36 practice questions with answers for SQL interviews in Bhubaneswar.
SQL jobs in Bhubaneswar
Bhubaneswar is eastern India's emerging IT hub, with campuses of large IT-services companies in Infocity and Info Valley. Freshers make up a big share of hiring, so fundamentals and aptitude rounds matter most.
Below are 36 questions that match the mix of service, product and domain employers hiring data analyst (sql)s in Bhubaneswar.
Solved for BhubaneswarStart practicing
3 QUESTIONS FREESign up to unlock the full question bank and track your progress.Unlock every question and answer, then continue practising company-wise and package-wise.
✓ 3 questions free • Unlock the rest in seconds
01
An interviewer asks to show each employee's salary next to their department's average. How do you do it?
Use a window aggregate: SELECT name, department_id, salary, AVG(salary) OVER (PARTITION BY department_id) AS dept_avg, salary - AVG(salary) OVER (PARTITION BY department_id) AS diff FROM employees. Unlike GROUP BY, every employee row is kept. Mention that a join with a grouped subquery produces the same result but is longer.
02
How do you read an EXPLAIN ANALYZE output in PostgreSQL?
EXPLAIN ANALYZE
SELECT * FROM orders WHERE customer_id = 101;
Read the tree from the innermost node outward. Check node types such as Seq Scan, Index Scan or Hash Join, compare estimated rows with actual rows, and look at actual time per node. Large estimate errors suggest stale statistics, and Seq Scan on a selective filter suggests a missing index.
03
Produce region subtotals and a grand total in one query.
SELECT COALESCE(region, 'All Regions') AS region,
COALESCE(city, 'Subtotal') AS city,
SUM(sales) AS sales
FROM store_sales
GROUP BY ROLLUP(region, city)
ORDER BY region, city;
In MySQL use GROUP BY region, city WITH ROLLUP. Use GROUPING() to label totals when real NULLs exist.
04
What is the difference between GROUP BY and window functions?
GROUP BY collapses rows into one row per group, so detail columns are lost. Window functions keep every row and add the computed value alongside, such as each employee with their department average. You can combine both: group first, then apply a window over the grouped result, for example percent of total using SUM(SUM(sales)) OVER ().
05
Why is NOT IN dangerous when the subquery contains NULL?
If the subquery returns any NULL, value NOT IN (list) becomes unknown for every row, because comparing with NULL is unknown, so the query returns no rows. For example, NOT IN (SELECT manager_id FROM employees) fails when a manager_id is NULL. Use NOT EXISTS or filter out NULLs in the subquery to get correct results.
06
What does DISTINCT do and how is it different from GROUP BY?
DISTINCT removes duplicate rows from the result based on all selected columns. GROUP BY groups rows so you can apply aggregates per group. SELECT DISTINCT city FROM customers and SELECT city FROM customers GROUP BY city give the same output, but GROUP BY is needed when you also want counts or sums. Overusing DISTINCT to hide duplicate join rows is a common mistake.
07
A query uses SELECT * on a table with 80 columns. Why is that a problem?
SELECT * fetches every column, increasing disk reads, memory, network transfer and application processing. It prevents covering indexes from being used and can break applications when columns are added or reordered. Select only required columns, which lets the optimiser use narrower indexes and makes the intent clear. SELECT * is acceptable only for quick exploration, not production code.
WITH spend AS (
SELECT customer_id, SUM(amount) AS total
FROM orders
GROUP BY customer_id
)
SELECT customer_id, total,
NTILE(4) OVER (ORDER BY total DESC) AS quartile
FROM spend;
Quartile 1 contains the highest spenders. Use this for targeted offers or loyalty tiers.
09
Match each employee's salary to a grade using a range table.
SELECT e.name, e.salary, g.grade
FROM employees e
JOIN salary_grades g
ON e.salary BETWEEN g.min_salary AND g.max_salary;
This is a non-equi join. Ensure grade ranges do not overlap, otherwise an employee matches more than one grade and appears twice.
10
What is the difference between DELETE, TRUNCATE and DROP?
DELETE removes selected rows using a WHERE clause, is logged row by row, fires triggers and can be rolled back inside a transaction. TRUNCATE removes all rows quickly by deallocating pages, usually resets identity values and cannot use WHERE. DROP removes the whole table including structure, indexes and permissions. Use DELETE for selective removal, TRUNCATE to empty staging tables and DROP to remove objects.
11
Find all customers whose email is missing or empty.
SELECT customer_id, name
FROM customers
WHERE email IS NULL
OR TRIM(email) = '';
NULL needs IS NULL, because email = NULL never matches. Empty strings and spaces are separate values, so TRIM handles both. Consider fixing data at entry with NOT NULL and CHECK constraints.
LIKE matches string patterns. The percent sign matches any sequence of characters and the underscore matches exactly one character. name LIKE 'Ra%' finds names starting with Ra, and code LIKE 'A_1' matches A01 or AB1. Leading wildcards such as LIKE '%kumar' prevent index use and are slow on large tables. Case sensitivity depends on the database collation.
13
Implement keyset pagination instead of OFFSET for large tables.
Slow for deep pages:
SELECT * FROM orders ORDER BY id LIMIT 50 OFFSET 500000;
Keyset pagination:
SELECT * FROM orders
WHERE id > :last_seen_id
ORDER BY id
LIMIT 50;
OFFSET reads and discards all skipped rows, while keyset seeks directly using the index on id, keeping each page fast.
14
A business user needs a dashboard on a complex 6-table join that runs slowly. Would you use a view or a materialised view?
A normal view only simplifies the query; it still runs the full join every time, so performance does not improve. If the data can be a few minutes or hours old, a materialised view or summary table refreshed on schedule gives fast reads. If real-time data is required, tune the underlying query with indexes or build an incrementally maintained table.
15
What are database statistics and why do they matter?
Statistics describe data distribution in columns, such as row counts, distinct values and histograms. The optimiser uses them to estimate how many rows each step returns and pick join types and indexes. Stale statistics after large data loads lead to bad plans, such as nested loops over millions of rows. Update them with ANALYZE in PostgreSQL or UPDATE STATISTICS in SQL Server.
16
What is the difference between a stored procedure and a function?
A function must return a value or table and can be used inside SELECT or WHERE, but in most databases cannot change data or manage transactions. A stored procedure is executed independently, can return multiple result sets or output parameters, can modify data and control transactions. Use functions for reusable calculations and procedures for workflows such as month-end processing.
17
Write a query to show each employee with their manager's name.
SELECT e.emp_id,
e.name AS employee,
m.name AS manager
FROM employees e
LEFT JOIN employees m
ON e.manager_id = m.emp_id;
The same table is used twice with different aliases. LEFT JOIN ensures the CEO, who has no manager, still appears with a NULL manager name.
18
A query that ran in 1 second now takes 40 seconds after a data load. What do you check?
Compare the current execution plan with the old one. A big data load often makes statistics stale, causing poor row estimates and a different join or scan choice, so update statistics first. Check for index fragmentation, missing indexes after the load, blocking from other sessions, and whether the data volume crossed a threshold. Test again after each change.
19
Describe a time you reviewed someone else's SQL and suggested improvements.
Situation: a teammate's report query took eight minutes and had nested subqueries. Task: help without discouraging him. Action: I reviewed it with him, rewrote parts as CTEs, replaced a correlated subquery with a pre-aggregated join, and explained the execution plan differences. Result: the query ran in 40 seconds, and he later applied the same approach to two other reports.
Isolation levels balance consistency and concurrency. Read Uncommitted allows dirty reads. Read Committed, the default in PostgreSQL and SQL Server, prevents dirty reads but allows non-repeatable reads. Repeatable Read, the default in MySQL InnoDB, prevents non-repeatable reads, and InnoDB also prevents most phantoms with gap locks. Serializable prevents all anomalies but reduces concurrency. Choose based on business correctness needs.
21
What is the difference between clustered and non-clustered indexes?
A clustered index defines the physical order of rows in the table, so there can be only one, and in SQL Server the primary key creates it by default. A non-clustered index is a separate structure with key values and pointers to rows, and a table can have many. Range queries on the clustered key are very fast, while non-clustered indexes suit selective lookups.
22
Calculate a 7-day moving average of daily active users.
SELECT activity_date, dau,
ROUND(AVG(dau) OVER (
ORDER BY activity_date
ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
), 1) AS dau_7d_avg
FROM daily_users
ORDER BY activity_date;
This assumes one row per day; fill missing dates with a calendar table first, or ROWS will span more than seven days.
23
Two users book the last seat at the same time and both succeed. How do you fix it?
This is a race condition. Wrap the check and booking in one transaction and lock the seat row with SELECT ... FOR UPDATE so the second transaction waits and then sees it is taken. Alternatively use an atomic update such as UPDATE seats SET booked_by = :user WHERE seat_id = :id AND booked_by IS NULL and check affected rows, plus a unique constraint as a final safety net.
24
What is the difference between a CTE, a subquery, a temp table and a view?
A subquery and a CTE exist only for one statement; a CTE is more readable and can be recursive. A temporary table stores data physically for the session, can be indexed and reused across statements, useful for large intermediate results. A view is a saved query stored in the database that anyone with access can reuse. Choose by reuse, size and performance needs.
LAG returns a value from a previous row in the partition and LEAD from a following row, based on the ORDER BY. LAG(revenue, 1, 0) OVER (ORDER BY month) gives last month's revenue with 0 as default for the first row. They are used for month-over-month change, time between events, and detecting status changes without self joins.
Use string aggregation: STRING_AGG(name, ', ') in PostgreSQL and SQL Server, GROUP_CONCAT(name SEPARATOR ', ') in MySQL, and LISTAGG in some other databases. Most support ordering inside, for example STRING_AGG(name, ', ' ORDER BY name). It is used to list products in an order or skills of an employee in one row, but watch length limits.
A covering index contains all columns a query needs, either as key columns or included columns, so the database answers the query from the index alone without visiting the table. For SELECT order_date, amount FROM orders WHERE customer_id = 5, an index on (customer_id) INCLUDE (order_date, amount) avoids lookups. It speeds reads but increases index size and write cost.
28
How do you get the top 3 rows in different databases?
MySQL and PostgreSQL:
SELECT * FROM products ORDER BY price DESC LIMIT 3;
SQL Server:
SELECT TOP 3 * FROM products ORDER BY price DESC;
Standard SQL:
SELECT * FROM products ORDER BY price DESC
FETCH FIRST 3 ROWS ONLY;
Always use ORDER BY, otherwise the top rows are not guaranteed.
29
Add a foreign key and a check constraint to an existing table.
ALTER TABLE orders
ADD CONSTRAINT fk_orders_customer
FOREIGN KEY (customer_id)
REFERENCES customers(customer_id);
ALTER TABLE orders
ADD CONSTRAINT chk_amount_positive
CHECK (amount > 0);
First clean existing rows that violate the rule, otherwise the ALTER fails. Naming constraints makes errors easier to read.
30
You added an index but the query still does a full table scan. Why might that be?
Possible reasons: the predicate is not SARGable because of a function or type conversion, the query returns a large share of rows so a scan is cheaper, the composite index column order does not match the filter, statistics are stale, or OR conditions prevent index use. Check the plan, rewrite the predicate, update statistics, or adjust the index to match the query.
31
What is the CASE expression and where can it be used?
CASE adds conditional logic in SQL, returning a value based on conditions: CASE WHEN marks >= 60 THEN 'First' WHEN marks >= 45 THEN 'Second' ELSE 'Fail' END. It can be used in SELECT, WHERE, ORDER BY, GROUP BY and inside aggregates, for example SUM(CASE WHEN status = 'Paid' THEN amount ELSE 0 END) to pivot data. Conditions are checked in order and the first true one wins.
A Common Table Expression is a named temporary result defined with WITH before the main query. It improves readability by breaking a complex query into named steps, can be referenced multiple times in the same statement, and supports recursion. Example: WITH high_value AS (SELECT * FROM orders WHERE amount > 10000) SELECT customer_id, COUNT(*) FROM high_value GROUP BY customer_id.
WHERE filters individual rows before grouping and cannot use aggregate functions. HAVING filters groups after GROUP BY and can use aggregates such as COUNT or SUM. For example, WHERE city = 'Pune' keeps Pune rows, and HAVING COUNT(*) > 5 keeps only groups with more than five rows. Filtering early with WHERE is also more efficient.
34
What is the difference between an index scan and an index seek?
An index seek navigates the B-tree directly to the matching keys, reading only the needed rows, which is efficient for selective filters. An index scan reads the whole index or a large range of it. A table scan reads every row. Scans are not always bad; for queries returning a large share of rows, a scan can be cheaper than many seeks.
35
Describe a time you found and fixed a wrong number in a report.
Situation: a monthly dashboard showed 30 percent higher revenue than finance figures. Task: find the cause before the leadership review. Action: I traced the SQL, found a join to the shipments table that duplicated orders with split shipments, rewrote it with a pre-aggregated CTE and reconciled totals with finance. Result: numbers matched exactly and I added a row-count test to the report pipeline.
36
Tell me about a database design decision you made and its outcome.
Situation: our app stored user preferences in a wide table with 60 nullable columns. Task: make it flexible for new settings. Action: I proposed a key-value preferences table with a JSON column for rarely used settings, kept frequently queried fields as columns, and wrote a migration. Result: new settings no longer needed schema changes and query speed for common fields stayed the same.
Few big employers listed in Bhubaneswar?
Many Bhubaneswar candidates also apply to teams in Kolkata and to remote or hybrid roles. Practise the Kolkata set as well.
Which companies hire SQL professionals in Bhubaneswar?
Companies with large offices in Bhubaneswar include many IT services and product firms. Use their company pages below to practise employer-specific sets.
Are SQL interviews in Bhubaneswar different from other cities?
The questions depend more on the company than the city. Bhubaneswar's mix of employers decides the style: Freshers make up a big share of hiring, so fundamentals and aptitude rounds matter most
How do I find SQL jobs in Bhubaneswar?
Shortlist companies with Bhubaneswar offices, keep your CV focused on SQL projects, apply on company career pages and job portals with the Bhubaneswar filter, and practise the questions on this page before each interview.