint

Common Mistakes

Using employee_id instead of date for ordering breaks the time logic.

Not partitioning by region mixes data across regions.

The average is calculated on sales, partitioned by region, ordered by date to consider the previous 3 days correctly.

Practice

1. What is the main advantage of using advanced window functions in SQL compared to simple aggregate functions?

easy

A. They only work on single rows without considering others.

B. They automatically create indexes for faster queries.

C. They allow calculations across rows without collapsing the result into groups.

D. They replace the need for JOIN operations.

Solution

1. Step 1: Understand aggregate functions behavior

   Aggregate functions like SUM or AVG group rows and return one result per group, collapsing data.

2. Step 2: Understand window functions behavior

   Window functions perform calculations across rows but keep each row visible, without grouping them.

3. Final Answer:

   They allow calculations across rows without collapsing the result into groups. -> Option C

4. Quick Check:

   Window functions = calculations without grouping [OK]

Hint: Window functions keep rows, aggregates collapse them [OK]

Common Mistakes:

• Thinking window functions group rows like aggregates
• Confusing window functions with JOINs
• Assuming window functions only work on single rows

2. Which of the following is the correct syntax to calculate a running total of sales using a window function?

easy

A. SELECT SUM(sales) OVER (ORDER BY date) AS running_total FROM sales_data;

B. SELECT SUM(sales) GROUP BY date ORDER BY running_total FROM sales_data;

C. SELECT RUNNING_TOTAL(sales) FROM sales_data ORDER BY date;

D. SELECT sales + PREVIOUS(sales) FROM sales_data;

Solution

1. Step 1: Identify correct window function syntax

   SUM() OVER (ORDER BY column) computes running total ordered by that column.

2. Step 2: Check other options for syntax errors

   The other options use invalid SQL syntax or functions that don't exist.

3. Final Answer:

   SELECT SUM(sales) OVER (ORDER BY date) AS running_total FROM sales_data; -> Option A

4. Quick Check:

   SUM() OVER (ORDER BY ...) = running total [OK]

Hint: Use SUM() OVER (ORDER BY ...) for running totals [OK]

Common Mistakes:

• Using GROUP BY instead of OVER()
• Trying non-existent functions like RUNNING_TOTAL()
• Adding values manually without window syntax

3. Given the table employees with columns department, salary, and name, what will the following query return?

SELECT name, department, salary, RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS rank FROM employees;

medium

A. Calculates total salary per department.

B. Ranks employees by salary across all departments combined.

C. Returns employees sorted by name with no ranking.

D. Ranks employees by salary within each department, highest salary gets rank 1.

Solution

1. Step 1: Understand PARTITION BY usage

   PARTITION BY department groups rows by department for the window function.

2. Step 2: Understand RANK() OVER (ORDER BY salary DESC)

   Ranks salaries in descending order within each department, highest salary gets rank 1.

3. Final Answer:

   Ranks employees by salary within each department, highest salary gets rank 1. -> Option D

4. Quick Check:

   RANK() + PARTITION BY = rank within groups [OK]

Hint: PARTITION BY groups, ORDER BY sets rank order [OK]

Common Mistakes:

• Thinking RANK() ignores PARTITION BY
• Confusing RANK() with total aggregation
• Assuming sorting by name affects rank

4. Identify the error in this SQL query that tries to calculate a cumulative sum of sales per region:

SELECT region, sales, SUM(sales) OVER (PARTITION region ORDER BY date) AS cumulative_sales FROM sales_data;

medium

A. Missing parentheses around PARTITION BY clause.

B. Incorrect keyword; should be PARTITION BY instead of PARTITION.

C. ORDER BY cannot be used inside window functions.

D. SUM() cannot be used as a window function.

Solution

1. Step 1: Check window function syntax

   The correct syntax requires PARTITION BY, not just PARTITION.

2. Step 2: Validate other parts

   ORDER BY inside OVER() is valid for running totals; SUM() is valid as window function.

3. Final Answer:

   Incorrect keyword; should be PARTITION BY instead of PARTITION. -> Option B

4. Quick Check:

   Use PARTITION BY, not PARTITION alone [OK]

Hint: Use PARTITION BY, not just PARTITION [OK]

Common Mistakes:

• Omitting BY after PARTITION
• Thinking ORDER BY is invalid in window functions
• Believing SUM() can't be a window function

5. You want to find the difference in salary between each employee and the highest salary in their department using window functions. Which query correctly achieves this?

hard

A. SELECT name, department, salary, MAX(salary) OVER (PARTITION BY department) - salary AS diff FROM employees;

B. SELECT name, department, salary, salary - MAX(salary) OVER (PARTITION BY department) AS diff FROM employees;

C. SELECT name, department, salary, salary - MAX(salary) FROM employees GROUP BY department;

D. SELECT name, department, salary, salary - MAX(salary) FROM employees;

Solution

1. Step 1: Use MAX() as window function partitioned by department

   MAX(salary) OVER (PARTITION BY department) gives highest salary per department for each row.

2. Step 2: Calculate difference as highest salary minus employee salary

   Subtract employee salary from max salary to get positive difference.

3. Step 3: Check options for correct calculation and syntax

   SELECT name, department, salary, MAX(salary) OVER (PARTITION BY department) - salary AS diff FROM employees; correctly subtracts salary from max salary per department; SELECT name, department, salary, salary - MAX(salary) OVER (PARTITION BY department) AS diff FROM employees; reverses subtraction; The remaining options misuse GROUP BY or lack window function.

4. Final Answer:

   SELECT name, department, salary, MAX(salary) OVER (PARTITION BY department) - salary AS diff FROM employees; -> Option A

5. Quick Check:

   MAX() OVER PARTITION BY + subtraction = difference [OK]

Hint: Subtract salary from MAX() OVER partition for difference [OK]

Common Mistakes:

• Reversing subtraction order
• Using GROUP BY instead of window functions
• Omitting PARTITION BY in MAX()