Why query patterns matter in SQL - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select all columns from the employees table.

SQL

SELECT [1] FROM employees;

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

AALL

B*

Ceverything

Dcolumns

Attempts:

3 left

💡 Hint

Common Mistakes

Using words like ALL or columns instead of * causes errors.

Using * selects all columns from the table.

2fill in blank

medium

Complete the code to filter employees with salary greater than 50000.

SQL

SELECT * FROM employees WHERE salary [1] 50000;

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

A>

B=

C<

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using = or < will not select the correct rows.

The > operator filters for salaries greater than 50000.

3fill in blank

hard

Fix the error in the query to count employees in each department.

SQL

SELECT department, COUNT([1]) FROM employees GROUP BY department;

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

Aemployee_id

Bdepartment

Csalary

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Counting a column that can have NULLs leads to wrong counts.

Using COUNT(*) counts all rows per department correctly.

4fill in blank

hard

Fill both blanks to create a query that lists employee names and their salaries, ordered by salary descending.

SQL

SELECT [1], [2] FROM employees ORDER BY salary DESC;

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

Aname

Bsalary

Cemployee_id

Ddepartment

Attempts:

3 left

💡 Hint

Common Mistakes

Selecting wrong columns will not show the expected data.

Selecting name and salary shows the requested columns.

5fill in blank

hard

Fill all three blanks to create a query that shows department, average salary, and number of employees, only for departments with more than 5 employees.

SQL

SELECT [1], AVG([2]), COUNT([3]) FROM employees GROUP BY [1] HAVING COUNT([3]) > 5;

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

Adepartment

Bsalary

C*

Demployee_id

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong columns in COUNT or GROUP BY causes errors or wrong results.

This query groups by department, calculates average salary, counts employees, and filters groups with more than 5 employees.

Practice

1. Why is it important to use consistent query patterns when writing SQL queries?

easy

A. They make queries easier to read and maintain over time.

B. They always make queries run faster regardless of data size.

C. They allow you to write queries without knowing the database structure.

D. They prevent any syntax errors automatically.

Solution

1. Step 1: Understand the role of query patterns

   Query patterns help organize SQL commands in a clear and consistent way.

2. Step 2: Identify the main benefit of consistency

   Consistent patterns make queries easier to read and maintain, especially for teams or future updates.

3. Final Answer:

   They make queries easier to read and maintain over time. -> Option A

4. Quick Check:

   Consistency improves readability and maintenance [OK]

Hint: Think about teamwork and future you reading the query [OK]

Common Mistakes:

• Assuming patterns always improve speed
• Believing patterns remove need to know database
• Thinking patterns fix syntax errors automatically

2. Which of the following is the correct SQL syntax to select all columns from a table named employees?

easy

A. SELECT ALL FROM employees;

B. SELECT * FROM employees;

C. GET * FROM employees;

D. SELECT employees ALL;

Solution

1. Step 1: Recall the standard SQL SELECT syntax

   The correct way to select all columns is using SELECT * FROM table_name;.

2. Step 2: Check each option for syntax correctness

   SELECT * FROM employees; matches the correct syntax. Options B, C, and D use invalid keywords or order.

3. Final Answer:

   SELECT * FROM employees; -> Option B

4. Quick Check:

   Standard SELECT syntax uses * for all columns [OK]

Hint: Remember: SELECT * FROM table_name; is standard [OK]

Common Mistakes:

• Using GET instead of SELECT
• Using ALL without SELECT
• Incorrect keyword order

3. Given the table orders with columns order_id, customer_id, and amount, what will this query return?

SELECT customer_id, SUM(amount) FROM orders GROUP BY customer_id;

medium

A. A list of customers with the total amount they ordered.

B. All orders sorted by customer_id.

C. The average amount per order for each customer.

D. An error because SUM cannot be used without WHERE.

Solution

1. Step 1: Understand GROUP BY with aggregation

   The query groups rows by customer_id and sums the amount for each group.

2. Step 2: Interpret the SELECT clause

   It returns each customer_id with the total amount they ordered.

3. Final Answer:

   A list of customers with the total amount they ordered. -> Option A

4. Quick Check:

   GROUP BY + SUM gives totals per group [OK]

Hint: GROUP BY groups rows; SUM adds values per group [OK]

Common Mistakes:

• Confusing SUM with AVG
• Thinking GROUP BY sorts data
• Believing WHERE is needed for aggregation

4. Identify the error in this SQL query:

SELECT name, age FROM users WHERE age > 18 GROUP BY name;

medium

A. The query is correct as written.

B. WHERE clause cannot be used with GROUP BY.

C. SELECT must include only columns in GROUP BY.

D. GROUP BY should include all selected columns or use aggregation.

Solution

1. Step 1: Review SELECT and GROUP BY rules

   When using GROUP BY, all selected columns must be grouped or aggregated.

2. Step 2: Check the query columns

   The query selects name and age but only groups by name, so age is neither grouped nor aggregated.

3. Final Answer:

   GROUP BY should include all selected columns or use aggregation. -> Option D

4. Quick Check:

   All selected columns must be in GROUP BY or aggregated [OK]

Hint: All selected columns must be grouped or aggregated [OK]

Common Mistakes:

• Thinking WHERE conflicts with GROUP BY
• Ignoring missing aggregation for non-grouped columns
• Assuming query is correct without aggregation

5. You want to find customers who placed more than 3 orders and spent over $500 total. Which query pattern correctly achieves this?

SELECT customer_id, COUNT(*), SUM(amount) FROM orders GROUP BY customer_id HAVING COUNT(*) > 3 AND SUM(amount) > 500;

hard

A. Use WHERE to filter counts and sums after grouping.

B. Use GROUP BY only without HAVING or WHERE.

C. Use HAVING to filter groups based on aggregated values.

D. Use ORDER BY to filter groups with conditions.

Solution

1. Step 1: Understand filtering after aggregation

   WHERE filters rows before grouping; HAVING filters groups after aggregation.

2. Step 2: Identify correct clause for aggregated conditions

   Conditions on COUNT and SUM require HAVING, not WHERE.

3. Final Answer:

   Use HAVING to filter groups based on aggregated values. -> Option C

4. Quick Check:

   HAVING filters aggregated group results [OK]

Hint: Use HAVING for conditions on grouped data [OK]

Common Mistakes:

• Using WHERE instead of HAVING for aggregates
• Forgetting to group before filtering aggregates
• Using ORDER BY to filter data