Here's how you can tackle a data structures interview question effectively.

Actually I agree with this, before starting to solve complex problems. 1. Make sure you have strong basics in that particular language. 2. Get comfortable with yourself solving easy problems in your specific language. 3. Try to understand the clear picture of how time complexity, space complexity and data structures work. 4. Upgrade yourself by solving medium level problems. 5. In this way, you can become expert in solving or cracking this kind of interviews. Note: Finally, practicing consistently is the main ingredient of cracking interviews.

Understanding basics is the key to success. It's the basics from where we start building our solutions and finally reach to a compact and time efficient solution. We should try to break each problem into pieces and first understand what it's asking then start solving those problems using basic methods and forming a complete solution.

1. Understand the Problem Statement 2. Break Down the Problem 3. Communicate Clearly (vocalize your thought process ) 4. Identify Relevant Data Structures 5. Throw BruteForce solution 6. Choose the Optimal Data Structure 7. Dry Run the Solution 8. Tell the time and space complexity Note - Stay relaxed and present your best self.

Before starting interviews, projects, or hackathons, understand their purpose and benefits. Knowing your goals ensures a productive learning journey. Focus on the right approach, guidance, implementation, and execution. You don't need to know everything upfront; clear objectives are key. Questioning why an array problem can't have O(1) time optimization shows critical thinking. Basics are essential—don't rush. Avoid skipping easy steps and jumping to difficult problems, as this can lead to confusion and a return to basics.

1. Read the problem statement carefully. 2. Ask the interviewer questions to ensure you understand all aspects of the problem. 3. Divide the problem into smaller, more manageable parts. 4. Choose data structures that are most appropriate for solving the problem. 5. Begin with a brute force solution that is easy to implement. 6. Determine the time and space complexity of your initial solution. 7. Explain your thinking to the interviewer, including any potential optimizations. 8. Walk through your code with an example to ensure it works as expected. Remember, communicating your thought process is crucial. It helps the interviewer understand your approach and can lead to useful hints if you get stuck.

Always remember that an interview is a two-way communication. Don't expect to just be given a problem to solve like on LeetCode. One of the things the interviewer wants to measure is your communication and requirements gathering skills.

Question you should ask yourself before and after solving a problem. BEFORE 1. Can I restate the problem? 2. What are the inputs that go into the problem? 3. What are the expected output to the problem? 4. Can the output be derived from the input? AFTER. 1. Solve or Simplify 2. Look back & Refactor 3. Are you getting the expected result? 4. Can you understand it at a glance ( not possible all the time though ). 5. Can you improve the performance ( bigO ) ? 6. How have other people solved it?

As you practice, patterns will emerge, speeding up your thinking. When tackling a problem, think aloud about all approaches. For new questions, discuss your thought process with the interviewer to align with their thinking. Once you understand the problem, choose the right data structure. For example, for finding the kth largest element in an unsorted array, propose a brute force O(NlogN) solution, then optimize with a max heap to achieve O(logN) complexity. Which will lave an impact that you are aware about the naive solution and can optimize it further.

No interviewer wants you to directly solve the problem as soon as you get the question. Define inputs-outputs clearly, identify contraints and edge cases, and choose appropriate data structures. Design an effecient algorithm, and test with diverse scenarios with time complexity.

Once you've analyzed the problem, outline your approach verbally before writing any code. This shows your thought process and provides the interviewer with insight into your problem-solving strategy. Discuss the data structure you plan to use and justify its suitability, such as choosing a binary search tree for efficient searching, insertion, and deletion. This step is crucial for demonstrating both your reasoning and coding skills.

Design Approach. Learn the Techniques like Sliding Window, Prefix Sum, Fast & Slow Pointer and many more to map your question to these techniques and realise which approach you can utilise to optimally solve this question. Also while Designing approach make sure to be vocal about your thoughts. It helps the recruiter know how you tackle the issue. One last thing, ask questions, Nobody wants you to just jump into question and it always left a positive impression to get proper information about the questions.

At this phase, since it's the moment to explain my approach, I would create something visual very simple just to help both me and the interviewer understand my reasoning. A quick scheme made on Excalidraw or even a drawing on a software like Paint would suffice. A visual asset has the power to guide us when it comes to writing code objectively.

Once you have something in your mind, its very important that you are discussing your approach with your interviewer. It ensures that you and interviewer on the same page. Also, you can note down the inputs and outputs briefly on pen and paper, off course with interviewer permission. Explain which data structure you will be going to use and why you choose this over other available data structures. Last but not the least explain the time and space complexities of the solution you are providing.

Agree with the tip. It seems also important to scrutinize the task conditions, available resourses etc. Because these parameters may be crucial in choosing proper data structure.

A skill that you’ll learn with experience is the importance of clean and readable code. A clean and readable code is not only easy to be understood by the interviewer but also easy to debug in case of bugs. A readable code includes: a) Proper indentation b) Meaningful variable and method names c) Comments wherever required As you’ve already broken your problem into sub-problems (as per my answer to the question 2 in the article), you should think around having separate non-overlapping methods for each such use-case. Always follow the DRY principle (Don’t Repeat Yourself). Always pull out common code pieces into methods and reuse it.

Writing clean and readable code is essential for both you and the interviewer to understand your solution easily. Using meaningful variable names. Clearly explaining each part of your code, like the type of tree traversal you’re implementing and the reasoning behind it, showcases your understanding and thought process.

Analysis - Done! Discussion of na?ve and optimal solutions - Done! Now it's time for the implementation. Follow these basic steps: 1. Naming Conventions: Use meaningful names for variables and functions relevant to the problem. 2. Indentation: Ensure proper indentation to avoid confusion and enhance readability. 3. Comments: Comment on complex or non-obvious lines for clarity. 4. Modular Functions: Avoid doing everything in one function. Break down problems into subproblems to simplify debugging and improve organization. 5. Avoid Mid-code Dry Runs: Save dry runs for the end to avoid confusion. Regular practice of these steps will simplify interviews for you!

Don't jump over the code directly. With interviewer permission write pseudocode on paper. It will definitely help you in actual code. Once you are done with the pseudocode start converting into your favourite language(java, c, c++ etc.). Always be sensible while choosing variable names. It should be meaningful. Method/function names should clearly tell the purpose of it. While coding keep discussing the steps you are writing. It keeps your interviewer engaged and saves a lot of time in compare to explaining everything at the end.

Feel free to just write stuff in pseudo code and do not worry too much about if it will compile. The goal is to work through the problem, without wasting too much time on an actual syntax or correct method calls. You want to see if there's anything you might have missed. Much like a free-write for an English paper, take the same approach here and get your full thoughts out as quickly as possible. You can work on editing it to look better and making it compile after you're confident the solution is the right one

After coding, thorough testing is crucial. Test your code with various scenarios, including edge cases. For example, if you've written a queue implementation (FIFO), test it with an empty queue, one element, and multiple elements. This validates your solution and shows attention to detail. Discussing these tests with your interviewer highlights your commitment to robustness and understanding of testing's importance. Systematic testing improves code quality and reliability, demonstrating competence and professionalism in interviews.

After coding, test your solution with different cases, including edge cases. Walk through your code with these cases to ensure it behaves as expected. Discussing these test cases with your interviewer demonstrates thoroughness and highlights the importance of testing in software development. For example, test a function to find the maximum value in a binary tree with an empty tree, a single node, and multiple levels.

Testing basic test along with code. You can use print statements. Then think about the corner cases and try testing those. Like in binary search overflow can be an edge case.

Sometimes it is better to think about test cases before actually writing the code. It helps you to understand the task more completely before implementing the solution. Hence meet much less of bugs writing the code.

Testing is a critical step in validating the functionality and robustness of your code. By using diverse cases, including edge cases, you ensure your solution can handle all possible scenarios. This not only demonstrates your thoroughness but also your understanding of the importance of reliable software. Discussing test cases with your interviewer further highlights your attention to detail and commitment to quality.

Its ok if you can't optimize further. Not everyone remembers KMP algorithm for string pattern matching. These academic algorithms took years to develop. They are for implementation and not for mugging.

After going through brute force solution, if it was the only thing that came up in your mind in that limited time frame. Take your time after coding up to think about possible optimization over your current solution. The optimized solution and your reasoning behind it can be a game changer to get you through that interview. Optimizations are always valued. Performance of software is most critical thing after meeting basic functional requirement.

For optimisation, you need to find the current complexity and think of other solutions that could decrease the complexity. You should know the basic of complexity when its O(n^3) vs O(n^2). Think if any nested loop could be removed. This will come with practice.

This is usually the second half of a problem. If you can solve a problem. You will be asked to optimize the code for a better time complexity. It usually comes down to how much you know about the data structures and different approaches.

Coming up specific algorithm to solve the problem is not difficult, however coding the solution is where the struggle is, I call this as “Coding Mechanics”. If you consider there are several variations of backtracking, BFS, DFS. You should have absolute clarity of the structure, variants, invariant, coding style. This can significantly help to in terms of speed and accuracy of the solution, debugging

A common practice during interviews that I believe should be avoided is immediately recalling a familiar problem's previous solution. For example, imagine you're given a problem in an interview that looks familiar. But wait !!! Your brain starts to recall the approach you used before, mixing it up with concepts from other problems, leading to confusion. Instead, treat it as a new problem and try to solve it from scratch.

I have a hot take on grinding leetcode problems: that practice is a plague on the software industry. Please, avoid wasting your time on busywork, use the tech skill you have to solve problems you find interesting at the limits of your programming ability. Feel free to get inspiration from leetcode problems, but please, solve problems that are meaningful to you. Working on your own project means you create more durable artifacts (open source repo), and can have a better answer for that convicting interview prompt: "tell me about your technical side projects". Last I interviewed at a FAANG company, I crushed the coding interviews because of practice on my own scripting engine for my game side project. Showing off the game was a huge win too.

Interviews often reveal more about a candidate than their resume ever could, highlighting qualities such as problem-solving skills, cultural fit, and adaptability. One fascinating aspect is the "structured interview" technique, where each candidate is asked the same set of standardized questions. Research shows that this method is more effective in predicting job performance compared to unstructured interviews. Structured interviews minimize biases and provide a fair comparison by focusing on job-relevant criteria, thereby increasing the likelihood of selecting the best candidate for the role.

I would say communication and discussion are very important before you start the problem. The interviewer should be aware of what you're thinking and both of you should discuss it before you jump to write code. It shows your understanding and your approach to the problem. Sometimes it helps because you might come up with a solution that the interviewer hasn't thought of.