SQL Exercises, Practice, Solution - Using Boolean and Relational operators
SQL [12 exercises with solution]
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1. Customers with Grade > 100
From the following table, write a SQL query to locate the details of customers with grade values above 100. Return customer_id, cust_name, city, grade, and salesman_id.
Sample table: customer
customer_id | cust_name | city | grade | salesman_id
-------------+----------------+------------+-------+-------------
3002 | Nick Rimando | New York | 100 | 5001
3007 | Brad Davis | New York | 200 | 5001
3005 | Graham Zusi | California | 200 | 5002
3008 | Julian Green | London | 300 | 5002
3004 | Fabian Johnson | Paris | 300 | 5006
3009 | Geoff Cameron | Berlin | 100 | 5003
3003 | Jozy Altidor | Moscow | 200 | 5007
3001 | Brad Guzan | London | | 5005
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2. New York Customers with Grade > 100
From the following table, write a SQL query to find all the customers in ‘New York’ city who have a grade value above 100. Return customer_id, cust_name, city, grade, and salesman_id.Sample table: customer
customer_id | cust_name | city | grade | salesman_id
-------------+----------------+------------+-------+-------------
3002 | Nick Rimando | New York | 100 | 5001
3007 | Brad Davis | New York | 200 | 5001
3005 | Graham Zusi | California | 200 | 5002
3008 | Julian Green | London | 300 | 5002
3004 | Fabian Johnson | Paris | 300 | 5006
3009 | Geoff Cameron | Berlin | 100 | 5003
3003 | Jozy Altidor | Moscow | 200 | 5007
3001 | Brad Guzan | London | | 5005
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3. New York or Grade > 100
From the following table, write a SQL query to find customers who are from the city of New York or have a grade of over 100. Return customer_id, cust_name, city, grade, and salesman_id.Sample table: customer
customer_id | cust_name | city | grade | salesman_id
-------------+----------------+------------+-------+-------------
3002 | Nick Rimando | New York | 100 | 5001
3007 | Brad Davis | New York | 200 | 5001
3005 | Graham Zusi | California | 200 | 5002
3008 | Julian Green | London | 300 | 5002
3004 | Fabian Johnson | Paris | 300 | 5006
3009 | Geoff Cameron | Berlin | 100 | 5003
3003 | Jozy Altidor | Moscow | 200 | 5007
3001 | Brad Guzan | London | | 5005
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4. New York or Not Grade > 100
From the following table, write a SQL query to find customers who are either from the city 'New York' or who do not have a grade greater than 100. Return customer_id, cust_name, city, grade, and salesman_id.Sample table: customer
customer_id | cust_name | city | grade | salesman_id
-------------+----------------+------------+-------+-------------
3002 | Nick Rimando | New York | 100 | 5001
3007 | Brad Davis | New York | 200 | 5001
3005 | Graham Zusi | California | 200 | 5002
3008 | Julian Green | London | 300 | 5002
3004 | Fabian Johnson | Paris | 300 | 5006
3009 | Geoff Cameron | Berlin | 100 | 5003
3003 | Jozy Altidor | Moscow | 200 | 5007
3001 | Brad Guzan | London | | 5005
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5. Not New York and Not Grade > 100
From the following table, write a SQL query to identify customers who are not from the city of 'New York' and do not have a grade value greater than 100. Return customer_id, cust_name, city, grade, and salesman_id.Sample table: customer
customer_id | cust_name | city | grade | salesman_id
-------------+----------------+------------+-------+-------------
3002 | Nick Rimando | New York | 100 | 5001
3007 | Brad Davis | New York | 200 | 5001
3005 | Graham Zusi | California | 200 | 5002
3008 | Julian Green | London | 300 | 5002
3004 | Fabian Johnson | Paris | 300 | 5006
3009 | Geoff Cameron | Berlin | 100 | 5003
3003 | Jozy Altidor | Moscow | 200 | 5007
3001 | Brad Guzan | London | | 5005
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6. Exclude Specific Orders
From the following table, write a SQL query to find details of all orders excluding those with ord_date equal to '2012-09-10' and salesman_id higher than 5005 or purch_amt greater than 1000.Return ord_no, purch_amt, ord_date, customer_id and salesman_id.Sample table : orders
ord_no purch_amt ord_date customer_id salesman_id ---------- ---------- ---------- ----------- ----------- 70001 150.5 2012-10-05 3005 5002 70009 270.65 2012-09-10 3001 5005 70002 65.26 2012-10-05 3002 5001 70004 110.5 2012-08-17 3009 5003 70007 948.5 2012-09-10 3005 5002 70005 2400.6 2012-07-27 3007 5001 70008 5760 2012-09-10 3002 5001 70010 1983.43 2012-10-10 3004 5006 70003 2480.4 2012-10-10 3009 5003 70012 250.45 2012-06-27 3008 5002 70011 75.29 2012-08-17 3003 5007 70013 3045.6 2012-04-25 3002 5001
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7. Salespeople with Commission Range 0.10-0.12
From the following table, write a SQL query to find the details of those salespeople whose commissions range from 0.10 to0.12. Return salesman_id, name, city, and commission.Sample table : salesman
salesman_id | name | city | commission
-------------+------------+----------+------------
5001 | James Hoog | New York | 0.15
5002 | Nail Knite | Paris | 0.13
5005 | Pit Alex | London | 0.11
5006 | Mc Lyon | Paris | 0.14
5007 | Paul Adam | Rome | 0.13
5003 | Lauson Hen | San Jose | 0.12
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8. Orders with Amount < 200 or Exclusions
From the following table, write a SQL query to find details of all orders with a purchase amount less than 200 or exclude orders with an order date greater than or equal to '2012-02-10' and a customer ID less than 3009. Return ord_no, purch_amt, ord_date, customer_id and salesman_id.Sample table : orders
ord_no purch_amt ord_date customer_id salesman_id ---------- ---------- ---------- ----------- ----------- 70001 150.5 2012-10-05 3005 5002 70009 270.65 2012-09-10 3001 5005 70002 65.26 2012-10-05 3002 5001 70004 110.5 2012-08-17 3009 5003 70007 948.5 2012-09-10 3005 5002 70005 2400.6 2012-07-27 3007 5001 70008 5760 2012-09-10 3002 5001 70010 1983.43 2012-10-10 3004 5006 70003 2480.4 2012-10-10 3009 5003 70012 250.45 2012-06-27 3008 5002 70011 75.29 2012-08-17 3003 5007 70013 3045.6 2012-04-25 3002 5001
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9. Conditional Orders Exclusions
From the following table, write a SQL query to find all orders that meet the following conditions. Exclude combinations of order date equal to '2012-08-17' or customer ID greater than 3005 and purchase amount less than 1000.Sample table : orders
ord_no purch_amt ord_date customer_id salesman_id ---------- ---------- ---------- ----------- ----------- 70001 150.5 2012-10-05 3005 5002 70009 270.65 2012-09-10 3001 5005 70002 65.26 2012-10-05 3002 5001 70004 110.5 2012-08-17 3009 5003 70007 948.5 2012-09-10 3005 5002 70005 2400.6 2012-07-27 3007 5001 70008 5760 2012-09-10 3002 5001 70010 1983.43 2012-10-10 3004 5006 70003 2480.4 2012-10-10 3009 5003 70012 250.45 2012-06-27 3008 5002 70011 75.29 2012-08-17 3003 5007 70013 3045.6 2012-04-25 3002 5001
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10. Orders with Achieved Percentage > 50%
Write a SQL query that displays order number, purchase amount, and the achieved and unachieved percentage (%) for those orders that exceed 50% of the target value of 6000.Sample table: orders
ord_no purch_amt ord_date customer_id salesman_id ---------- ---------- ---------- ----------- ----------- 70001 150.5 2012-10-05 3005 5002 70009 270.65 2012-09-10 3001 5005 70002 65.26 2012-10-05 3002 5001 70004 110.5 2012-08-17 3009 5003 70007 948.5 2012-09-10 3005 5002 70005 2400.6 2012-07-27 3007 5001 70008 5760 2012-09-10 3002 5001 70010 1983.43 2012-10-10 3004 5006 70003 2480.4 2012-10-10 3009 5003 70012 250.45 2012-06-27 3008 5002 70011 75.29 2012-08-17 3003 5007 70013 3045.6 2012-04-25 3002 5001
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11. Employees with Last Name Dosni or Mardy
From the following table, write a SQL query to find the details of all employees whose last name is ‘Dosni’ or ‘Mardy’. Return emp_idno, emp_fname, emp_lname, and emp_dept.Sample table : emp_details
EMP_IDNO EMP_FNAME EMP_LNAME EMP_DEPT --------- --------------- --------------- ---------- 127323 Michale Robbin 57 526689 Carlos Snares 63 843795 Enric Dosio 57 328717 Jhon Snares 63 444527 Joseph Dosni 47 659831 Zanifer Emily 47 847674 Kuleswar Sitaraman 57 748681 Henrey Gabriel 47 555935 Alex Manuel 57 539569 George Mardy 27 733843 Mario Saule 63 631548 Alan Snappy 27 839139 Maria Foster 57
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12. Employees in Department 47 or 63
rom the following table, write a SQL query to find the employees who work at depart 47 or 63. Return emp_idno, emp_fname, emp_lname, and emp_dept.
Sample table : emp_details
EMP_IDNO EMP_FNAME EMP_LNAME EMP_DEPT --------- --------------- --------------- ---------- 127323 Michale Robbin 57 526689 Carlos Snares 63 843795 Enric Dosio 57 328717 Jhon Snares 63 444527 Joseph Dosni 47 659831 Zanifer Emily 47 847674 Kuleswar Sitaraman 57 748681 Henrey Gabriel 47 555935 Alex Manuel 57 539569 George Mardy 27 733843 Mario Saule 63 631548 Alan Snappy 27 839139 Maria Foster 57
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