Start by locking the fundamentals — arrays, strings, linked lists, stacks, queues, trees, graphs, and dynamic programming — and then practise two to three problems daily. Learn to derive the time and space complexity of every solution and revise core patterns like two pointers, sliding window, hashing, binary search, and recursion. In the last two or three weeks, switch to timed mock interviews, because in DSA interviews at product-based companies, explaining your thought process clearly matters as much as writing correct code.

Fresher rounds usually begin with arrays and strings — reversal, rotation, removing duplicates — then move to hashing problems, linked list operations, stack- and queue-based questions, basic recursion, and simple tree traversals. Interviewers commonly follow up with "Can you optimise this?" or "What is the time complexity?", so always present a brute-force approach first and then improve it step by step. For freshers, clearly communicating the approach often counts for more than solving the toughest problem.

Use a fixed framework: restate the problem and clarify the constraints, discuss a brute-force solution, identify its bottleneck, and then optimise using patterns like hashing, two pointers, or dynamic programming. State the time and space complexity before you start coding, write clean code with readable variable names, and finish by dry-running your solution on edge cases such as empty input, a single element, and the maximum input size. This structure shows strong problem-solving skills even when the optimal answer doesn't come to you immediately.

Interviewers expect depth instead of volume — harder graph and dynamic programming problems, optimisation follow-ups, and questions about trade-offs rather than basic array puzzles. Your work experience also gets woven in, so be ready to explain how you debugged performance issues or handled large-scale data in real projects. Focus on clean, production-quality code and confident communication, because experienced candidates are rarely given any relaxation on fundamentals.

A good beginner tutorial should move from variables, data types, loops, and functions to pointers and memory management, OOP concepts like classes and inheritance, and finally the STL — vectors, maps, sets, and algorithms. Make sure it includes exercises after every concept, because tricky C++ ideas like pointers only become clear through practice. If English explanations feel heavy, starting with tutorials in your own language, such as Punjabi or Hindi, can make the basics much easier to absorb.

Linux system programming is writing software that communicates directly with the operating system kernel through system calls instead of relying only on high-level libraries. It covers creating and managing processes with fork, exec, and wait, threads, file handling with file descriptors, signals, inter-process communication such as pipes and shared memory, and sockets. It is written mostly in C and powers operating system tools, servers, drivers, and embedded software.

Get comfortable with C first, since nearly every system call is designed around it, and learn basic Linux commands and shell usage. Then cover the core topics one by one — file I/O, processes, signals, pthreads, IPC, and sockets — writing a small program for each on a real Linux machine such as Ubuntu, either natively or in a virtual machine. Pair the theory with hands-on mini projects like building your own basic shell, and the concepts will stay with you far longer than passive reading.

Linux kernel programming means writing code that runs inside the kernel itself — loadable kernel modules, device drivers, custom system calls, and other kernel-space components. While system programming runs your code in user space and requests services from the kernel through system calls, kernel code interacts directly with hardware and kernel internals. It is considered an advanced skill, so strong C, data structures, and operating system fundamentals are expected before you begin.

Build the prerequisite layer first — solid C, operating system concepts, and Linux system programming — because kernel code assumes you already understand processes, memory, and scheduling. Then start small: compile and load a "hello world" kernel module, write a simple character device driver, read the source of drivers similar to what you want to build, and always experiment inside a virtual machine so mistakes don't damage your system. Classic device-driver texts and kernel developer communities are the usual next step from there.

The most frequently asked ones cover process vs thread, what happens during fork, exec, and wait, virtual memory and paging, file descriptors versus file pointers, signal handling, and IPC mechanisms like pipes, message queues, shared memory, and semaphores. Interviewers often make you write code too — a basic shell, zombie-process handling, or synchronised threads — so practise writing these programs rather than only reading theory. Going through a curated list of Linux system programming interview questions before the interview helps, but hands-on practice is what actually gets you selected.

Self-study is possible since man pages, documentation, and standard books cover the theory well, but a structured course speeds things up by giving you a fixed topic order, guided assignments, and someone to unblock you when concepts like fork semantics or signals behave unexpectedly. If you are preparing for a role switch or an interview deadline, a course with projects and doubt-solving support usually pays off; if you have time and discipline, a book-plus-projects route works too. Whichever you pick, write real programs on a Linux machine, because this subject cannot be learned by watching videos alone.

Demand comes mainly from embedded systems and IoT companies, automotive firms working on EVs and infotainment, telecom and networking companies, semiconductor firms, and backend-heavy product startups. Common Linux system programming jobs include Systems Software Engineer, Embedded Linux Developer, Kernel or Device Driver Engineer, and Platform Engineer, and the same skills also strengthen your profile for DevOps and backend roles. Bengaluru, Hyderabad, Pune, and Chennai have the largest concentration of these roles.

Most learners start with Linux System Programming by Robert Love — it is compact and covers system calls, processes, memory, files, and signals in a readable way. Once comfortable, The Linux Programming Interface by Michael Kerrisk is the definitive deep reference that working professionals keep on their desk. Whichever you choose, code along on a real Linux machine, because the concepts only stick when you practise every chapter instead of just reading it.