Querying Using SQL

Both A and R are true and R is the correct explanation of A.

Explanation
In SQL, the execution order of a SELECT statement is such that the ORDER BY clause is processed last, after all other clauses like FROM, WHERE, GROUP BY, HAVING, and SELECT have been executed. This is because the ORDER BY clause sorts the final result set produced by these earlier steps.

Both A and R are true and R is the correct explanation of A.

Explanation
MOD() and MIN() are numeric functions, but they are different types. MOD() is a single-row function, meaning it operates on individual rows and returns a result for each row. In contrast, MIN() is a group function (an aggregate function), which operates on a set of rows and returns a single result for the entire set.

A is false but R is true.

Explanation
The GROUP BY clause uses aggregate functions. It combines all those records that have identical values in a particular field or a group of fields and applies group-functions, resulting in one summary record per group.

Both A and R are true and R is the correct explanation of A.

Explanation
The GROUP BY clause yields summary results using group functions because it combines all records that have identical values in a particular field or a group of fields. This grouping results in one summary record per group if group functions are used with it.

Both A and R are true and R is the correct explanation of A.

Explanation
Both the WHERE and HAVING clauses can be used with the GROUP BY clause in a SELECT statement to filter and specify conditions for rows and groups, respectively. The WHERE clause filters rows before they are grouped, while the HAVING clause filters groups of rows after they have been grouped by the GROUP BY clause. This is because WHERE conditions cannot include aggregate functions, whereas HAVING conditions can include them.

Both A and R are true but R is not the correct explanation of A.

Explanation
Both WHERE and HAVING clauses are used to specify conditions in a SELECT query, they operate at different levels. The WHERE clause filters rows based on conditions applied to individual rows before grouping, while the HAVING clause filters groups based on conditions applied to the result of aggregation functions after grouping. Hence, they are not interchangeable.

Both A and R are true but R is not the correct explanation of A.

Explanation
Both WHERE and HAVING clauses are used to specify conditions in a SELECT query, they operate at different levels. The WHERE clause filters rows based on conditions applied to individual rows before grouping, while the HAVING clause filters groups based on conditions applied to the result of aggregation functions after grouping.

Yes, we can arrange the result set of an SQL query on multiple columns. We should specify the multiple column names in the ORDER BY clause along with the desired sort order.

For example, the following statement will sort the records in the data table first by the section column in ascending order and then by the marks column in descending order.

SELECT * FROM data
ORDER BY section ASC, marks DESC;

Data of table person on the basis of column emp_id will be sorted in ascending order

Reason — The significance of the ORDER BY clause in the given query is to sort the result set based on the values in the emp_id column in ascending order by default.

Firstly on emp_id and then on emp_name

Reason — The ORDER BY emp_id, emp_name clause sorts the result set firstly by the emp_id column. If there are rows with the same emp_id, those rows are further sorted by the emp_name column.

{1, 2, 3, 4, 6, 7, 9}

Reason — The ORDER BY clause in SQL sorts the result set based on the specified column(s) in ascending order by default. In the given query, the emp_id column is specified in the ORDER BY clause. Therefore, the query will sort the emp_id values in ascending order.

FIELD()

Reason — The FIELD() function in SQL allows to specify a custom sort order by listing the values in the order we want them to appear.
For example, the following statement selects column fruit_name from the fruits table and orders the result set based on the custom sort order specified by the FIELD() function.

SELECT * FROM fruits
ORDER BY FIELD(fruit_name, 'Apple', 'Banana', 'Orange', 'Grapes');

Ascending

Reason — By default, ORDER BY clause lists the results in ascending order.

ORDER BY

Reason — The ORDER BY clause in SQL is used to sort the result set in either ascending or descending order.

GROUP BY

Reason — The GROUP BY clause in SQL is used to create summary results by grouping together all records that share identical values in a particular field or a group of fields.

Desc, Asc
Descending, Ascending

Reason — To display the salary from greater to smaller i.e., in descending order we use DESC or descending keyword and to sort name in alphabetical order i.e., in ascending order we use ASC or ascending keyword.

SELECT city, temperature FROM weather ORDER BY city ;

Reason — The query SELECT city, temperature FROM weather ORDER BY city ; selects the city and temperature columns from the weather table and sorts the result set by the city column in ascending order.

COUNT(*)

Reason — The SQL function COUNT(*) is used to count the number of rows in an SQL query, including duplicates and rows with NULL values.

SELECT COUNT(*) FROM ORDERS ;

Reason — The COUNT() function in SQL counts the number of rows or records in a table. When used with the asterisk (*), it counts all rows in the table, including duplicates and nulls. Therefore, SELECT COUNT(*) FROM ORDERS will return the total number of records in the ORDERS table.

SELECT COUNT(Project) FROM Students

Reason — The COUNT() function in SQL counts the number of non-null records in a column. By using COUNT(Project), we are counting the number of rows where the Project column is not null in the student table.

With

Reason — Aggregate functions in SQL include AVG, COUNT, MAX, MIN, and SUM.

Count(*)

Reason — All aggregate functions, except for COUNT(*), ignore null values in their input collection. COUNT(*) returns all rows, including duplicates and nulls.

All of these

Reason — All aggregate functions, except for COUNT(*), ignore null values in their input collection.

Acts like a WHERE clause but is used for groups rather than rows.

Reason — The HAVING clause places conditions on groups in contrast to WHERE clause that places conditions on individual rows.

Having, where

Reason — Aggregate functions can be used in the select list or the HAVING clause of a select statement. But they cannot be used in a WHERE clause. The reason for this is that the WHERE clause filters rows before any grouping or aggregation occurs, while HAVING clause applies conditions to groups after the data has been grouped using the GROUP BY clause.

Having

Reason — The HAVING clause applies conditions to groups after the data has been grouped using the GROUP BY clause in SQL queries.

GROUP BY

Reason — Aggregate functions (such as COUNT, SUM, AVG, MAX, MIN) are used with the GROUP BY clause to perform operations on each group of rows. The GROUP BY clause groups rows that have the same values in specified columns into summary rows.

To filter out the summary group

Reason — The HAVING clause is used to filter out summary groups in a SELECT query that involves aggregate functions and the GROUP BY clause.

False

Reason — The HAVING clause places conditions on groups in contrast to WHERE clause that places conditions on individual rows. While WHERE conditions cannot include aggregate functions, HAVING conditions can include aggregate functions. Hence, WHERE and HAVING clauses cannot be used interchangeably in SELECT queries.

SELECT DEPT, AVG(SAL) FROM EMP GROUP BY DEPT HAVING COUNT(*) > 5;

Reason — In the above query, the WHERE clause cannot be used with aggregate functions like COUNT(*) because it is meant to filter individual rows before the aggregation. On the other hand, the HAVING clause filters the groups created by the GROUP BY clause to include only those departments (DEPT) that have more than five employees.

False

Reason — In SQL, the ORDER BY clause is used to sort the rows of the result relation produced by a SELECT statement. It allows sorting by one or more columns in ascending or descending order.

True

Reason — The HAVING clause in SQL is used to filter groups based on specified conditions, while the WHERE clause filters individual rows. This means that the HAVING clause works with grouped data, applying conditions to groups that meet certain criteria, whereas the WHERE clause applies conditions to individual rows before any grouping occurs.

True

Reason — The DISTINCT keyword in SQL is used to eliminate duplicate rows from the results of a query. Therefore, when a user wants to ensure that only unique rows are returned, they must use the DISTINCT qualifier in their SQL statement.

False

Reason — In SQL, both DISTINCT and ALL keywords can be specified only once in a given SELECT clause.

False

Reason — In SQL, DISTINCT and ALL cannot be used together on a single field in a SELECT statement. As DISTINCT eliminates duplicate rows from the results, while ALL includes all rows, including duplicates. Therefore, attempting to use them together on the same field would result in a syntax error.

True

Reason — When we use COUNT(fieldname), it counts only the rows where the specified field (fieldname) is not null. It does ignore null values for that specific field during counting.

True

Reason — The aggregate functions SUM, AVG, MIN, and MAX are designed to work with numeric columns in SQL. They expect numeric values as arguments and return numeric results.

True

Reason — The SQL statement SELECT Salary + Comm AS Total FROM Emp; adds the values of the Salary and Comm columns from each row together and lists the results in a column named Total. This is achieved by using the "+" operator to perform arithmetic addition on the values of the specified fields.

True

Reason — The ORDER BY can be combined with the SELECT statements in SQL to sort query results based on one or more columns.

True

Reason — The SQL keyword GROUP BY clause instructs the DBMS to combine all those rows that have identical values in a particular column or a group of columns.

The difference in output is due to how COUNT() function behaves with different types of data in the specified column.

Command1: Select count(*) from employee; : This command counts all rows in the employee table, regardless of whether any column contains NULL values or not. When we specify an asterisk (*) with COUNT(), it returns the total number of rows in the table, including duplicates and NULL values. So, it returns the total number of rows in the table, which is 4.

Command2: Select count(commission) from employee; : If we specify argument (expr) in COUNT() function, it returns rows where expr is not null. This command counts the number of non-NULL values in the commission column. Therefore, it returns a count of 3 instead of 4 due to the presence of a NULL value in that column in one of the rows.

In SQL, the ORDER BY clause is used to sort the rows of the result set produced by a SELECT statement. We can specify one or more columns in the ORDER BY clause to sort the result set either in ascending (default) or descending order.

The default sort order of ORDER BY clause is ascending order.

The GROUP BY clause in SQL is used to combine all records that have identical values in a particular field or group of fields. This grouping results in one summary record per group if group functions, such as aggregate functions, are used with it.

Aggregate functions in SQL work with data from multiple rows at a time and return a single aggregated value. They are used to perform calculations across multiple rows and return a summary result for that group.

Examples of aggregate functions include SUM(), COUNT(), MAX(), MIN(), AVG() etc.

Aggregate functions are used with the GROUP BY and HAVING clauses to apply conditions on grouped data.

The error in Harjat's query is the order of the clauses. The GROUP BY clause should come after the FROM clause. The corrected query is as follows :

SELECT dept, sum(comm)
FROM EMP
GROUP BY dept;

SELECT MAX(PRICE) FROM BOOK_INFORMATION;

1. SELECT BOOK_ID, BOOK_TITLE, MAX(PRICE) FROM BOOK_INFORMATION; — This query selects the BOOK_ID, BOOK_TITLE, and maximum PRICE from the BOOK_INFORMATION table. However, the requirement is to find the highest price only.
2. SELECT MAX(PRICE) FROM BOOK_INFORMATION; — This query selects the maximum PRICE from the BOOK_INFORMATION table using the MAX() aggregate function. This option is correct because it directly retrieves the highest price among all the books listed in the BOOK_INFORMATION table, which is what the question asks for.
3. SELECT MAXIMUM(PRICE) FROM BOOK_INFORMATION; — There is no MAXIMUM() function in SQL.
4. SELECT PRICE FROM BOOK_INFORMATION ORDER BY PRICE DESC; — This query selects all prices from the BOOK_INFORMATION table and orders them in descending order using ORDER BY PRICE DESC but it doesn't directly give the highest price.

SELECT STORE_ID, SUM(SALES_AMOUNT) FROM SALES GROUP BY STORE_ID;

1. SELECT STORE_ID, SUM(SALES_AMOUNT) FROM SALES; — This statement selects the store_ID and calculates the total sales amount using SUM() aggregate function from the SALES table. It does not group the results by store ID, so it will return a single row with the total sales amount across all stores.
2. SELECT STORE_ID, SUM(SALES_AMOUNT) FROM SALES ORDER BY STORE_ID; — This statement selects the store_ID and calculates the total sales amount using SUM() aggregate function from the SALES table and uses an ORDER BY clause to sort the results by store ID. However, it still doesn't group the results by store_ID.
3. SELECT STORE_ID, SUM(SALES_AMOUNT) FROM SALES GROUP BY STORE_ID; — This statement selects the store_ID and calculates the total sales amount using SUM() aggregate function from the SALES table and uses the GROUP BY clause to group the results by store ID. It calculates the total sales amount for each store ID separately. As a result, it calculates the total sales amount for each unique store ID separately, providing a breakdown of sales amounts for each store in the dataset.
4. SELECT STORE_ID, SUM(SALES_AMOUNT) FROM SALES HAVING UNIQUE STORE_ID; — This statement is incorrect because the HAVING clause is used for filtering grouped data based on a condition, not for identifying unique values. Also, "UNIQUE STORE_ID" is not a valid condition in SQL.

SELECT STORE_ID, SUM(SALES_AMOUNT) FROM SALES
GROUP BY STORE_ID HAVING SUM(SALES_AMOUNT) > 5000;

1. SELECT STORE_ID, SUM(SALES_AMOUNT) FROM SALES GROUP BY STORE_ID HAVING SUM(SALES_AMOUNT) > 5000; — This statement selects the store ID and calculates the total sales amount for each store using the SUM() aggregate function. The GROUP BY STORE_ID clause ensures that the results are grouped by store ID. The HAVING SUM(SALES_AMOUNT) > 5000 condition then filters the grouped data, selecting only those stores whose total sales amount is over 5000.
2. SELECT STORE_ID, SUM(SALES_AMOUNT) FROM SALES GROUP BY STORE_ID HAVING SALES_AMOUNT > 5000; — This option is incorrect because the HAVING clause cannot directly reference SALES_AMOUNT without an aggregate function like SUM() since SUM(SALES_AMOUNT) is used in the SELECT statement.
3. SELECT STORE_ID, SUM(SALES_AMOUNT) FROM SALES WHERE SUM(SALES_AMOUNT) > 5000 GROUP BY STORE_ID; — This option is incorrect because aggregate functions like SUM(SALES_AMOUNT) cannot be used directly in the WHERE clause. The WHERE clause is used for filtering individual rows before grouping.
4. SELECT STORE_ID, SUM(SALES_AMOUNT) FROM SALES WHERE SALES_AMOUNT > 5000 GROUP BY STORE_ID; — This option is incorrect because it tries to filter individual sales amounts (SALES_AMOUNT) directly without using the SUM() aggregate function to calculate the total sales amount for each store. The GROUP BY STORE_ID clause is used for grouping after the filtering, which is not the correct approach for filtering based on the total sales amount.

SELECT COUNT(DISTINCT STORE_ID) FROM SALES;

1. SELECT COUNT(STORE_ID) FROM SALES; — This query uses the COUNT() aggregate function with the STORE_ID column in the SELECT statement. It counts the number of non-null values in the STORE_ID column, and this count includes duplicates.
2. SELECT COUNT(DISTINCT STORE_ID) FROM SALES; — This option uses COUNT(DISTINCT STORE_ID) to count the number of unique store IDs in the SALES table. The DISTINCT keyword ensures that only distinct (unique) values are counted, avoiding overcounting due to duplicates.
3. SELECT DISTINCT STORE_ID FROM SALES; — This option selects distinct (unique) store IDs from the SALES table but does not count or provide the total number of stores.
4. SELECT COUNT(STORE_ID) FROM SALES GROUP BY STORE_ID; — This option attempts to count the number of occurrences of each store ID by using COUNT(STORE_ID) and grouping by store ID with GROUP BY STORE_ID. However, this results in a count for each unique store ID separately, not the total number of stores in the table.

SELECT STORE_ID, SUM(SALES_AMOUNT) FROM SALES   
WHERE STORE_ID IN (25, 45) GROUP BY STORE_ID;

1. SELECT STORE_ID, SUM(SALES_AMOUNT) FROM SALES WHERE STORE_ID IN (25, 45) GROUP BY STORE_ID; — This query uses the IN operator to filter rows where the STORE_ID is either 25 or 45. It then calculates the total sales amount for each store ID using SUM(SALES_AMOUNT) and groups the results by STORE_ID. This query correctly finds the total sales amount for Store ID 25 and Store ID 45 separately.
2. SELECT STORE_ID, SUM(SALES_AMOUNT) FROM SALES GROUP BY STORE_ID HAVING STORE_ID IN (25, 45); — This query will also give the required output but it is inefficient because it first retrieves all rows from the "SALES" table, then groups the results by store ID, and finally filters the result set to include only store IDs 25 and 45. The inefficiency arises from the fact that it processes all rows in the "SALES" table before filtering out the unnecessary data. This means that it processes more data than necessary, which can be wasteful in terms of computational resources and time. A more efficient approach would be to select only the rows having store IDs 25 and 45 first (using WHERE clause), and then perform the aggregation.
3. SELECT STORE_ID, SUM(SALES_AMOUNT) FROM SALES WHERE STORE_ID IN (25, 45); — This query filters rows where the STORE_ID is either 25 or 45 and calculates the total sales amount for these store IDs using SUM(SALES_AMOUNT). However, it doesn't include a GROUP BY clause, so it would return a single row with the total sales amount for both Store ID 25 and Store ID 45 combined.
4. SELECT STORE_ID, SUM(SALES_AMOUNT) FROM SALES WHERE STORE_ID = 25 AND STORE_ID = 45 GROUP BY STORE_ID; — This query filter rows where the STORE_ID is both 25 and 45 simultaneously using STORE_ID = 25 AND STORE_ID = 45. However, this condition is impossible to satisfy because a single value cannot be both 25 and 45 at the same time. Therefore, this query would not return any results.

SELECT AVG(EXAM_SCORE) FROM EXAM_RESULTS GROUP BY EXAM_ID HAVING EXAM_ID = 1;

+-----------------+
| AVG(EXAM_SCORE) |
+-----------------+
|         83.2500 |
+-----------------+

1. SELECT AVG(EXAM_SCORE) FROM EXAM_RESULTS; — This statement calculates the average exam score across all exam IDs in the EXAM_RESULTS table.
2. SELECT AVG(EXAM_SCORE) FROM EXAM_RESULTS GROUP BY EXAM_ID WHERE EXAM_ID = 1; — This statement is incorrect because the WHERE clause should come before the GROUP BY clause. Additionally, grouping by EXAM_ID and then trying to filter by EXAM_ID = 1 within the GROUP BY clause will result in an error because grouping should be done before filtering.
3. SELECT AVG(EXAM_SCORE) FROM EXAM_RESULTS GROUP BY EXAM_ID HAVING EXAM_ID = 1; — This query groups the exam results by EXAM_ID and then calculates the average exam score for each group. The HAVING clause filters the groups and returns only those where the EXAM_ID is equal to 1, giving us the average exam score for the exam with EXAM_ID equal to 1.
4. SELECT COUNT(EXAM_SCORE) FROM EXAM_RESULTS WHERE EXAM_ID = 1; — This statement calculates the count of exam scores for EXAM_ID = 1, but it doesn't calculate the average score.

SELECT EXAM_ID, COUNT(DISTINCT STU_ID) FROM EXAM_RESULTS GROUP BY EXAM_ID;

+---------+------------------------+
| EXAM_ID | COUNT(DISTINCT STU_ID) |
+---------+------------------------+
|       1 |                      4 |
|       2 |                      5 |
+---------+------------------------+

1. SELECT COUNT(DISTINCT STU_ID) FROM EXAM_RESULTS GROUP BY EXAM_ID; — It groups the EXAM_RESULTS table by EXAM_ID and uses the COUNT(DISTINCT STU_ID) function to count the number of distinct student IDs for each exam. However, the result set does not include EXAM_ID.
2. SELECT EXAM_ID, MAX(STU_ID) FROM EXAM_RESULTS GROUP BY EXAM_ID; — This query groups the results by EXAM_ID and then selects the maximum STU_ID for each exam. However, this doesn't provide the count of students who took each exam, as it gives the maximum student ID instead of counting the distinct student IDs.
3. SELECT EXAM_ID, COUNT(DISTINCT STU_ID) FROM EXAM_RESULTS GROUP BY EXAM_ID; — It groups the EXAM_RESULTS table by EXAM_ID and uses the COUNT(DISTINCT STU_ID) function to count the number of distinct student IDs for each exam. The result set includes the EXAM_ID and the count of students who took each exam.
4. SELECT EXAM_ID, MIN(STU_ID) FROM EXAM_RESULTS GROUP BY EXAM_ID; — This query groups the results by EXAM_ID and selects the minimum STU_ID for each exam. It does not provide information about the number of students who took each exam.

SELECT * FROM EXAM_RESULTS WHERE LNAME LIKE 'L%' ;

+--------+-------+-------+---------+------------+
| stu_id | fname | lname | exam_id | exam_score |
+--------+-------+-------+---------+------------+
|     10 | LAURA | LYNCH |       1 |         90 |
|     10 | LAURA | LYNCH |       2 |         85 |
+--------+-------+-------+---------+------------+

1. SELECT * FROM EXAM_RESULTS WHERE LNAME LIKE 'L%'; — The LIKE operator is used for pattern matching in SQL. '%' is a wildcard character that matches zero or more characters. 'L%' specifies that the last name (LNAME) should start with 'L' followed by zero or more characters. The SELECT * statement retrieves all columns from the EXAM_RESULTS table for the matching records.
2. SELECT * FROM EXAM_RESULTS WHERE LNAME LIKE 'L'; — This query attempts to select all columns (*) from the EXAM_RESULTS table where the last name (LNAME) is exactly equal to 'L'. However, when using the LIKE operator in SQL for pattern matching, we use wildcard characters (%) to represent unknown parts of a string.
3. SELECT * FROM EXAM_RESULTS WHERE LNAME 'L'; — This statement contains a syntax error. In SQL, when using the WHERE clause to filter records based on a specific condition, we need to use comparison operators or functions to define the condition properly.
4. SELECT * FROM EXAM_RESULTS WHERE LNAME <> 'L'; — This query retrieves records where the last name is not equal to 'L'. It does not specifically look for last names starting with 'L', so it's not the correct option for the given requirement.

+-----------------+
| MAX(EXAM_SCORE) |
+-----------------+
|              95 |
+-----------------+

The above SQL query calculates the maximum exam score for EXAM_ID 1 from the EXAM_RESULTS table, by grouping results based on EXAM_ID and filtering using HAVING.

1.

+------------------------+
| COUNT(DISTINCT SPORTS) |
+------------------------+
|                      4 |
+------------------------+

The SQL query SELECT COUNT(DISTINCT SPORTS) FROM Club ; calculates the count of unique values in the 'SPORTS' column of the 'Club' table. This query helps us to get information about the number of sports offered by the club.

2.

+----------+
| MIN(Age) |
+----------+
|       34 |
+----------+

The SQL query SELECT MIN(Age) FROM CLUB WHERE Sex = 'F' ; retrieves the minimum Age from the 'CLUB' table where the 'Sex' column has the value 'F'. This query gives us the age of the youngest female coach in the club.

3.

+-----------+
| AVG(Pay)  |
+-----------+
| 1100.0000 |
+-----------+

The SQL query SELECT AVG(Pay) FROM CLUB WHERE Sports = 'KARATE' ; calculates the average value of the 'Pay' column from the 'CLUB' table where the 'Sports' column has the value 'KARATE'. This query helps us to get information about the average pay of karate coaches in the club.

4.

+----------+
| SUM(Pay) |
+----------+
|     7800 |
+----------+

The SQL query SELECT SUM(Pay) FROM CLUB WHERE Dateofapp > '1998-01-31'; calculates the sum of the 'Pay' column from the 'CLUB' table where the 'Dateofapp' column has a date value greater than '1998-01-31'. This query gives us the total pay of all the coaches who joined after 31/01/1998.

(i)

+--------------+
| MIN(AvgMark) |
+--------------+
| 64.4         |
+--------------+

(ii)

+--------------+
| SUM(Stipend) |
+--------------+
|         1150 |
+--------------+

(iii)

+--------------+
| AVG(Stipend) |
+--------------+
|          475 |
+--------------+

(iv) It will give an error because the COUNT function requires an argument specifying what to count. Additionally, the DISTINCT keyword is followed by a column name to count the distinct values of that column.

1.

SELECT EMPLOYEE.EMPLOYEEID, EMPLOYEE.NAME, EMPLOYEE.JOBID, JOB.JOBTITLE
FROM EMPLOYEE, JOB
WHERE EMPLOYEE.JOBID = JOB.JOBID;

+------------+-------------------+-------+--------------------------+
| EMPLOYEEID | NAME              | JOBID | JOBTITLE                 |
+------------+-------------------+-------+--------------------------+
| E1         | SUMIT SINHA       |   102 | VICE PRESIDENT           |
| E2         | VIJAY SINGH TOMAR |   101 | PRESIDENT                |
| E3         | AJAY RAJPAL       |   103 | ADMINISTARTION ASSISTANT |
| E4         | MOHIT RAMNANI     |   102 | VICE PRESIDENT           |
| E5         | SHAILJA SINGH     |   103 | ADMINISTARTION ASSISTANT |
+------------+-------------------+-------+--------------------------+

2.

SELECT EMPLOYEE.NAME, EMPLOYEE.SALES, JOB.JOBTITLE
FROM EMPLOYEE, JOB 
WHERE EMPLOYEE.JOBID = JOB.JOBID
AND EMPLOYEE.SALES > 1300000;

+---------------+---------+--------------------------+
| NAME          | SALES   | JOBTITLE                 |
+---------------+---------+--------------------------+
| AJAY RAJPAL   | 1400000 | ADMINISTARTION ASSISTANT |
| SHAILJA SINGH | 1450000 | ADMINISTARTION ASSISTANT |
+---------------+---------+--------------------------+

3.

SELECT EMPLOYEE.NAME, JOB.JOBTITLE
FROM EMPLOYEE, JOB 
WHERE EMPLOYEE.JOBID = JOB.JOBID
AND EMPLOYEE.NAME LIKE '%SINGH%';

+-------------------+--------------------------+
| NAME              | JOBTITLE                 |
+-------------------+--------------------------+
| VIJAY SINGH TOMAR | PRESIDENT                |
| SHAILJA SINGH     | ADMINISTARTION ASSISTANT |
+-------------------+--------------------------+

4. In the given tables, EMPLOYEE and JOB, the JOBID column in the EMPLOYEE table is a foreign key referencing the JOBID column in the JOB table.

5.

UPDATE EMPLOYEE
SET JOBID = 104
WHERE EMPLOYEEID = 'E4';

SELECT * FROM EMPLOYEE ;

+------------+-------------------+---------+-------+
| EMPLOYEEID | NAME              | SALES   | JOBID |
+------------+-------------------+---------+-------+
| E1         | SUMIT AINHA       | 1100000 |   102 |
| E2         | VIJAY SINGH TOMAR | 1300000 |   101 |
| E3         | AJAY RAJPAL       | 1400000 |   103 |
| E4         | MOHIT RAMNANI     | 1250000 |   104 |
| E5         | SHAILJA SINGH     | 1450000 |   103 |
+------------+-------------------+---------+-------+

SELECT job, AVG(Sal) AS AvgSalary 
FROM Empl 
GROUP BY job HAVING COUNT(*) > 3;

+----------+-----------+
| job      | AvgSalary |
+----------+-----------+
| CLERK    |    1037.5 |
| SALESMAN |      1400 |
+----------+-----------+

SELECT Job 
FROM Empl 
GROUP BY Job HAVING MAX(Sal) >= 3000;

+-----------+
| Job       |
+-----------+
| PRESIDENT |
| ANALYST   |
+-----------+

SELECT COUNT(*) AS NumManagers
FROM EmpSalary
WHERE Designation = 'Manager';

+-------------+
| NumManagers |
+-------------+
|           3 |
+-------------+

SELECT deptno, COUNT(*) AS employee_count
FROM EMPL
GROUP BY deptno;

+--------+----------------+
| deptno | employee_count |
+--------+----------------+
|     20 |              5 |
|     30 |              6 |
|     10 |              3 |
+--------+----------------+

SELECT deptno, SUM(sal) AS total_salary
FROM EMPL
GROUP BY deptno;

+--------+--------------+
| deptno | total_salary |
+--------+--------------+
|     20 |        10885 |
|     30 |         9400 |
|     10 |         8750 |
+--------+--------------+

SELECT deptno, MAX(sal) AS max_salary
FROM EMPL
GROUP BY deptno;

+--------+------------+
| deptno | max_salary |
+--------+------------+
|     20 |       3000 |
|     30 |       2850 |
|     10 |       5000 |
+--------+------------+

(i)

+------------+
| SUM(PRICE) |
+------------+
|       2600 |
+------------+

(ii)

+----------------+----------+
| DESCRIPTION    | TYPE     |
+----------------+----------+
| INFORMAL PANT  | COTTON   |
| INFORMAL SHIRT | COTTON   |
| FORMAL PANT    | TERELENE |
+----------------+----------+

(iii)

+------------+
| MAX(FCODE) |
+------------+
| F04        |
+------------+

(iv)

+-----------------------+
| COUNT(DISTINCT PRICE) |
+-----------------------+
|                     7 |
+-----------------------+

(i)

+-----------+
| City      |
+-----------+
| Delhi     |
| Mumbai    |
| Bangalore |
+-----------+

(ii)

+--------------+------------+------------+----------+
| Manufacturer | MAX(Price) | Min(Price) | Count(*) |
+--------------+------------+------------+----------+
| ABC          |         55 |         45 |        2 |
| XYZ          |        120 |         95 |        2 |
| LAK          |         40 |         40 |        1 |
+--------------+------------+------------+----------+

(iii)

+---------------+---------------+
| ClientName    | ProductName   |
+---------------+---------------+
| Cosmetic Shop | Face Wash     |
| Total Health  | Bath Soap     |
| Live Life     | Shampoo       |
| Pretty Woman  | Face Wash     |
| Dreams        | Talcum Powder |
+---------------+---------------+

(iv)

+---------------+-----------+
| ProductName   | Price * 4 |
+---------------+-----------+
| Bath Soap     |       220 |
| Face Wash     |       180 |
| Face Wash     |       380 |
| Shampoo       |       480 |
| Talcum Powder |       160 |
+---------------+-----------+

(i)

+-----------+
| City      |
+-----------+
| DELHI     |
| MUMBAI    |
| BANGALORE |
+-----------+

(ii)

+-------------------+------------+----------+
| ItemName          | MAX(Price) | Count(*) |
+-------------------+------------+----------+
| LAPTOP            |      57000 |        2 |
| PERSONAL COMPUTER |      37000 |        3 |
+-------------------+------------+----------+

(iii)

+--------------+--------------+
| CustomerName | Manufacturer |
+--------------+--------------+
| N ROY        | PQR          |
| H SINGH      | XYZ          |
| R PANDEY     | COMP         |
| C SHARMA     | PQR          |
| K AGARWAL    | ABC          |
+--------------+--------------+

(iv)

+-------------------+-------------+
| ItemName          | Price * 100 |
+-------------------+-------------+
| LAPTOP            |     5500000 |
| PERSONAL COMPUTER |     3500000 |
+-------------------+-------------+

(i)

+------------+
| SenderCity |
+------------+
| MUMBAI     |
| NEW DELHI  |
+------------+

(ii)

+------------+-----------+
| SenderName | RecName   |
+------------+-----------+
| R JAIN     | H SINGH   |
| S JHA      | P K SWAMY |
+------------+-----------+

(iii)

+------------+-----------------------+
| RecName    | RecAddress            |
+------------+-----------------------+
| S MAHAJAN  | 116, A VIHAR          |
| S TRIPATHI | 13, B1 D, MAYUR VIHAR |
+------------+-----------------------+

(iv)

+-------+------------+
| RecID | RecName    |
+-------+------------+
| ND08  | S MAHAJAN  |
| ND48  | S TRIPATHI |
+-------+------------+