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SQL TEST base on Aggregate function

1. From the following table, write a SQL query to calculate total purchase amount of all orders. Return total purchase amount.

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

Sample Output:

sum
17541.18



2. From the following table, write a SQL query to calculate the average purchase amount of all orders. Return average purchase amount. 

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

Sample Output:

avg
1461.7650000000000000






3. From the following table, write a SQL query that counts the number of unique salespeople. Return number of salespeople.  

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
Sample Output:
count
6




4. From the following table, write a SQL query to count the number of customers. Return number of customers.  

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
Sample Output:
count
8

5. From the following table, write a SQL query to determine the number of customers who received at least one grade for their activity.  

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

Sample Output:

count
7


6. From the following table, write a SQL query to find the maximum purchase amount.  

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

Sample Output:
max
5760.00



7. From the following table, write a SQL query to find the minimum purchase amount. 

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

Sample Output:

min
65.26


8. From the following table, write a SQL query to find the highest grade of the customers in each city. Return city, maximum grade.  

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
Sample Output:

city		max
London		300
Paris		300
New York	200
California	200
Berlin		100
Moscow		200






9. From the following table, write a SQL query to find the highest purchase amount ordered by each customer. Return customer ID, maximum purchase amount. 

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
Sample Output:

customer_id	max
3007		2400.60
3008		250.45
3002		5760.00
3001		270.65
3009		2480.40
3004		1983.43
3003		75.29
3005		948.50


10. From the following table, write a SQL query to find the highest purchase amount ordered by each customer on a particular date. Return, order date and highest purchase amount.

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
Sample Output:

customer_id	ord_date	max
3002		2012-10-05	65.26
3003		2012-08-17	75.29
3005		2012-10-05	150.50
3007		2012-07-27	2400.60
3009		2012-08-17	110.50
3001		2012-09-10	270.65
3002		2012-09-10	5760.00
3005		2012-09-10	948.50
3009		2012-10-10	2480.40
3008		2012-06-27	250.45
3004		2012-10-10	1983.43
3002		2012-04-25	3045.60
Q11. From the following table, write a SQL query to select all the salespeople. Return salesman_id, name, city, commission with the percent sign (%).
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
Sample Output:
salesman_id	name		city		?column?	?column?
5001		James Hoog	New York	%		15.00
5002		Nail Knite	Paris		%		13.00
5005		Pit Alex	London		%		11.00
5006		Mc Lyon		Paris		%		14.00
5007		Paul Adam	Rome		%		13.00
5003		Lauson Hen	San Jose	%		12.00

Q12. From the following table, write a SQL query to find the number of orders booked for each day. Return the result in a format like "For 2001-10-10 there are 15 orders".". 
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
Sample Output:
?column?	ord_date	?column?	count	?column?
For		2012-04-25	,there are	1	orders.
For		2012-06-27	,there are	1	orders.
For		2012-07-27	,there are	1	orders.
For		2012-08-17	,there are	2	orders.
For		2012-09-10	,there are	3	orders.
For		2012-10-05	,there are	2	orders.
For		2012-10-10	,there are	2	orders.


Q13. From the following table, write a SQL query to find all the orders. Sort the result-set in ascending order by ord_no. Return all fields.  
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

Q14. From the following table, write a SQL query to find all the orders. Sort the result-set in descending order by ord_date. Return all fields. 
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
Sample Output:
ord_no	purch_amt	ord_date	customer_id	salesman_id
70010	1983.43		2012-10-10	3004		5006
70003	2480.40		2012-10-10	3009		5003
70002	65.26		2012-10-05	3002		5001
70001	150.50		2012-10-05	3005		5002
70009	270.65		2012-09-10	3001		5005
70008	5760.00		2012-09-10	3002		5001
70007	948.50		2012-09-10	3005		5002
70011	75.29		2012-08-17	3003		5007
70004	110.50		2012-08-17	3009		5003
70005	2400.60		2012-07-27	3007		5001
70012	250.45		2012-06-27	3008		5002
70013	3045.60		2012-04-25	3002		5001

Q15. From the following table, write a SQL query to find all the orders. Sort the result-set in descending order by ord_date and purch_amt. Return all fields.  
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
Sample Output:
ord_no	purch_amt	ord_date	customer_id	salesman_id
70013	3045.60		2012-04-25	3002		5001
70012	250.45		2012-06-27	3008		5002
70005	2400.60		2012-07-27	3007		5001
70004	110.50		2012-08-17	3009		5003
70011	75.29		2012-08-17	3003		5007
70008	5760.00		2012-09-10	3002		5001
70007	948.50		2012-09-10	3005		5002
70009	270.65		2012-09-10	3001		5005
70001	150.50		2012-10-05	3005		5002
70002	65.26		2012-10-05	3002		5001
70003	2480.40		2012-10-10	3009		5003
70010	1983.43		2012-10-10	3004		5006



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