What are the most common coding challenges during an interview?

I've interviewed at multiple startups and MNCs, including Facebook, Google, Microsoft etc. Some key focus areas for coding interviews are: 1. Data structures and algorithms, including time and space complexity 2. Object Oriented Programming and Design 3. Quality of code (scalable, extensible, clean etc) 4. Handling edge cases and proper error handling 5. Speed of writing error-free code 6. Efficiency of the solution 7. Explaining thought process throughout the interview

In my opinion, almost everyone out there applying for big tech companies knows basic stuff, syntaxes. So, the sole way to assess a candidate for an interviewer is to come up with an algorithm creation problem. In this way, the core logical and problem skills of a candidate can be determined.

I have given interviews at multiple startups and MNCs, including Microsoft, Amazon, Bloomberg etc. The primary focus areas for coding interviews typically include: 1. Object Oriented Programming. 2. Data structures and Algorithms. 3. Explaining your thought process throughout the interview, demonstrating problem-solving skills and decision-making. 4. Ability to optimize code beyond the brute-force approach for improved efficiency. 5. Handling Edge Cases. 6. Code Quality with proper error handling.

In my opinion the most common coding challenges during an interview, I've faced are :- -Algorithmic Problem Solving: Assessing problem-solving skills with tasks like array manipulation, sorting, and dynamic programming. -Data Structures Proficiency: Testing knowledge and application of key data structures like arrays, linked lists, trees, and graphs. -System Design Questions: Evaluating the ability to design scalable and efficient software systems. -Real-Time Coding: Writing code under time pressure on a whiteboard or online platform.

Some of the most important type of coding problems involve. For Big tech its mostly 1. Data structures and Algorithms based coding challenges. 2. Low level design with the expectation that you can design a complete service with class design and implementing the apis 3. High level design with the complete overall architecture of the system. For startups it can be a little different depending on the need of the startup and maturity 😅 1. Take home assignments of the problem startup is trying to solve. 2. Basic low level and high level design overview. 3. Coding quick can give you some leverage in early age startups.

For solving a DS questions knowing where to use which DS is an essential thing. -> Arrays: Ideal for random access and storing elements, offering efficient operations and versatility. -> Linked Lists: Allow dynamic memory allocation and efficient insertion/deletion, perfect for frequent data modifications. -> Stacks: LIFO structure useful for function calls, backtracking, and depth-first search. -> Queues: FIFO structure for breadth-first search, scheduling, and real-time systems. -> Hash Tables: Fast insertion, deletion, and retrieval for indexing, duplicate detection, and caching. -> Trees: Efficient searching, sorting, and hierarchical organization. -> Graphs: Model relationships, solve shortest path and connectivity problems.

Look out for these cues when listening to the interviewer describe the problem. If it involves fast look up by index, Array. Fast look up by a key, Hash Table. The last element inserted is important, Stack. The insertion order is important, Queue. Insertions and deletions in a sequence, Linked List. Hierarchy, Tree. Priority, Heap. Connections, paths or networks, Graph.

Arrays: As they are frequently encountered, a solid understanding of array manipulation is essential. Sliding Window Technique: This technique is invaluable for efficiently processing arrays or lists by maintaining a subset or 'window' of elements. Dynamic Programming: Being adept at dynamic programming is crucial for solving complex problems by breaking them down into simpler subproblems. Hash Sets and Hash Maps: These are fundamental for handling unique elements and efficient key-value pair storage.. Python: Given its versatility, Python is a popular choice for coding interviews. Understanding how to leverage Python's built-in data structures and libraries is advantageous.

Data structures, like arrays and linked lists, have trade-offs. Stacks and queues suit specific behaviors. Trees represent hierarchy, and graphs allow arbitrary connections. Heaps prioritize elements efficiently. Hash tables offer constant-time operations but face challenges. Choosing depends on needs—linked lists for frequent insertions, hash tables for quick searches Hashing, sorting, and traversal are crucial techniques. Real-world scenarios emphasize the impact of structure choices on performance. Mastering involves intuition and aligning structures with requirements for efficient solutions. In conclusion, DSA in software development, requires a solid understanding for effective problem-solving.

I have seen a lot of implementing-a-custom data structure questions, like building caches with optimized storage, retrieval and eviction. Solving such problems improves the perspective and the ability to appropriately use the known data structures as building blocks and evaluating trade offs. Also implementing popular data structures from scratch is also required to understand it deeply and make minor tweaks to fit various other use cases.

I have given interviews at multiple tech companies like Microsoft, Amazon, Walmart and system design questions are asked for mid to senior level engineers. When asked a question: - ask for clarifying questions - ask for which case to handle - build solutions and keep talking about your solution - first go with high level design and then go low level - discuss different trade offs, pros and cons They are an essential part of an interview and they are there to understand your capabilities of building scalable, optimised systems and how you trade pros and cons. It’s appreciated if you engage in a tech conversation about a system and you talk about every situation, solution and system.

We're talking scalable, reliable, and maintenance-friendly software wonders. Picture this: you might need to conjure up a whole system, like a social network or a chat app. Or, they could toss you an existing setup and ask you to spice it up—new features, performance boosts, or crisis control. To dazzle your interviewer, roll out a methodical plan. First up, clear requirements, scope, and assumptions. Then, sketch out the big picture: high-level architecture, key components, and their dependency. Get into the intricacies of resource estimates, costs, and, of course, the juicy trade-offs and challenges.

Especially helpful to assess mid to senior SDEs on how well they comprehend DS, Algos and technologies to put them together for a nice recipe just right for the problem at hand

Totally agree with all your comments. Apart from these, I feel one of the most important things in an interview is thinking out loud so that the interviewer can know more about your thinking process! Remember it’s not just the destination but also the journey that matters!

Well understanding of Data Structures & Algorithms is important because it helps in identifying problems & improvising solutions. But in order to make robust & scalable systems. System Design knowledge is also important such as: Designing APIs: Some interviews might involve designing a simple API for a given functionality, showcasing your understanding of software architecture and communication between components. Database design: Depending on the role, you might be asked to design a database schema for a specific scenario, demonstrating your knowledge of data modeling and relationships.

Coding interviews typically involve assessing a candidate's problem-solving skills, algorithmic knowledge, and coding proficiency. These are the things that need to be focused fluently. 1. Array and String Manipulation 2. Sorting and Searching 3. Linked Lists 4. Trees and Graphs 5. Dynamic Programming 6. Recursion 7. Hashing 8. Greedy Algorithms 9. Bit Manipulation 10. Design Problems 11. System Design (for Senior Roles) 12. Coding Challenges on Online Platforms Interviewers may focus on data structures, algorithms, problem-solving, and the ability to write clean and efficient code. It's crucial for candidates to practice these types of problems and be comfortable explaining their thought process and reasoning during the interview.

Behavioral questions offer insights into your personality, motivation, and work style, making them integral to the interview process. Utilizing the STAR method—describing the Situation, Task, Action, and Result—ensures effective responses. Sharing specific examples from past experiences, such as handling difficult problems, collaborating in a team, managing feedback, or acquiring new skills, demonstrates practical application of your abilities. Clearly articulating your actions, motivations, and the resulting outcomes provides interviewers with a comprehensive understanding of your capabilities and the valuable lessons gained. Mastering behavioral questions complements technical skills, showcasing a well-rounded candidate.

Behavioral Questions are a great way to analyze your thinking abilities and your reaction to situations, Depending on your responses recruiters can have an Idea about how fit you will be within the team. At the end of the day, a great team with common interests and common goals can reach greater heights and give best possible outcome. As there will be strong communication within the team. Tip: While answering these questions try to be as genuine as possible and align your reactions and behaviour with the Company's or Team's Core principles and Values. Because that is what recruiters will be looking for in a candidate. Try to answer this as an incident happened in your real life where you might have faced similar situation. Thank you

Behavioral questions provide insight into your soft skills and how you might fit into the company culture. Using the STAR method is a great approach to structure your responses and ensure you provide clear and concise answers. It's important to draw from real-life experiences and provide concrete examples that demonstrate your ability to handle various situations effectively. Being able to articulate your thought process, actions taken, and the results achieved will help interviewers assess your suitability for the role and the organization.

Dynamic programming problems involve breaking down a complex problem into simpler overlapping subproblems, solving each subproblem only once, and storing the solutions to subproblems to avoid redundant work. It's important to note that interview formats can vary between companies and roles. Preparing for these challenges by practicing coding problems, understanding fundamental algorithms and data structures, and brushing up on problem-solving techniques can significantly improve your performance in coding interviews.

Debugging questions assess our ability to identify and resolve code issues so keep the below points in mind -> Analyze error messages: Carefully read and understand error messages to pinpoint the source of the issue. -> Review code logic: Thoroughly examine the code to identify any logical errors or inconsistencies. -> Use print statements: Strategically place print statements to trace the flow of the code and identify problematic areas. -> Step through the code: Utilize debuggers or manually trace the code execution step by step to identify the point of failure. Navigating debugging questions demands a systematic approach and a keen eye for detail. By employing those techniques, you can efficiently pinpoint and fix code errors.

When tackling debugging questions in AI: 1. Identify the problem clearly. 2. Implement logging and monitoring for insights. 3. Isolate and reproduce the issue systematically. 4. Conduct thorough code reviews and employ comprehensive testing strategies. 5. Use debugging tools and version control for efficient bug resolution. 6. Foster collaboration, document the process, and perform regression testing. 7. Consider user feedback and conduct root cause analysis to prevent recurrence.

Debugging questions in technical interviews are designed to assess your ability to identify and fix issues in code. The interviewer may provide you with a piece of code containing errors, and your task is to identify and correct the issues. Here are some common types of debugging questions you might encounter: - Syntax Errors - Logical Errors - Runtime Errors - Variable Scope - Concurrency Issues - Exception Handling - Code Optimization - Code Review Here's a general approach to tackling debugging questions: - Read Carefully - Identify Issues - Isolate the Problem - Use Debugging Tools - Propose Fixes

When you're faced with debugging questions, start by carefully analyzing the code to understand its logic and behavior. Break down the problem into smaller parts and use systematic methods like print statements or debugging tools to pinpoint where the issue might be occurring. Approach it step by step, testing and validating changes as you go, and communicate your process clearly, showcasing your problem-solving skills and ability to systematically resolve issues in code. Practice debugging across various languages and scenarios to strengthen your troubleshooting skills.

Debugging questions assess a candidate's proficiency in identifying and rectifying issues within existing code. A professional example could involve providing a malfunctioning code snippet and asking the candidate to diagnose and fix the errors, showcasing their debugging prowess.

Most candidates are aware of a reasonable solution. They have the capability to code and explain their code. However, they fail to reach to the line of thinking that takes them to that solution. So, assuming the candidate knows the technical stuff, the skill that they need to develop is on their ability to align to the kind of thinking required to solve the problem. Specifically this calls for the following skills: 1. Ability to understand the question/problem clearly 2. Ability to ask relevant questions to gain clarity, remove ambiguities, and set the problem context 3. Ability to code in a structured manner. Some follow a top-down approach (start with method stubs and then expand as in a breadth first strategy).

To optimize your approach in coding exercises, it's essential to first thoroughly understand the problem, and then apply the most effective solution strategy.

Practicing coding problems regularly, adhering to best practices, and demonstrating clarity, modularity, and adherence to coding standards can significantly enhance your performance in these exercises. Employing a systematic and organized coding approach helps showcase your coding proficiency during interviews.

This is one of the way to select candidates. Some of the startups use this approach to evaluate candidate’s coding quality skills and also low level design skills.

Clarity in Code: Use clear, consistent naming for readability. Well-labeled variables and functions benefit both you and reviewers. Modularity and Reusability: Break down code into reusable components for maintainability and to showcase your design skills. Comments and Documentation: Code should tell a story. Add comments for complex logic and provide context through documentation for future collaboration. Rigorous Testing and Debugging: Thorough testing catches bugs early, while debugging reflects your commitment to reliable solutions. Language Proficiency: Showcase proficiency in requested languages or frameworks, highlighting versatility and problem-solving aptitude.

Many (most?) aspects of software development don't have a single "correct" answer. Candidates that understand and and clearly explain tradeoffs are in high demand.

Stories are always powerful. Even if you dont know the answer to a specific technical question - if you can relate the topic to a hands-on experience, you will sound more exciting. For example "I dont remember the exact complexity of merge sort but I know it is an efficient divide-and-conquer algorithm. When I worked on XX I had to implement scalable sorting so I did XX.." So adding a story and a personal touch gives you the chance to stand out and to control the narrative a bit. Honesty and stories are much more appreciated than just repeating memorized facts.

If you are a fresher then you will be ask question from this subjects also like operating system, computer networks, database design and object oriented programming.

For a fresher in Computer Science and Engineering (CSE), mastering programming languages (such as Python, Java, or C++), understanding web development basics (HTML, CSS, JavaScript), gaining knowledge in databases (MySQL, PostgreSQL), and familiarizing oneself with software development life cycle (SDLC) will be useful. And if you are specifically aiming for a particular job, I would suggest connect with the people who is currently working on that role and try to gather as much as info from them. You can always check their linked profile and look into their skillset , certifications and expertise. This is something that I found useful

As a former bar raiser at Amazon, System Design is the area where almost all candidates fail. Coding is basically a given for mid+ positions because you can easily pass those coding tasks to other devs. Teaching system design and understanding scaling, reliability, testability, etc. of systems can't be leet-coded into existence. Of course, I'm beginning to really question the point of coding exercises beyond just ensuring you aren't lying. Team fit, compatibility, and ability to accept change are much more important in the overall picture of creating a well-oiled and high-functioning team.