Technical Interview Questions and Answers for 2025

• Latest version of the operating system and that all patches and updates have been applied. 
• Secure the network 
• Enable logging 
• Set strong passwords 
• Limit access 
• Install security software 
• Monitor the server 

One of the main differences between static and dynamic IP addresses is that static addresses remain the same, while dynamic addresses can change over time. This means that if you have a device with a static IP address, you can always connect to it using the same address. With a dynamic IP address, however, you might need to use a different address each time you connect to the device, depending on which address has been assigned to it. 

This question is often featured in the list of top IT Technical interview questions, be ready to tackle it.

• Determine your load balancing needs: The first step is to assess your network's traffic patterns and determine the type of load balancer that will best meet your needs. You should consider factors such as the type of traffic you are balancing (e.g., HTTP, HTTPS, TCP), the amount of traffic you expect, and the number of servers you need to balance. 
• Choose a load balancer: There are several types of load balancers available, including hardware load balancers, software load balancers, and cloud-based load balancers. You should choose a load balancer that is suitable for your environment and meets your performance and scalability requirements. 
• Configure the load balancer: Once you have chosen a load balancer, you will need to configure it to distribute traffic to your servers. This will typically involve setting up virtual servers and defining rules for how traffic should be routed to each server. 
• Set up your servers: In order for the load balancer to distribute traffic to your servers, you will need to set up your servers to accept incoming traffic. This may involve installing software or making configuration changes to your servers. 
• Test and deploy: Before you deploy the load balancer in production, you should test it to ensure that it is working correctly and that it is able to handle the expected traffic levels. Once you have tested the load balancer and verified that it is working as expected, you can deploy it in your production environment. 
• Monitor and maintain: After you have deployed the load balancer, you should monitor it to ensure that it is functioning properly and to identify any potential issues. You should also perform regular maintenance on the load balancer to keep it up to date and ensure that it continues to perform well. 

This is one of the very popular IT technical interview questions.

Here is an example of how a RESTful API might work: 

• The client sends a GET request to the server, asking for a list of users. The request message might look like this: GET /users HTTP/1.1 
• The server receives the request and processes it. If the request is valid, the server sends a response message back to the client. The response message might look like this: HTTP/1.1 200 OK   
  [{"id": 1, "name": "John Smith"}, {"id": 2, "name": "Jane Doe"}] 
• The client receives the response message and processes the data. In this example, the client would receive a list of two users (John Smith and Jane Doe) and could then display the data in a user interface. 

Expect to come across this popular question in Tech interviews.  

• Build and test the new version well. 
• Prepare the production environment. 
• Deploy the new version. 
• Test the new version in production. 
• Roll back if necessary. 
• Monitor the new version. 

A stack is a data structure that allows you to store and retrieve items in a Last In, First Out (LIFO) order. You can implement a stack using an array by creating an array that stores the stack items and using two variables to track the top of the stack. 

Here is an example of how you might implement a stack using an array in Python: 

# Initialize an empty array to store the stack items 
stack = [] 
# Initialize a variable to track the top of the stack 
top = -1 
# Define a function to push an item onto the stack 
def push(item): 
  global top 
  top += 1 
  stack.append(item) 
# Define a function to pop an item off the stack 
def pop(): 
  global top 
  if top < 0: 
    return None 
  item = stack[top] 
  top -= 1 
  return item 

A must-know for anyone heading into a technical interview, this is one of the most frequently asked technical interview questions. 

A hash table is a data structure that stores key-value pairs and allows you to quickly retrieve values based on their associated keys. To implement a hash table, you will need to define a hash function that takes a key as input and returns an integer, which is used to index into an array of buckets where the key-value pair will be stored. 

Here is an example of how you might implement a hash table in Python: 

class HashTable: 
  def __init__(self, size): 
    # Initialize an array of size "size" to store the hash table 
    self.table = [None] * size 
  def hash_function(self, key): 
    # Define a simple hash function that adds up the ASCII values of the characters in the key 
    hash = 0 
    for c in key: 
      hash += ord(c) 
    return hash % len(self.table) 
  def add(self, key, value): 
    # Calculate the hash of the key and use it to find the index in the table 
    index = self.hash_function(key) 
    # If the bucket at the index is empty, create a new linked list to store the key-value pair 
    if self.table[index] is None: 
      self.table[index] = LinkedList() 
    # Add the key-value pair to the linked list at the index 
    self.table[index].add((key, value)) 
  def get(self, key): 
    # Calculate the hash of the key and use it to find the index in the table 
    index = self.hash_function(key) 
    # If the bucket at the index is empty, return None 
    if self.table[index] is None: 
      return None 
    # Otherwise, search the linked list at the index for the key-value pair 
    return self.table[index].find(key) 

Depth-first search (DFS) and breadth-first search (BFS) are two algorithms for traversing a graph or tree data structure. 

In a depth-first search, the algorithm starts at the root node and explores as far as possible along each branch before backtracking. This means that the algorithm will visit all the nodes in one branch of the tree before moving on to the next branch. 

In a breadth-first search, the algorithm starts at the root node and explores all the nodes at the current level before moving on to the next level. This means that the algorithm will visit all the nodes at the shallowest level of the tree before moving on to the deeper levels. 

One of the main differences between DFS and BFS is the order in which they visit the nodes in the tree. DFS visits nodes in a depth-first order, while BFS visits nodes in a breadth-first order. This can affect the performance of the algorithm and the way that it processes the data. 

Another difference between DFS and BFS is the way that they handle traversing the tree. DFS uses a stack to keep track of the nodes that it needs to visit, while BFS uses a queue. This can affect the space complexity of the algorithm, as DFS uses a fixed amount of space regardless of the size of the tree, while BFS uses space proportional to the size of the tree. 

Overall, DFS and BFS are useful algorithms for traversing a tree or graph, and the choice of which one to use will depend on the specific needs of your application. 

It's no surprise that this one pops up often in Technical interviews.  

Dynamic programming is a method of solving problems by breaking them down into smaller subproblems and storing the results of these subproblems to avoid recalculating them. This can be an efficient way to solve problems that have overlapping subproblems, as it allows you to reuse the solutions to these subproblems rather than recomputing them. 

To use dynamic programming, you typically define a function that takes the input to the problem as an argument and returns the solution. You can then break the problem down into smaller subproblems and use the function to solve these subproblems recursively. As you solve the subproblems, you store the results in a table or array, which you can use to avoid recalculating the subproblems. 

Here is an example of how you might use dynamic programming to solve the Fibonacci sequence problem in Python: 

def Fibonacci(n): 

  # Initialize an array to store the Fibonacci numbers 
  fib = [0] * (n + 1) 
  # Base case: fib(0) = 0, fib(1) = 1 
  fib[0] = 0 
  fib[1] = 1 
  # Iterate over the range 2 to n 
  for i in range(2, n + 1): 
    # Calculate the next Fibonacci number using the previous two 
    fib[i] = fib[i - 1] + fib[i - 2] 
  # Return the nth Fibonacci number 
  return fib[n] 

In this example, the function uses an array to store the Fibonacci numbers and calculates them using a bottom-up approach, starting from the base cases and building up to the desired result. This allows the function to avoid recalculating the same subproblems and improve the efficiency of the solution. 

Dynamic programming is a useful technique for solving problems that have overlapping subproblems and can be used to solve a wide range of problems in computer science. 

A stack is a data structure that allows you to store and retrieve items in a Last In, First Out (LIFO) order. A linked list is a data structure that consists of a sequence of nodes, where each node contains a value and a reference to the next node in the list. 

One of the main differences between a stack and a linked list is the way that they store and access data. A stack uses an array to store data and allows you to access the top element of the stack directly, while a linked list stores data in nodes and requires you to follow the references between nodes to access the data. 

Another difference between a stack and a linked list is the way that they grow and shrink. A stack has a fixed size, so you can only push items onto the stack if there is space available. A linked list, on the other hand, can grow and shrink dynamically as you add and remove items. 

Overall, both stacks and linked lists are useful data structures with their own strengths and limitations. You can use a stack when you need a simple data structure for storing and accessing items in a LIFO order, and you can use a linked list when you need a more flexible data structure that allows you to easily add and remove items. 

This often features in the list of top Technical round questions, don't miss this one.

One of the most frequently posed basic Technical interview questions, be ready for it.  

In a database, a primary key is a field that is used to uniquely identify each row in a table. A foreign key is a field that is used to establish a link between two tables. 

A primary key is a column or set of columns that is used to uniquely identify each row in a table. It must contain unique values, and it cannot contain null values. A primary key is often used as an index to help optimize the performance of queries on the table. 

A foreign key, on the other hand, is a field in one table that is used to reference the primary key of another table. It is used to establish a link or relationship between the two tables. For example, you might use a foreign key to link a customer table to an orders table so that you can track which customers have placed which orders. 

One of the main differences between a primary key and a foreign key is that a primary key is used to identify rows within a single table, while a foreign key is used to establish relationships between tables. Another difference is that a primary key must contain unique values, while a foreign key may contain duplicate values. 

NoSQL is a type of database that does not use the traditional SQL relational model for storing and retrieving data. NoSQL databases are designed to handle large amounts of data that may be structured, semi-structured, or unstructured and to scale horizontally across multiple servers. 

There are several types of NoSQL databases, including key-value stores, document databases, column-oriented databases, and graph databases. Each type of NoSQL database is designed to handle a specific set of data and use cases, and they differ in the way that they store and retrieve data. 

One of the main differences between NoSQL databases and traditional relational databases is the way that they store and structure data. Relational databases use tables to store data, with rows representing individual records and columns representing the data fields. On the other hand, NoSQL databases may use various data models, such as key-value pairs, documents, columns, or graphs, to store data. 

Another difference between NoSQL and relational databases is how they scale. Relational databases are typically designed to scale vertically by adding more resources to a single server. In contrast, NoSQL databases are designed to scale horizontally by adding more servers to the database cluster. 

Overall, NoSQL databases are useful for handling large amounts of data that may not fit well into the traditional relational model, and they offer a number of benefits, such as horizontal scaling, flexibility, and high availability. However, they may not offer the same level of consistency and transactional support as relational databases. 

A staple in Technical interview questions, be prepared to answer this one.  

Normalization is the process of organizing a database in a way that reduces redundancy and dependency and helps to improve the integrity and performance of the database. Normalization is typically applied to relational databases, which are organized into tables of rows and columns. 

The first normal form (1NF) is the most basic normal form, and it requires that each column in a table contains a single value and that the values in each column be of the same data type. The second normal form (2NF) builds on the first normal form and requires that each non-key column be fully dependent on the primary key. The third normal form (3NF) further reduces redundancy by requiring that non-key columns be independent of one another. 

Firewalls work by examining the data packets that are transmitted over a network and comparing them to a set of rules or criteria. If the data packets meet the criteria, the firewall allows them to pass through. If the data packets do not meet the criteria, the firewall blocks them or sends an alert. 

The rules or criteria that are used by a firewall can be based on various factors, such as the source and destination of the data packets, the port number, the protocol, or the type of data. For example, a firewall might allow incoming data packets from a trusted source but block data packets from an untrusted source. 

Here is how Domain Name System work -

• When a user enters a domain name into their web browser, the browser sends a request to a DNS resolver to translate the domain name into an IP address. 
• The DNS resolver looks up the domain name in a DNS server to find the corresponding IP address. 
• If the DNS server has the IP address in its cache, it returns it to the DNS resolver. If the DNS server does not have the IP address in its cache, it sends a request to a root nameserver to find the IP address. 
• The root nameserver responds with the address of a top-level domain (TLD) nameserver, such as .com or .org. 
• The TLD nameserver responds with the address of a domain-level nameserver, such as example.com. 
• The domain-level nameserver responds with the IP address of the domain name. 
• The DNS resolver sends the IP address back to the web browser, which uses it to establish a connection to the website.

This is a regular feature in the list of top Technical interview questions, be ready to tackle it.  

Linear search is a simple search algorithm that checks each item in the list in sequence until it finds the target item or determines that the item is not present. Linear search has a time complexity of O(n), meaning that the time it takes to perform the search increases linearly with the list size. 

Binary search, on the other hand, is a more efficient search algorithm that divides the list in half at each step and compares the target item to the middle item. If the target item is greater than the middle item, it searches the right half of the list, and if it is less than the middle item, it searches the left half of the list. This process continues until the target item is found or it is determined that the item is not present. Binary search has a time complexity of O(log n), which means that the time it takes to perform the search increases logarithmically with the size of the list.

This is a frequently asked question in Technical rounds. 

A binary tree is a data structure that consists of nodes arranged in a tree-like structure, with a maximum of two children for each node. Each node in a binary tree has a value and two children, which are referred to as the left child and the right child. 

The diameter of a binary tree is the longest path between any two nodes in the tree. It is not necessarily the same as the height of the tree, which is the maximum distance from the root to a leaf node. 

def diameter(node): 
  # Base case: if the node is None, return 0 
  if node is None: 
    return 0 
  # Calculate the height of the left and right subtrees 
  left_height = height(node.left) 
  right_height = height(node.right) 
  # Calculate the diameter of the left and right subtrees 
  left_diameter = diameter(node.left) 
  right_diameter = diameter(node.right) 
  # Return the maximum of the following three values: 
  # - The height of the left subtree 
  # - The height of the right subtree 
  # - The sum of the heights of the left and right subtrees, plus 1 
  return max(left_height + right_height + 1, left_diameter, right_diameter) 

There are several algorithms that you can use to find the shortest distance between two nodes in a graph. Here are two common approaches: 

• Dijkstra's algorithm: This algorithm is used to find the shortest path between two nodes in a graph with non-negative edge weights. It works by maintaining a set of nodes for which the shortest path is already known and continually updating the shortest path estimates for the remaining nodes. To implement Dijkstra's algorithm, you will need to use a priority queue to keep track of the nodes that need to be processed. 
• Breadth-first search (BFS): This algorithm is used to find the shortest path between two nodes in an unweighted graph. It works by starting at the starting node and exploring all of its neighbors before moving on to the next level of nodes. To implement BFS, you can use a queue to keep track of the nodes that need to be processed. 

Expect to come across this popular question in tech interviews.  

Multitasking is the ability of an operating system to run multiple tasks or processes concurrently. This allows the computer to perform multiple tasks at the same time, such as running a web browser and a word processor at the same time. 

Multithreading is a technique that allows a single task or process to be divided into multiple threads of execution, which can be run concurrently on a multicore processor. This allows the task to be broken down into smaller pieces that can be run in parallel, which can improve the performance of the task. 

One of the main differences between multitasking and multithreading is that multitasking involves running multiple tasks or processes concurrently. In contrast, multithreading involves dividing a single task or process into multiple threads that can be run concurrently. 

Another difference is that multitasking is typically handled by the operating system, while the programmer typically implements multithreading. 

A must-know for anyone heading into a React interview, this is one of the most frequently asked technical interview questions for software engineers.  

• A file system is a system that organizes and manages the storage of files on a computer or other device. It is responsible for storing, retrieving, and organizing files and managing the available storage space on the device. 
• There are many different types of file systems, but most follow a similar basic structure. A file system typically consists of a hierarchy of directories, also known as folders, which are used to organize the files into a logical structure. Each directory can contain files and other directories, which can be nested to any depth. 
• When you create a file, it is stored in a specific directory, and the file system assigns it a unique name and location. The file system also stores metadata about the file, such as its size, creation date, and access permissions. 
• To access a file, you use a file manager or a command-line interface to navigate to the directory where the file is stored and open it. The file system retrieves the file from storage and presents it to you in the appropriate application. 
• When you delete a file, the file system marks the space that the file was using as available for reuse, but the actual data is not necessarily erased immediately. Instead, it is often overwritten when new data is written to the same location. 
• Overall, the file system is an essential part of a computer system, as it manages the storage and organization of files on the device.

CPU process scheduling is managing the execution of processes on a computer's central processing unit (CPU). The CPU is a critical resource, as it is responsible for executing the instructions of a process and performing the calculations and operations needed to run a program. To ensure that processes are executed efficiently and fairly, the operating system uses a process scheduler to manage the execution of processes on the CPU. The process scheduler is responsible for determining which process should be executed next and allocating the CPU to that process. 

There are many different CPU process scheduling algorithms, each with its own rules and criteria for determining which process should be executed next. Some common types of scheduling algorithms include: 

• First-Come, First-Served (FCFS): This algorithm executes processes in the order they arrive, without considering their priority or other factors. 
• Shortest Job First (SJF): This algorithm executes the process with the shortest execution time first, regardless of when it arrived. 
• Priority Scheduling: This algorithm executes processes based on their priority, with higher-priority processes being executed before lower-priority processes. 

It's no surprise that this one pops up often in Technical round interviews.  

• It enables a computer to run larger applications than the available physical memory. 
• It allows multiple applications to be run concurrently, even if the total memory requirements of the applications exceed the available physical memory. 
• It can improve the performance of a computer by allowing the OS to swap out inactive pages of memory to disk storage, making more physical memory available for active processes. 
• It allows a computer to run more efficiently, as it can make better use of its available memory resources. 

SSH works in the following fashion -

• A client initiates a connection to an SSH server by sending a request over an unsecured network, such as the internet.
• The server authenticates the client by asking for a username and password or using a public key-based authentication method.
• Once the client is authenticated, the server and the client establish a secure and encrypted connection using a shared key.
• The client can then use the secure connection to execute commands and transfer files to and from the server.
• When the client is finished, it closes the connection, and the server terminates the session.

A common question in IT Technical interviews, don't miss this one.