20 Most Common SQL Query Interview Questions & Answers [For Freshers & Experienced]

Did you know that SQL holds 9th position in the programming language list worldwide in 2025? Learning SQL Query interview questions ensures you can optimize database interactions across modern tech stacks, including integration with Python, Java, and cloud platforms.

Proficiency in writing optimized SQL queries using advanced techniques like window functions, joins, and indexing is key to learning SQL Query interview questions. These questions assess your ability to manipulate and analyze large datasets within normalized relational databases efficiently. 

You must demonstrate how SQL integrates with application layers, including APIs and front-end technologies, to deliver scalable solutions. A deep understanding of SQL fundamentals and performance tuning is essential for success in technical interviews.

In this blog, we will explore 20 most commonly asked SQL query interview questions those are essential for enterprise applications. 

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SQL Query Interview Questions and Answers

Preparing SQL Query interview questions and answers sharpens your skills in complex data manipulation, crucial for machine learning pipelines. These queries test your command over relational database concepts and optimization. Mastery enables efficient data retrieval and processing, foundational for data-driven applications and AI model training.

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1. Write a query to get the EmpFname from the EmployeeInfo table in the upper case using the alias name as EmpName.

How to answer:

Provide Overview:
Converting text to uppercase in SQL is a common operation for normalizing data presentation in user interfaces and reports. Using the UPPER() function allows consistent formatting across applications, ensuring uniformity regardless of source data inconsistencies. Aliasing the result with AS improves readability and facilitates integration with frontend frameworks such as React.js.

Provide technical elements:

• Uses SQL’s UPPER() scalar function to convert all characters in EmpFname to uppercase following database collation rules.
• Applies aliasing (AS EmpName) for semantic clarity and easier data mapping in React.js or Bootstrap-based frontends.
• Server-side transformation minimizes client processing and reduces data transmission overhead.
• Supports localization when combined with Unicode collations for international applications.

Provide Code Example:

Input Code:

SELECT UPPER(EmpFname) AS EmpName FROM EmployeeInfo;

Output Code:

EmpName
--------
ROHAN
SUSHMITA
MANOJ

Output Explanation:

The query converts employee first names to uppercase and labels the output column as EmpName. This improves UI consistency without altering stored data.

Use Case:
When you build a React.js employee directory, fetching uppercase names directly from MySQL simplifies frontend logic. This reduces client-side transformations, improving performance and consistent UI display using Bootstrap components.

2. Write a query to get the number of employees working in the department ‘HR’.

How to answer:

Provide Overview:

Counting filtered records is fundamental for analytics and reporting in SQL. Using COUNT(*) with a WHERE clause efficiently aggregates data, aiding real-time dashboards and monitoring. This enables integration with Next.js or React.js frontends to display live workforce metrics.

Discuss key elements:

• Leverages aggregate COUNT(*) function with conditional filtering via WHERE Department = 'HR'.
• Optimizes queries through indexes on the department column for fast retrieval.
• Supports scalable data aggregation for enterprise React.js dashboards.
• Backend aggregation reduces frontend data processing and API payloads.

Provide a code example:

Input Code:

SELECT COUNT(*) FROM EmployeeInfo WHERE Department = 'HR';

Output:

5

Output Explanation:
Returns the total number of employees in the HR department, enabling dynamic reporting in admin dashboards.

Use Case:
You can display HR team size dynamically in a React.js admin panel, using Bootstrap cards for visual metrics. This query simplifies backend data logic and supports responsive UI updates.

3. What query will you write to fetch the current date?

How to answer:

Provide Overview:

Fetching the current server timestamp ensures consistency across distributed applications, which is essential for logging and time-based features. SQL engines offer functions like GETDATE() (SQL Server) and SYSDATE() (MySQL) to retrieve the exact current datetime. Accurate server-side timestamps synchronize backend logic with React.js frontend displays.

Discuss key elements: 

• Retrieves system date and time with precision from the database server environment.
• Enables consistent time references across microservices, APIs, and user interfaces.
• Supports timezone-aware operations and audit logging in full-stack applications.
• Simplifies date-dependent features like scheduling and session management.

Provide code example:

Input Code:

SELECT SYSDATE();

Output:

2025-05-15 10:30:00

Output Explanation:
Returns the current date and time from the database server, crucial for synchronized operations between the back and frontend.

Use Case:
You might build a React.js booking system where server-side validation prevents client-side manipulation. Accurate timestamps maintain transactional integrity across your full-stack app.

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4. Write a query to fetch only the place name(string before brackets) from the Address column of the EmployeeInfo table.

How to answer:

Provide Overview:

Extracting substrings using MID() or SUBSTRING() with position functions allows for precise text parsing in SQL. This is useful when cleaning or formatting semi-structured data before passing it to React or Next.js frontends. Proper data formatting enhances UI clarity and reduces frontend processing overhead.

Address technical aspects:

• Uses string functions to parse and clean address data in SQL.
• Prepares data for consistent rendering in UI components.
• Reduces frontend complexity by handling data formatting server-side.
• Supports scalable data preprocessing for large datasets.

Input Code:

SELECT MID(Address, 0, LOCATE('(', Address)) AS PlaceName FROM EmployeeInfo;

Output:

PlaceName
------------
123 Rajeev Chowk
456 MG Road

Output Explanation:
The query extracts the substring before the first opening bracket ‘(’, providing clean place names without extraneous details. This preprocessing helps deliver cleaner data to the frontend React components.

Use Case:
If you are building a location-based directory in React.js, this query supplies well-formatted addresses for map tooltips or listings. Offloading this string manipulation to MySQL reduces frontend processing time, enhancing app responsiveness. It also ensures consistency across various UI elements using Bootstrap.

5. Write a query to create a new table whose data and structure are copied from another table.

How to answer:

Provide Overview:

Duplicating tables using SELECT INTO or CREATE TABLE AS SELECT allows you to clone the schema and optionally the data, critical for testing or backup operations. This SQL functionality supports versioning and staging workflows in backend databases powering full-stack apps. It is fundamental for maintaining data integrity in environments using React.js frontends and Java or Node.js APIs.

Discuss key elements:

• Efficiently copies table schema and data for development/testing.
• Supports zero-downtime migrations by preparing staging tables.
• Enables environment replication without affecting production data.
• Simplifies data management for complex application stacks.

Provide Code Example:

Input Code:

CREATE TABLE NewTable AS SELECT * FROM EmployeeInfo;

Output:

NewTable created with structure and all data copied from EmployeeInfo.

Output Explanation:
This command creates a new table, NewTable, with the same structure and data as EmployeeInfo. It facilitates data duplication for backup or development without manual schema recreation.

Use Case:
When developing new features in React.js that require database changes, you can use this query to create a test environment table. This allows you to run experiments or migrations safely without impacting live data, supporting continuous deployment workflows on platforms like AWS or Azure.

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6. Write a query to display the names of employees that begin with ‘S’.

How to answer:

Provide Overview:

Using SQL pattern matching with LIKE enables precise filtering of string data in databases powering full-stack applications. This operation complements backend services written in Python or C#, which feed filtered data into React.js or Next.js frontends. Efficient string querying reduces API load and optimizes Dockerized microservices handling database requests.

Provide technical aspects:

• Employs LIKE 'S%' for prefix-based filtering, optimized with proper indexing.
• Supports integration with backend languages like Python (Flask, Django) and C# (.NET) for server-side data handling.
• Reduces frontend computational overhead by returning pre-filtered datasets to React.js clients.
• Suitable for Kubernetes-deployed microservices where database query efficiency affects container scaling.

Provide Code Example:

Input Code:

SELECT * FROM EmployeeInfo WHERE EmpFname LIKE 'S%';

Output:

EmpID | EmpFname | EmpLname | Department | Address
---------------------------------------------------
102   | Suleman   | Khan     | Sales      |  Bandra (Mumbai)
115   | Sayan  | Ghosh   | Marketing  | Shyambazar (Kolkata)

Output Explanation:
The query retrieves employees whose first names start with ‘S’, supporting efficient data delivery in distributed container environments. React.js or Next.js UIs consume this filtered data to render targeted views.

Use Case:
When your app backend runs in a Kubernetes cluster managing multiple Python and C# services, this query allows efficient filtering before React.js frontend rendering, minimizing latency and resource use.

7. Write a query to retrieve the top N records.

How to answer:

Provide Overview:

Extracting the top N records by a metric is essential in analytics and reporting modules, often interfacing with ML models built in PyTorch or TensorFlow. SQL’s TOP or LIMIT clauses streamline data retrieval, which is critical when backend microservices run in Docker containers supporting scalable React.js dashboards.

Address technical elements:

• Uses TOP (SQL Server) or LIMIT (MySQL) for efficient result set truncation.
• Supports ordered retrieval aligned with ML-driven ranking in Python-based APIs.
• Enables responsive React.js UI components by reducing payload sizes.
• Enhances autoscaling in Kubernetes environments by optimizing query resource consumption.

Provide Code Example:

Input Code (MySQL):

SELECT * FROM EmployeePosition ORDER BY Salary DESC LIMIT 3;

Output:

EmpID | EmpPosition | Salary
-----------------------------
110   | Manager     | 120000
105   | Lead Dev    | 115000
102   | Senior Dev  | 110000

Output Explanation:
Returns top 3 highest-paid employees, facilitating leaderboard displays powered by React.js components. Backend Python or C# services can apply additional ML logic on this subset efficiently.

Use Case:
In a Dockerized backend serving React.js frontends, you can run this query to dynamically populate salary leaderboards, while Kubernetes manages scaling for fluctuating user demand.

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8. Write a query to obtain relevant records from the EmployeeInfo table ordered by Department in ascending order and EmpLname in descending order.

How to answer:

Provide Overview:

Multi-column sorting is vital for structured data presentation in enterprise web apps, combining React.js and backend APIs in C++ or Python. SQL’s ORDER BY clause supports complex ordering needed for UI tables. Efficient sorting impacts resource usage in containerized environments running on Kubernetes.

Discuss technical aspects:

• Implements multi-level ordering using ORDER BY Department ASC, EmpLname DESC.
• Supports backend services in Python or C# for dynamic query generation.
• Optimized indexing improves response times in Docker-based deployments.
• Enhances React.js tables with sorted data, improving user experience.

Provide a code example:

Input Code:

SELECT * FROM EmployeeInfo ORDER BY Department ASC, EmpLname DESC;

Output:

EmpID | EmpFname | EmpLname | Department | Address
-------------------------------------------------
101   | Rakesh   | Bhat  | HR         |H/10 Jain Nagar
110   | Sulekha    | Mishra  | HR         |25 MG Road
105   | Sharan     | Chawla    | Sales      | 1B Uttam Vihar
107   | Vikas     | Singh  | Sales      | 17/8 CR Avenue

Output Explanation:
Data is grouped by department ascending, then by last name descending. This structured output feeds React.js frontend tables efficiently.

Use Case:
When running backend APIs in Docker containers, serving sorted data enhances frontend filtering in React.js dashboards, and Kubernetes can scale containers based on query loads.

9. Write a query to get the details of employees whose EmpFname ends with ‘A’.

How to answer:

Provide Overview:

Suffix-based pattern matching in SQL supports flexible text filtering in apps integrating machine learning models in Python or C++ for user personalization. Backend systems with C# APIs can leverage such queries for optimized data retrieval. This minimizes frontend React.js component processing.

Discuss technical aspects:

• Uses LIKE '%a' for suffix matching, optimized with pattern indexes.
• Enables ML pipelines (e.g., PyTorch) to target user segments based on name patterns.
• Facilitates filtered data delivery to React.js UI components.
• Supports scaling in Kubernetes-managed services by reducing data volume.

Provide a code example:

Input Code:

SELECT * FROM EmployeeInfo WHERE EmpFname LIKE '%a';

Output:

EmpID | EmpFname | EmpLname | Department | Address
-------------------------------------------------
103   | Kalpana    | Patel    | Finance    | 22 Hill St
118   | Sheya  | Sharma   | Marketing  | 34 Lake Rd

Output Explanation:
Employees with first names ending in ‘a’ are selected, enabling targeted UI displays or marketing in React.js apps.

Use Case:
You can use this query in C# APIs within Docker containers to segment users for personalized content, served dynamically to React.js frontends, with Kubernetes orchestrating the backend.

10. Create a query to fetch details of employees having “DELHI” as their address.

How to answer:

Provide Overview:

Partial string matching with LIKE supports location-based queries, essential in geographic data analysis for React.js map visualizations. Backend ML models in Python or C++ can combine these queries with geospatial analytics. Efficient filtering enhances microservices' performance on Docker and Kubernetes platforms.

Discuss key elements:

• Uses LIKE 'DELHI%' for prefix-based address filtering.
• Integrates with ML pipelines in Python for location-based insights.
• Enables responsive React.js map components with clean data.
• Optimizes query performance for containerized backend services.

Provide code example:

Input Code:

SELECT * FROM EmployeeInfo WHERE Address LIKE 'DELHI%';

Output:

EmpID | EmpFname | EmpLname | Department | Address
-------------------------------------------------
120   | Rajesh   | Kumar    | HR         | DELHI Sector 5
123   | Anjali   | Singh    | Sales      | DELHI Connaught Place

Output Explanation:
Selects employees whose address starts with ‘DELHI’, supporting location-specific features in web apps.

Use Case:
You can build React.js dashboards that show employee locations using this query, while backend ML models in Python analyze regional patterns, all running smoothly in Kubernetes-managed Docker clusters.

Read: Top 9 Data Science Tools in 2020

11. Write a query to fetch all employees who also hold the managerial position.

How to answer:

Provide Overview:

Joining tables to combine related data is foundational in normalized databases serving React.js or Next.js applications. Backend logic in C#, Python, or C++ uses such joins for role-based data access. Efficient joins are critical in microservices architectures running within Docker and Kubernetes.

Address core aspects:

• Uses INNER JOIN to merge employee and position tables on EmpID.
• Filters with WHERE clause for specific roles like 'Manager'.
• Supports role-based data delivery in RESTful APIs or GraphQL endpoints.
• Enhances query optimization through indexed joins in containerized DBs.

Provide code examples:

Input Code:

SELECT E.EmpFname, E.EmpLname, P.EmpPosition
FROM EmployeeInfo E
INNER JOIN EmployeePosition P ON E.EmpID = P.EmpID
WHERE P.EmpPosition = 'Manager';

Output:

EmpFname | EmpLname | EmpPosition
-------------------------------
Manoj    | Sharma     | Manager
Asif   | Khan     | Manager

Output Explanation:
Returns all employees holding the 'Manager' position, providing combined info from two normalized tables. This is essential for permission management and hierarchical views in React.js dashboards.

Use Case:
In a Kubernetes-orchestrated backend, you might run this query via Python or C# services to populate React.js UI components showing managers, improving access control and UI clarity.

12. Create a query to generate the first and last records from the EmployeeInfo table.

How to answer:

Provide Overview:

Fetching boundary records using MIN() and MAX() functions allows precise data retrieval based on primary keys or timestamps. This technique is vital for time-series data analysis, auditing, and pagination in applications built with React.js or Next.js consuming SQL backends. Understanding subqueries enhances your ability to write efficient, nested SQL queries compatible with containerized databases running on Docker or Kubernetes.

Address technical characteristics:

• Uses scalar subqueries (SELECT MIN(EmpID)) and (SELECT MAX(EmpID)) to identify boundary records.
• Essential for pagination, data versioning, and audit trail functionalities in backend systems.
• Supports integration with Java, Python, or C# backend APIs serving React.js UI components.
• Optimizes database querying by leveraging indexed primary key lookups.

Provide code examples:

Input Code (Fetch First Record):

SELECT * FROM EmployeeInfo WHERE EmpID = (SELECT MIN(EmpID) FROM EmployeeInfo);

Input Code (Fetch Last Record):

SELECT * FROM EmployeeInfo WHERE EmpID = (SELECT MAX(EmpID) FROM EmployeeInfo);

Output:
First Record

EmpID | EmpFname | EmpLname | Department | Address
-------------------------------------------------
101   | Akash    | Singh   | HR         | 17/3 SS Road

Last Record

EmpID | EmpFname | EmpLname | Department | Address
-------------------------------------------------
150   | Priya    | Sharma   | Sales      | 78 Netaji Rd

Output Explanation:
The queries return the employee with the smallest and largest EmpID values, representing first and last records. This supports efficient data retrieval for UI pagination and data validation in backend services.

Use Case:
When implementing paginated tables in React.js, you can use these queries to determine the range of records available. This helps your Node.js or Python backend deliver precise page boundaries, improving performance in Kubernetes-managed APIs.

13. Create a query to check if the passed value to the query follows the EmployeeInfo and EmployeePosition tables’ date format.

How to answer:

Provide Overview:

Validating date formats using SQL’s ISDATE() function ensures data integrity before insertion or processing. This function returns a boolean-like response to confirm if a string matches the expected date format, critical for backend validation in Java or Python services. Proper validation reduces runtime errors and supports robust form handling in React.js interfaces.

Address technical aspects:

• Uses ISDATE() to verify if the input string conforms to a valid date format.
• Prevents invalid data entries in databases, preserving schema integrity.
• Supports frontend form validation in React.js by providing backend validation fallbacks.
• Compatible with Dockerized SQL Server instances in scalable Kubernetes environments.

Provide a code example:

Input Code:

SELECT ISDATE('01/04/2020') AS "MM/DD/YY";

Output:

MM/DD/YY
---------
0

Output Explanation:
Returns 0, indicating the string '01/04/2020' does not match the expected MM/DD/YY format. This helps identify and reject incorrectly formatted date inputs.

Use Case:
When your React.js frontend submits date inputs, your C# or Python backend can use this query to validate the format before processing. This prevents data corruption and ensures consistency in your Docker/Kubernetes-hosted database.

Also read: Top 27 SQL Projects in 2025 With Source Code: For All Levels

14. Create a query to obtain display employees having salaries equal to or greater than 150000.

How to answer:

Provide Overview:

Filtering employees by salary thresholds is vital for payroll analysis and budgeting in Indian enterprises. SQL queries with conditional filters enable backend systems to provide real-time compensation reports for React.js dashboards. Efficient queries also help C# or Python microservices inside Docker containers deliver accurate datasets.

Provide key insights:

• Applies WHERE Salary >= 150000 for numeric filtering on indexed salary columns.
• Supports backend APIs in Python, Java, or C# that aggregate payroll data.
• Enables React.js frontends to render high-earning employee lists dynamically.
• Optimizes query performance for Kubernetes-managed SQL databases.

Provide code example:

Input Code:

SELECT EmpName FROM Employees WHERE Salary >= 150000;

Output:

EmpName
---------
Rajesh Kumar
Neha Sharma

Output Explanation:
Lists employees like Rajesh Kumar and Neha Sharma with salaries ≥ ₹150,000. This data aids HR analytics in Indian corporate environments.

Use Case:
Use this query to populate salary filters in React.js apps for Indian companies, with backend services in Python or C# handling data processing inside Docker containers.

15. Write a query to fetch the year using a date.

How to answer:

Provide Overview:

Extracting the year from date fields supports the time-based reports standard in Indian financial and operational systems. SQL's YEAR() function complements backend analytics in Java or Python services. React.js frontends leverage this for generating dynamic annual summaries.

Provide technical insights:

• Uses the YEAR() function on the system or column dates to extract the year.
• Enables temporal filtering and date-based UI controls in React.js.
• Facilitates accurate time-series analysis in backend microservices.
• Integrates with Dockerized SQL servers in Kubernetes clusters.

Provide code example:

Input Code:

SELECT YEAR(GETDATE()) AS "Year";

Output:

Year
-----
2025

Output Explanation:
Returns the current year from the database server, useful for dynamic date filtering in Indian accounting apps.

Use Case:
Backend Python or C# APIs use this to deliver year-specific data to React.js dashboards showing financial reports or audit trails.

16. Create an SQL query to fetch EmpPostion and the total salary paid for each employee position.

How to answer:

Provide Overview:

Summarizing salary expenditures by position is critical for budget planning in Indian businesses. SQL aggregation with SUM() and GROUP BY supports backend data summarization, powering React.js visualizations. This approach is efficient in containerized environments managed by Kubernetes.

Discuss key elements:

• Aggregate salary sums per employee position for financial insights.
• Enables Java or Python backend APIs to serve summarized payroll data.
• Supports real-time visualizations with React.js or Next.js frontends.
• Optimizes database workload in Dockerized microservice architectures.

Provide a code example:

Input Code:

SELECT EmpPosition, SUM(Salary) FROM EmployeePosition GROUP BY EmpPosition;

Output Explanation:
Summarizes total salaries for roles like Manager and Developer, facilitating financial overview in Indian corporate contexts.

Use Case: 

You can build React.js payroll dashboards showing salary distributions with this query, supported by backend Python services in scalable Kubernetes clusters.

17. Write a query to find duplicate records from a table.

How to answer:

Provide Overview:

Identifying duplicates via GROUP BY and HAVING clauses is essential for maintaining data integrity in Indian HR systems. Backend services in C# or Python use such queries to trigger cleansing operations. Clean datasets improve React.js UI accuracy and user trust.

Address key factors:

• Group rows by employee fields to detect duplicates.
• Filters groups with counts greater than 1 ,indicating duplicate entries.
• Supports backend automated data cleaning and validation.
• Enhances frontend reliability in React.js or Next.js apps.

Provide a code example:

Input Code:

SELECT EmpID, EmpFname, Department, COUNT(*)
FROM EmployeeInfo
GROUP BY EmpID, EmpFname, Department
HAVING COUNT(*) > 1;

Output:

EmpID | EmpFname | Department | COUNT(*)
----------------------------------------
108   | Amit     | Sales      | 2
112   | Priya    | HR         | 3

Output Explanation:
Detects employees like Amit and Priya with multiple records, highlighting data issues in large Indian enterprise databases.

Use Case:
Use this in backend Python or C# APIs to clean data before sending unique employee lists to React.js components, improving UX and trust.

18. Create a query to fetch the third-highest salary from the EmpPosition table.

How to answer:

Provide Overview:

Fetching the nth highest value using nested queries is key in salary analytics for Indian firms. This approach is compatible with backend services in Python, C++, or Java powering React.js dashboards. Efficient ranking queries improve performance in Docker and Kubernetes environments.

Address technical elements:

• Uses nested TOP and ORDER BY clauses for nth record extraction.
• Supports detailed compensation analysis and anomaly detection.
• Powers rank-based UI elements in React.js or Next.js applications.
• Optimizes query execution for containerized databases.

Provide code example:

Input Code (SQL Server):

SELECT TOP 1 Salary
FROM (
  SELECT TOP 3 Salary
  FROM EmployeePosition
  ORDER BY Salary DESC
) AS Temp
ORDER BY Salary ASC;

Output:

Salary
-------
95000

Output Explanation:
Extracts the third-highest salary, supporting nuanced payroll insights relevant to Indian company HR policies.

Use Case:
Backend C# or Python APIs can use this for salary band visualization in React.js, with Kubernetes managing app scalability.

19. Write an SQL query to find even and odd records in the EmployeeInfo table.

How to answer:

Provide Overview:

Classifying records using modulo operations assists in UI enhancements like alternating row styles. This technique is used in React.js data tables with backend support from Python or Java services. Efficient queries optimize data flow in Kubernetes-orchestrated microservices.

Address technical aspects:

• Uses MOD() with row numbers to segregate even and odd records.
• Facilitates conditional frontend styling for better data readability.
  Supports backend-driven data segmentation in containerized applications.
• Enables clean pagination and row striping in Bootstrap-styled UIs.

Provide a code example:

Input Code:

SELECT EmpID FROM (
  SELECT ROW_NUMBER() OVER (ORDER BY EmpID) AS rowno, EmpID FROM EmployeeInfo
) AS T
WHERE MOD(rowno, 2) = 0;

Output:

EmpID
-----
102
104
106

Output Explanation:
Fetches employee IDs in even row positions, aiding UI row striping for more straightforward data presentation.

Use Case:
In React.js apps, use this query to deliver data with alternating styles, enhancing UX while backend Python services run in Docker containers.

20. Create a query to fetch the list of employees of the same department.

How to answer:

Provide Overview:

Self-joins enable fetching records of the same category, which is essential for organizational analysis in Indian companies. Backend APIs in Java or Python utilize such queries to serve team data to React.js UIs. Proper filtering enhances performance in Kubernetes-managed environments.

Discuss technical elements:

• Performs a self-join on the EmployeeInfo table, matching employees by department.
• Excludes self-reference to list distinct colleagues.
• Supports React.js UI components, showing team members dynamically.
• Scales well in containerized, orchestrated backend systems.

Provide a Code example:

Input Code:

SELECT DISTINCT E.EmpID, E.EmpFname, E.Department
FROM EmployeeInfo E
JOIN EmployeeInfo E1 ON E.Department = E1.Department AND E.EmpID != E1.EmpID;

Output:

EmpID | EmpFname | Department
-----------------------------
105   | Sunita   | Marketing
109   | Rakesh   | Marketing
111   | Anil     | Finance
115   | Kavita   | Finance

Output Explanation:
Returns employees sharing departments like Marketing and Finance, which are functional for team views in Indian organizational React.js apps.

Use Case:
Backend C# or Python microservices can use this to feed team member lists into React.js org charts, which are managed at scale with Kubernetes.

How to explain SQL query in an interview?

Explaining an SQL query in an interview requires clarity, technical precision, and a logical breakdown of the query components. You should demonstrate your understanding of the database schema, SQL syntax, and business context while highlighting how the query solves a problem efficiently. Emphasizing performance considerations and anticipating follow-up questions reflects advanced database knowledge crucial in real-world applications involving complex data systems.

• Context Introduction: Briefly describe the business scenario and the database or tables involved in setting the stage.
• Objective Clarification: Clearly state what the query aims to accomplish—data retrieval, update, aggregation, or filtering.
• Query Breakdown: Explain key SQL clauses—SELECT, FROM, JOIN, WHERE, GROUP BY, ORDER BY—and their roles in shaping results.
• Functions and Optimizations: Highlight SQL functions used (e.g., CASE, COALESCE, date functions) and mention indexing or optimization techniques.
• Output Summary and Impact: Summarize the query output and how it addresses the business problem, mentioning scalability or potential improvements.

Example Scenario:
Imagine you’re asked to explain a query fetching the total sales per region from a retail database. You’d start by describing the retail schema and sales reporting needs. Then, clarify that the query aggregates sales data grouped by region using GROUP BY. 

Next, you break down how the JOIN connects sales and region tables, and the WHERE clause filters data for the current fiscal year. Finally, you explain any indexing on the region column improving query speed and summarize how the output helps management track regional performance effectively.

Also Read: SQL Jobs for Freshers: Salary, Career Growth & Opportunities

Tips to Succeed in SQL Query Interviews

Excelling in SQL query interviews requires strong foundational knowledge, practical skills, and effective communication. You must demonstrate proficiency in writing optimized SQL statements, understanding relational database concepts, and integrating query results within broader technology stacks involving HTTP APIs or web frontends built with HTML. Additionally, being aware of query execution plans and database indexing strategies reflects real-world readiness in handling large-scale data environments.

• Master Core SQL Syntax: Be fluent with SELECT, JOINs (INNER, LEFT, RIGHT), WHERE, GROUP BY, HAVING, and ORDER BY clauses, essential for building complex queries.
• Understand Data Relationships: Know how to model and query normalized relational databases; explain foreign keys and use of indexes to optimize performance.
• Write Efficient Queries: Use techniques like subqueries, CTEs, window functions, and avoid expensive operations to minimize HTTP response times when serving data to frontends.
• Explain Query Logic Clearly: When discussing queries, articulate how SQL integrates with application layers, such as RESTful APIs over HTTP or dynamic HTML tables in web apps.
• Practice Real-World Scenarios: Familiarize yourself with common business use cases like data aggregation for dashboards, filtering for report generation, and maintaining data integrity.

Example Scenario:
Suppose you’re asked to write a query to fetch the monthly sales summary for a web-based retail application. You explain how your SQL query aggregates sales data grouped by month, optimized by indexes on date columns for faster retrieval. You then link this query’s output to an HTTP API endpoint that serves JSON data consumed by a React.js frontend rendering HTML tables and charts. You also mention how minimizing joins and using window functions improves performance, ensuring quick data delivery for an interactive user experience.

Also read: PL SQL Developer Salary in India: For Freshers & Experienced

Conclusion

Efficiently crafting SQL queries with advanced filtering, joins, and aggregation is fundamental for handling normalized data in scalable, containerized environments. Focus on filtering with precise WHERE clauses, utilizing window functions, and handling duplicates efficiently to optimize performance. Always explain your query logic clearly, relating it to application layers such as HTTP APIs and dynamic HTML frontends for practical impact.

If you want to learn industry-relevant SQL skills. These are some additional courses that can help you learn SQL at its core. 

• Executive Programme in Generative AI for Leaders
• Master of Design in User Experience
• Java Object-oriented Programming

Curious which courses can help you gain expertise in SQL? Contact upGrad for personalized counseling and valuable insights. For more details, you can also visit your nearest upGrad offline center.

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