Top 20 Technical Interview Questions & Answers to Ace Your Next Interview

Preparing for a technical interview can be overwhelming, especially when you aren't sure what to expect. 

From your first job to a more advanced role, often the interview process tests how clearly you understand core concepts and how confidently you explain them. 

That's why having a reliable set of questions and answers can really make a difference.

Below, I have collated some of the most frequently asked technical interview questions with clear, concise explanations. 

The aim is that you will understand why your answer should be as it is, so that you can respond confidently and not just memorize the responses.

Technical Interview Questions & Answers

By the end, you'll know what interviewers usually look for and how to present knowledge in a calm, structured way. Let's dive in and make your next technical interview easier to prepare for.

1. What is a Technical Interview?

A technical interview measures your knowledge of the tools, concepts, and ability to solve problems associated with a particular job function. Some examples include coding, APIs, documentation, test generation, data analysis, and design. Technical interviews are intended to evaluate your thought process, application of knowledge, and ability to work on actual job-related tasks.

2. How to Prepare for a Technical Interview?

You can prepare by focusing on three areas:

1. Get to know the basics

Understand the fundamentals of the position you are applying for, whether it be API documentation, software development, UX writing or technical writing.

2. Review the job description

Each company requires a different set of skills; therefore, review the JD and prepare for the knowledge they outlined in the JD: tools, writing examples, style guides, domain knowledge, etc.

3. Do some hands-on work

Most companies request candidates to do an assignment. Do sample projects such as:
• Create an API document.
• Write a user guide.
• Improve a web page.
• Describe a difficult subject in layman's terms.

Further preparation

1. Review your portfolio. (It is very important to have well-written example work)
2. Practice explaining technical information in clear, concise layman's language.
3. Document several situations where you have solved a problem or created documentation in the past.
4. Familiarize yourself with the tools relevant to the position you are applying for (Markdown, Confluence, Git, Swagger etc).

3. Why Technical Interviews are Conducted?

When evaluating potential employees, companies conduct technical interviews for:

1. Verifying your skill level based on performance rather than just what you wrote on your resume.
2. Learning about your approach to solving problems, the way you think about solutions is just as important, if not more so, than the answer itself.
3. Assessing whether you would be able to work with their existing team, this is particularly necessary when considering job candidates in technical writing, documentation, and product development.
4. Determining whether you have the capacity to learn and adapt, the technology industry evolves rapidly, so they want to know how well you will handle learning new things.

Overall, the technical interview gives the employer a good indication of your potential to perform in the role advertised.

4. Explain the difference between a linked list and an array

A linked list and an array both have the purpose of housing collections of data; however, they function in radically different ways.

An array maintains its elements in a contiguous block of memory. This means that all items will sit beside one another. 

Because of this, access to any element is extremely fast-you directly access it by using an index. On the other hand, insertion and removal are relatively slow because shifting is necessary. 

For instance, if you insert something at the beginning, all the other elements must shift one step further to accommodate this new element.

In the linked list, elements are stored in separate nodes anywhere in the memory. Every node contains both the data and the pointer to the next node. 

You can easily insert or delete an item because you only change the pointers; no shifting is necessary. 

However, access to an element will be more time-consuming because you need to travel from the beginning of the list to the position you want.

In other words,

1. Array: Fast access, slow insertion and deletion.

2. Linked list: Slow access, fast insertion, and deletion.

This makes arrays better for tasks where you read data often, while linked lists are better when frequently updating the data.

5. What is the time and space complexity of a binary search algorithm?

Binary search is designed to find an element in a sorted list by repeatedly dividing the search range in half. Because the search area shrinks so rapidly, it's much faster than checking each element one after another.

Time Complexity

1. Best case: O(1) — This is when the target element is at the middle position on the first check.

2. Worst-case: O(log n) — Because each step cuts the search space in half, the number of steps grows very slowly even as the list gets large.

3. Average case: O(log n) — On average, it still reduces the range at the same rate.

In other words, the binary search is fast because at each step it ignores half the data.

Space Complexity

1. Iterative approach: O(1) - Since it only uses a few extra variables, such as start, end, and mid, the memory usage will always remain constant.

2. Recursive approach: O(log n). This is because each recursive call adds a layer to the call stack; therefore, space will grow in relation to how many levels of recursion occur.

So in summary:

1. Time Complexity: O(log n)

2. Space Complexity: O(1) for iterative and O(log n) for recursive. This combination makes binary search one of the most efficient search techniques.

6. How would you debug a memory leak in your code?

This occurs when your program continues to consume more and more memory but fails to release that memory when it is no longer needed. Consequently, the application will consume increasingly more memory over time, which may lead to sluggishness or even a crash.

The following steps would be used to debug a memory leak:

1. Confirm There is a Memory Leak

First, I'd verify that the issue really is a memory leak and not just a temporary spike in usage.

• Run the application for some time, observing memory consumption with the help of Task Manager, Activity Monitor, or built-in monitoring utilities.

• If memory usage continuously goes higher and never comes back, it could be a serious indication of a leak if the app is either sitting idle or doing the same kind of work over and over.

2. The bug is always reproducible.

Next, I'd try to find a repeatable scenario that triggers the leak.

• Determine which ones cause memory growth: things like opening a screen many times, processing lots of requests, and uploading files repeatedly.

• Create a small test case that repeats this, either in a loop or via automated tests, through which I can study memory behaviour in a controlled fashion.

This serves to localize the bug and assists in testing of the patches.

3. Utilize profiling tools

Next, I'd use a memory profiler or other similar tool to find out what is occupying memory.

• Depending on the language/environment, this could be:

• C/C++: tools like Valgrind, AddressSanitizer, or similar.

• For Java, one may use VisualVM, YourKit or Eclipse Memory Analyzer.

• For .NET: dotMemory or similar profilers.

• For Python/Node.js: Memory profiling modules and heap snapshots.

With these tools, I'd look for:

• Objects that grow in number.

• Large data structures that are never released.

• References that keep objects "alive" even though they are no longer needed.

4. Identify what is holding references

A memory leak usually happens because something still holds a reference to data that should be garbage collected or freed.Common causes include:

• Global variables holding onto objects, or singletons.

• Event listeners or callbacks that are never removed.

• Collections (lists, maps, arrays) to which elements are added, but never removed.

• Circular references (when objects reference each other and thus are never released, mostly in certain languages/environments).

I would look at what objects the profiler claims to be "stuck" and trace back who is keeping them.

5. Correct the root cause.

• Once I know where the leak comes from, I'd adjust the code:

• Remove objects from lists or maps when they are no longer needed.

• Unregister event listeners when a component or object is destroyed.

• Close files, database connections, and network sockets when finished.

• In manual memory management languages -such as C/C++- make sure free() or delete is called for every malloc/new.

• Avoid unnecessary in-memory caching that grows without limits.

The goal is to ensure that every allocated resource is eventually released.

6. Retest and follow up

• After applying a fix:

• Run the same test scenario as before.

• Watch memory usage over time.

• Make sure it doesn't keep going up; it stabilizes. If the memory still grows, I'd repeat the profiling steps, because sometimes there can be multiple leaks.

7. Explain the concept of object-oriented programming (OOP) principles.

This occurs when your program continues to consume more and more memory but fails to release that memory when it is no longer needed. Consequently, the application will consume increasingly more memory over time, which may lead to sluggishness or even a crash.

The following steps would be used to debug a memory leak:

1. Confirm There is a Memory Leak

First, I'd verify that the issue really is a memory leak and not just a temporary spike in usage.

• Run the application for some time, observing memory consumption with the help of Task Manager, Activity Monitor, or built-in monitoring utilities.

• If memory usage continuously goes higher and never comes back, it could be a serious indication of a leak if the app is either sitting idle or doing the same kind of work over and over.

2. The bug is always reproducible.

Next, I'd try to find a repeatable scenario that triggers the leak.

• Determine which ones cause memory growth: things like opening a screen many times, processing lots of requests, and uploading files repeatedly.

• Create a small test case that repeats this, either in a loop or via automated tests, through which I can study memory behaviour in a controlled fashion.

This serves to localize the bug and assists in testing of the patches.

3. Utilize profiling tools

Next, I'd use a memory profiler or other similar tool to find out what is occupying memory.

• Depending on the language/environment, this could be:

• C/C++: tools like Valgrind, AddressSanitizer, or similar.

• For Java, one may use VisualVM, YourKit or Eclipse Memory Analyzer.

• For .NET: dotMemory or similar profilers.

• For Python/Node.js: Memory profiling modules and heap snapshots.

With these tools, I'd look for:

• Objects that grow in number.

• Large data structures that are never released.

• References that keep objects "alive" even though they are no longer needed.

4. Identify what is holding references

A memory leak usually happens because something still holds a reference to data that should be garbage collected or freed.Common causes include:

• Global variables holding onto objects, or singletons.

• Event listeners or callbacks that are never removed.

• Collections (lists, maps, arrays) to which elements are added, but never removed.

• Circular references (when objects reference each other and thus are never released, mostly in certain languages/environments).

I would look at what objects the profiler claims to be "stuck" and trace back who is keeping them.

5. Correct the root cause.

• Once I know where the leak comes from, I'd adjust the code:

• Remove objects from lists or maps when they are no longer needed.

• Unregister event listeners when a component or object is destroyed.

• Close files, database connections, and network sockets when finished.

• In manual memory management languages -such as C/C++- make sure free() or delete is called for every malloc/new.

• Avoid unnecessary in-memory caching that grows without limits.

The goal is to ensure that every allocated resource is eventually released.

6. Retest and follow up

• After applying a fix:

• Run the same test scenario as before.

• Watch memory usage over time.

• Make sure it doesn't keep going up; it stabilizes. If the memory still grows, I'd repeat the profiling steps, because sometimes there can be multiple leaks.

8. Describe the difference between GET and POST requests in HTTP.

GET and POST represent two of the most common methods in HTTP that allow sending data between a client, such as a browser, and a server. Although they both appear to perform similar actions, they differ in the way they work and are used for different purposes.

GET Request

Information is retrieved from the server via a GET request. In fact, opening a web page or fetching data from an API usually sends a GET request.Key points:

• It sends data through the URL, usually as query parameters, such as ?name=sureshi&city=delhi.

• It is visible in the address bar.

• It is best for simple and safe operations, such as fetching data.

• GET requests can be cached, bookmarked, and stored in browser history.

• Because URLs cannot be very long, they have size limitations.

• Use GET when you want to read data, and nothing is being changed on the server.

POST Request

A POST request to the server is used to send data, generally when something needs to be created, updated, or processed. Key points:

• The data is sent in the request body, not in the URL.

• It's better for sensitive data, as it is not visible in the address bar.

• This can be used for actions such as form submission, logging in, file upload, or creation of new records.

• POST requests cannot be cached and are not saved in the browser history.

• They do not have any significant size limit; therefore, huge amounts of data can be transferred, such as images or details of forms.

• Use POST when you need to send data that might change something on the server.

9. What are the benefits of using a cache?

A cache is a temporary storage area that holds the most frequently used data for quick access.

Rather than fetching the same information again and again from a slow source, like a database, file system, or API, the cache delivers it instantaneously.

This improves performance by cutting down on unnecessary work.The following are the key benefits:

1. Faster Response Time

Because cached data is a lot closer to the application (very often it would reside in memory), access is way faster than fetching the data from a database or remote server. This, all things being equal, makes for quicker-loading websites and applications that feel more responsive.

2. Decreased Burden on Servers

Serving data from the cache, rather than from the main database or a backend service, means those systems receive fewer requests. This reduces the load on them and helps prevent slowdowns or even system crashes when traffic is high.

3. Enhanced User Experience

Users get results quicker, pages load smoothly, and applications feel more reliable. It especially matters in apps where speed counts, e.g., an e-commerce site, a dashboard, or a mobile app.

4. Better Scalability

Because caching reduces the overhead of the main server, a system can handle more users without needing expensive hardware upgrades. This is one of the key motives why large platforms rely heavily on caching.

5. Reduced Costs

Accessing data from memory is cheaper compared to constant database queries or external service calls. By reducing database usage, companies can save on resources, infrastructure, and cloud bills.

10. Explain the concept of normalization in databases

Normalization is the process of organizing data in a database so that the data can be kept clean, efficient, and free from unnecessary duplication.

In summary, normalization involves structuring tables such that each piece of information is stored once. This minimizes errors, saves space, and makes the database better to work with.

Normalization usually involves several steps, known as normal forms, through which the structure of a database is improved: 1NF, 2NF, 3NF, and so on.

Here's what normalization helps with:

1. Removes Duplicate Data (Redundancy)

If the same information is stored in many places, it becomes hard to keep everything updated. Normalization ensures that each fact is stored only once.

Example: Instead of writing the same customer details in every order, you store the customer information in a separate table and link it by customer ID.

2. Improves Data Accuracy (Integrity)

When data exists only in one place, it is easy and safe to update. There will be no spread of inconsistent and obsolete information throughout the database.

3. Makes the Database More Efficient

Clean, normalized tables speed up the queries and make maintenance at the system level much easier. You won't have large tables full of repeated values.

4. Breaks Data into Logical Groups

Normalization splits the data into smaller, meaningful tables and builds their respective relationships, thereby making the database flexible and scalable.

5. Avoids Problems with Updates, Inserts, and Deletions

Poor database design can lead to a number of problems, such as:

• Having to update the same value in multiple rows

• Not being able to insert a row because some unrelated data is missing 

• Accidental deletion of critical data 

• Normalization reduces these risks.

11. How would you handle errors in your code?

Error handling is a major part of any programming in order to write robust and stable code. Errors may occur due to many reasons: invalid input, network issues, missing files, or unexpected user actions. Instead of letting the program crash, proper error handling allows the application to respond graciously.

Here's how I would handle errors in my code:

1. Use Try–Catch (or similar) Blocks

First, you have to wrap risky code in a structure that is capable of catching errors.

Example:

• Trying to open a file

• Calling an API

• Converting user input

If something goes wrong, the program doesn’t crash; the error is caught, and I can decide what to do next.

2. Validate Input Early

Many errors can be avoided by checking the inputs before using them. For instance:

• Make sure a number is actually a number

• Ensure a file exists before opening it

• Check required fields in a form

Validating early, I prevent unnecessary errors from happening later in code.

3. Log the Error

Basic logging is vital. Whenever a mistake happens, I log information, for example:

• What the error was

• Where it happened

• The values involved

This helps diagnose the problems without guessing. Most of the time, logs are important in production systems since end users cannot see the internal errors.

4. Displaying Friendly Messages to Users

Users should never see confusing technical errors. Instead, I display clear, helpful messages such as:

• “Something went wrong. Please try again.”

• “Please check your input and try again.”

• Technical details remain in the logs, not on the user's screen.

5. Employ Custom Error Handling Where Necessary

In larger programs, I might define custom error types so I can deal with different situations separately. For example:

• A login error

• Payment failure

• A missing resource 

This helps the program take an appropriate action based on the type of error it is dealing with. 

6. Test and simulate failures

I also test how the system reacts when things go wrong, like disconnecting the internet or giving invalid data. This ensures that any error handling is robust and predictable.

12. What is the difference between a compiler and an interpreter?

A compiler and an interpreter both translate human-written code into something a computer can understand; however, they accomplish this in different ways.

A compiler translates the entire program into machine code before it runs. This means the program is checked and converted all at once.

Key points:

• Converts the complete code upfront

• Shows only errors after the entire program has been analyzed.

• Runs faster once compiled because the machine code is ready

• Used by languages like C, C++, and Java (partly)

Simple example: It's just like translating a whole book into another language before printing it.

Interpreter

• An interpreter translates code line by line while the program is running.

• It reads, translates, and executes each line instantly.

Key points:

• Converts code step by step

• Errors appear right away when a faulty line is reached 

• Slower execution than compiled code, as translation occurs at run time 

• Used by languages like Python, JavaScript, Ruby 

Simple example: It's like having a live translator speak every sentence as you say it.

13. Explain the purpose of a join operation in SQL

In SQL, the JOIN operation is used to put together data from two or more tables into a single result. Since much related information stored within databases is divided into separate tables, to avoid duplication and keep information organized, a JOIN helps you link such tables together based on a common column.

Why JOINs are needed:

In real-world databases, data is seldom stored in a single large table. For instance:

• A Users table for storing user information

• An Orders table holds all orders

You have to bring in data from both tables to tell you which user placed which order. This is possible with a JOIN.

What JOINs help you do:

1. Combine related information that is stored in different tables

2. Generate meaningful reports, like a report listing customers along with their orders

3. Avoid data duplication: keep tables separate but still retrieve complete information

4. Work with normalized databases where data is broken into logical tables

14. Explain the concept of recursion

Recursion is a programming technique where a function calls itself to solve smaller parts of a larger problem. 

The idea is to break a complex task into simpler, repeatable steps until you reach a point where no further breakdown is needed.

To work correctly, recursion always has two key parts:

1. Base Case

This is the stopping point. It tells the function when to stop calling itself. Without a base case, the function would run forever and eventually crash.

Example: If you're counting down from 5 to 1, the base case is when the number reaches 0—at that point, recursion stops.

2. Recursive Case

This is the part of the function that calls itself with a smaller or simpler input. Each call pushes the problem toward the base case.

15. What are some advantages and disadvantages of using Agile methodology?

Agile methodology offers several advantages: faster delivery, continuous feedback, and the ability to adapt quickly to changes. 

Teams work in short cycles, which assists in spotting issues early, improving collaboration, and delivering value to customers more frequently. 

However, Agile also has some disadvantages. It requires a high level of team communication, which can be hard in distributed teams. 

Planning can feel less predictable since requirements usually evolve, and projects without strong discipline easily lose direction. 

Besides, Agile may also not be suitable for projects that require strict documentation, fixed timelines, or detailed upfront planning. 

Despite these limitations, Agile remains one of the most popular approaches to team collaboration in conditions of flexibility and quick responsiveness.

16. Describe a situation where you had to troubleshoot a complex technical issue

In one such project, one of our web applications, without warning, started slowing down during peak usage. The problem was hard to isolate since no error messages were given, and the slow down occurred only at certain times. 

I started off by monitoring system resources: memory usage of the application kept going up, never to be released, even after traffic reduced. 

This gave a clue to detect a possible memory leak. Using profiling tools, objects in memory were traced, where a large list was used to store temporary data, which was never cleared after use. 

Each new request just continued to add more, eventually slowing the system down. Having pinpointed the exact problematic function, I updated the code to properly clear the list and free those resources. 

The memory stabilized once deployed, and the application went back to normal speed.

 This experience taught me the importance of systematic investigation, using the right tools, and validating assumptions through data rather than guesswork.

17. How do you stay up-to-date with the latest advancements in your field?

I keep myself updated through reputable industry sources, short online courses, and frequent reading of blogs or newsletters from experts in the field. 

Equally, I have used some time in experimenting with new tools or technologies through small personal projects, helping me learn through practice. 

When possible, I like to join webinars, attend meetups, or connect with peers to exchange ideas on how others understand real-world challenges. 

This combination of learning, practicing, and staying connected means I am growing and know just about the most current trends and best practices in the field.

18. Explain the difference between authorization and authentication

Authentication and authorization are two important security concepts. Even though these two terms sound similarly related, their purposes are very different.

Authentication describes the process of verifying who the user is. It answers the question: “Are you really the person you claim to be?”

This normally includes verification of things like passwords, OTPs, fingerprints, or login credentials.

Example: You enter your username and password to log into an app and are thus authenticated. 

On the contrary, authorization determines what a user can do after they have been authenticated. It answers the question: “What actions or resources can this user access?” 

Example: A normal user might not have permission to access the admin settings or modify sensitive data even after he logged in.

19. What is the purpose of unit testing in software development?

In unit testing, the aim is to see whether the individual parts of a program, called "units," work on their own. 

A unit can be a function, a method, or a small block of code. By testing small pieces alone, developers are able to catch any kinds of errors before they grow into bigger and hard-to-fix problems.

Unit tests provide a guarantee that all parts of your code will work as intended, even when new features are integrated into the code or changes are made. 

Additionally, this helps keep the codebase more reliable because developers will be able to immediately tell when a change accidentally breaks something that previously worked. Another advantage is improved confidence during development. If the tests are passing, then you know the core logic is stable. 

Ultimately, unit testing leads to cleaner code with fewer bugs and smoother development down the line.

20. Describe the concept of Big Data and how it's handled differently from traditional data

Big Data can be described as extremely large and complex datasets that go past the ability of traditional data-processing tools to handle. Unlike regular data, which can easily fit into a single computer or relational database, Big Data grows at scale and speed that precludes it from being stored and managed conventionally for analysis.

Big Data is often described using the 3 V's, and sometimes more:

1. Volume

Big Data does indeed entail huge amounts of information, sometimes terabytes, petabytes, or more. Traditional systems struggle with this scale because they rely on a single server or limited storage.

2. Velocity

Big Data is created at high velocity-for example, social media posts, online transactions, sensor data, or website activity. Traditional databases are incapable of processing the incoming data with sufficient speed.

3. Variety

Big Data comes in many formats:

• Tabulated (tables)

• Semi-structured (JSON, XML) 

• Unstructured: Videos, emails, images, logs 

Traditional systems were primarily designed for structured, table-based data.

Big Data uses modern technologies that distribute the workload over many machines instead of using one system. 

Tools such as Hadoop and Spark enable the data to be stored in distributed systems and processed by different machines simultaneously, making the analysis a lot faster. 

Because the volume of data is very large, it isn't structured, so NoSQL databases are commonly used because of their ability to handle flexible, unstructured data in comparison to traditional SQL databases. 

Big Data systems are also designed to scale horizontally, meaning you can add more servers as the data grows, and they often support real-time processing through tools like Kafka or Spark Streaming. 

To put that in simpler terms, classic data uses tables of well-structured form and one-machine-only processing, while Big Data needs distributed storage, parallel computation, and flexible databases that can cope with its size and complexity.

21. Explain the difference between TCP and UDP protocols

The two protocols for sending data over the internet are TCP and UDP, though they work in entirely different ways. TCP stands for Transmission Control Protocol and is all about reliability. 

It ensures that all packets of data reach the destination in order and thus is quite apt for web browsing, downloads, and emails, wherein accuracy matters. 

On the other hand, UDP is the User Datagram Protocol, and it chooses speed over reliability. It transmits without checking if packets are received or remain in order, making it faster but less dependable. 

This is why live video, online games, and voice calls use UDP. Naturally, TCP sends data slowly but reliably, and UDP does so very fast but with less guarantee.

22. How would you approach designing a scalable system?

In designing a scalable system, my focus is on making it handle more users, more data, or more traffic without breaking or slowing down much. 

• First, I would understand the requirements clearly: what the system needs to do, how many users are expected, what types of operations will be performed on it, that is, read-heavy, write-heavy, or both, and finally, any performance targets in terms of response time.

• Once I know that, I would design the system in a modular fashion, breaking it up into smaller services or components so each can be independently improved upon or scaled if need be.

• Next, I would think about ways to handle increased load. That would include using load balancers to distribute traffic across several servers, using caching to reduce repeated work for common requests, and choosing databases that support scaling-such as read replicas, sharding, or using a mix of SQL and NoSQL depending on the type of data. 

• I would also design with fault tolerance in mind so that if one part fails, the whole system doesn't go down. That might include redundancy, health checks, and automatic restarts. 

• Finally, I would set up monitoring and logging from day one to track performance, errors, and usage patterns. That helps in finding out bottlenecks early on and adjusting the capacity as the system grows. 

My overall approach would combine clear requirements, clean design, smart use of infrastructure, and continuous monitoring to ensure the system can grow smoothly over time.

23. Do you have any questions for us?

Professional and Polite

• Is there anything you’d like me to clarify or expand on?

• Would you like to ask me anything at this stage?

Confident and Engaging

• Is there anything you’d like to know from my side?

• Do you have any questions you'd like me to answer?

Friendly and Natural

• Is there something you’d like to ask me before we wrap up?

• Any questions you're curious about from my end?