Lexi Lewtan’s Post

🌟Array Problem Solving Roadmap for Product-Based Interviews🌟 Looking to master array problems for your next technical interview? Below I have curated the must do questions that helped me crack offers. 🚀 Beginner Level – Start Here! 1. [Find the Maximum and Minimum in an Array](https://lnkd.in/gJ7bMf3v) 2.[Two Sum](https://lnkd.in/gbzA58qK) 3. [Remove Duplicates from Sorted Array](https://lnkd.in/g-vTNUxF) 4. [Best Time to Buy and Sell Stock](https://lnkd.in/gy7az5UE) 5. [Move Zeroes](https://lnkd.in/g9-M7gpq) 6. [Intersection of Two Arrays II](https://lnkd.in/gUav5mpq) 7. [Find All Numbers Disappeared in an Array](https://lnkd.in/gKj9DCyY) 8. [Find the Missing Number](https://lnkd.in/gCG4aSsk) 🌿Intermediate Level 9. [Rotate Array](https://lnkd.in/guXSqiBN) 10. [Find All Duplicates in an Array](https://lnkd.in/gNK3_SC4) 11. [Maximum Subarray (Kadane’s Algorithm)](https://lnkd.in/g3FG5y_N) 12. [Contains Duplicate III](https://lnkd.in/g9G_PPtX) 13. [Subarray Sum Equals K](https://lnkd.in/gPmsTDPD) 14. [Product of Array Except Self](https://lnkd.in/gCD_eRSM) 15. [3Sum](https://lnkd.in/gEb5ypAs) 16. [Container With Most Water](https://lnkd.in/gDVmPZrA) 17. [Longest Consecutive Sequence](https://lnkd.in/gWC_fepc) 18. [Merge Intervals](https://lnkd.in/g_f364_y) 19. [Find the Duplicate Number](https://lnkd.in/gGQptupK) 20. [Increasing Triplet Subsequence](https://lnkd.in/gQwh4YRz) 🚀 Follow for more! I’ll be breaking down different data structures in the coming days—stacks, queues, trees, graphs, and more! Each post will have a roadmap just like this to boost your skills and get you interview-ready. Don’t miss out. Having trouble transitioning your carrer. Connect with me: https://lnkd.in/gbZcWUha #leetcode

🚀 Excited to share two incredible resources for anyone preparing for technical interviews! 🚀 If you're aiming to ace those algorithm and data structure questions, these comprehensive guides are just what you need: Tech Interview Handbook's Algorithm Study Cheatsheet - This guide covers all the essential topics, offering clear explanations, efficient solutions, and practical tips to enhance your preparation. Check it out here: https://lnkd.in/gXiVB7BE Techie Delight's Data Structures and Algorithms Problems - This collection features a wide range of problems to help you practice and master key concepts in data structures and algorithms. Explore it here: https://lnkd.in/g_cRnuSn Whether you're a beginner or looking to brush up on your skills, these resources are invaluable tools to boost your confidence and performance in interviews. Happy coding and best of luck with your interviews! 💻💡 #TechInterview #Coding #Algorithms #DataStructures #InterviewPreparation #CareerGrowth #TechCareers #CodingInterviews #DeveloperResources

You are as #good as your #design, You are as bad as your design. Everything is based on design, so design smartly to create valuable systems. #innovation #engineering #india #coding

Adding to the bucket list.

Algorithms you should know before taking System Design Interviews What are some of the algorithms you should know before taking system design interviews? I put together a list and explained why they are important. Those algorithms are not only useful for interviews but good to understand for any software engineer. One thing to keep in mind is that understanding “how those algorithms are used in real-world systems” is generally more important than the implementation details in a system design interview. What do the stars mean in the diagram? It’s very difficult to rank algorithms by importance objectively. I’m open to suggestions and making adjustments. Five-star: Very important. Try to understand how it works and why. Three-star: Important to some extent. You may not need to know the implementation details. One-star: Advanced. Good to know for senior candidates. Over to you: did I miss anything important on the list? Which ones do you know and which you don’t? --  Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages): https://bit.ly/bbg-social #systemdesign #coding #interviewtips  .

How do you gauge a candidate during a software Whiteboard Interview? Maybe because I have proctored or participated in this interview style with such frequency that I am convinced, by its written definition, the problem being solved produces no qualitative value. I am biased and might fall into this rhetoric because I do poorly during whiteboard interviews. It's not the pressure of what I call "the fish bowl feeling," being observed by someone with authority over me for 45 minutes. It's the fact that a poorly chosen interview question is about knowing "the trick" to the problem. Take the "Two Sum" problem from LeetCode as an example. "Two Sum" feels like the kind of question I have seen during an interview, and like my first submission to LeetCode today, it wasn't particularly efficient. Would I pass the interview? Probably not; will I pass it the next time? Heck yeah! What qualitative value as an interviewer would I have in my rubric for this problem? - Did I finish the solution in a reasonable time? - How did I communicate my process? - Was my solution easy to understand? - Did I follow Red/Green/Refactor? - Did the candidate use external resources often? - What was my stress response like? - Did I guess and check or meditate on the solution, running it only when I was done? In the end, I don't think we will see an end to the whiteboard interview, but if I could convince every interviewer to focus less on whether the candidate figured out the "trick" of the problem and instead showed a competency for communication, was confident, and followed an iterative process. I pass candidates who show a capacity for self-education(use resources, find the solution) and good process(refactoring) over how fast or efficient their solution was. There is a business model here that promotes doing remote live coding interviews. Like most things with a monetary incentive for its continued presence its on us to use these interactions to gauge a candidate on their whole self. Remember, all live interviews are behavioral interviews, we learn more through the interaction than the result of the interaction! https://lnkd.in/gMnqKTGh #interviewing #whiteboard #software

💡React Interview Question💡 How to decide whether to have a single state, containing an object to store the entire component state or declare a separate state for each data in the functional component? 𝗔𝗻𝘀𝘄𝗲𝗿: Whenever you have related pieces of information that you want to manage together, then store them in an object in a state. For example, if you have form inputs where each input value is a separate field in an object, it can be convenient to manage them all together in a single state object. This can help keep your code organized and make it easier to update multiple values at once as shown below: const [state, setState] = useState({  firstName: '',  lastName: '',  email: '', }); const handleChange = (event) => {  const { name, value } = event.target ;  setState((prevState) => ({  ...prevState,  [name]: value,  })); }; However, use separate state variables when each piece of state is independent and doesn't need to be managed together. For example, if you have a component that needs to track both a count and a boolean value which are not related to each other, it would make sense to use separate state variables for each of them as shown below: const [count, setCount] = useState(0); const [isModalOpen, setIsModalOpen] = useState(false); #ReactStateManagement #FunctionalComponents #StateDeclaration #StateManagement #ComponentState #CodeOrganization #ReactBestPractices #StateUsage #ComponentDesign #ReactDevelopment

✅ Looking to ace your next tech interview?  It all starts with having a solid foundation of fundamentals. With a little bit of effort, you'll be ready to confidently crack your interview. Let's get started 🚀 ☑️ Concepts It is a core part of programming that helps to improve the code and operations. It helps you to identify how the code is performing. Also, helps you to think about a good approach to solving problems. Example: Bit Manipulation, Big O, Memory, Dynamic Programming ☑️ Data Structures Data structures are building blocks of any program just like HTML for the web. So it is important to understand how these data structures work. If you understand how these building blocks work, you can improve your code and get a better understanding of algorithms. Example: Arrays, Linked List, Hash, Stack, Queues. ☑️ Algorithms Algorithms help in solving problems and optimizing operations based on input. Implementing algorithms from scratch is a good way to understand their working process. Examples: Binary Search, Merge Sort, Quick Sort, Breadth-First Search, Depth-First Search. During an interview, interviewers ask questions to test your problem-solving skills and coding ability. So, if you have a good understanding of the fundamentals, be confident and do your best. Let me know in comment section To get the detailed explanation of all these concepts in the next post 👇Click here to know more about me 👇 https://lnkd.in/gu4aBWHh

I've often observed a common pattern during technical interviews where candidates face challenges in effectively communicating their problem-solving approach. Here's a scenario: Interviewer: You'll be tackling the Combination Sum problem. Interviewee: Yes, I've solved this problem before. Its Time Complexity is XYZ and Space Complexity is XYZ. I'll use the Dynamic Programming approach. (Interviewee proceeds to code directly) (Makes some mistakes, Multiple runs, Multiple submits, gets it running) Interviewee: This is the time complexity, and this is the space complexity. Days later: Notification: we have decided to progress with other candidates for this role This scenario highlights a common mistake made by candidates during interviews, prompting me to share a guide on tackling Tech problems effectively. 1. **Active Listening and Understanding**: It's crucial to thoroughly understand the problem statement before jumping into the solution. 2. **Requirements Gathering**: Gather all requirements, including input-output specifications, edge cases, and the preferred approach. 3. **Brute Force Approach**: Start by explaining and coding the brute force approach quickly. It's natural for the mind to explore brute force solutions first. 4. **Optimization**: Discuss the limitations of the brute force approach and propose optimizations. 5. **Optimized Approach**: Present your optimized solution, explaining the reasons behind it. 6. **Complexity Analysis**: Clearly explain the time and space complexities of your solution. 7. **Self-Check and Further Optimization**: Evaluate if there's room for further optimization and inquire with the interviewer if unsure. Key Points: 1. **Clear Communication**: Continuously communicate your thought process with the interviewer. 2. **Clean Coding Style**: Maintain a clean coding style for better understanding. 3. **Granular Solutions**: Break down your solution into granular steps. 4. **Logical Naming Conventions**: Use logical variable names for clarity. 5. **Null Checks**: Prioritize null checks before coding solutions. I've encapsulated this approach in a LeetCode solution I recently posted, demonstrating how to methodically approach tech problems: https://lnkd.in/eSnb-PcS I believe there's always room for improvement which is why I invite my network to share additional insights and tips in the comments section. Let's foster a culture of continuous learning and growth together.

How do you assess competency in an interview? I’m not a fan of take-home tests. Instead ask “What project are you most proud of and why?” Then (very) importantly, dig into the details. Inquire about their data and decision-making process: Why MongoDB? Why a random forest? What features did you choose, and why? Did you consider alternatives? Which features were most impactful, and how do you know? What would you do next to enhance the results? There’s no set script here; it’s about exploring something they should know intimately. If candidates can’t answer these questions, it suggests they either mindlessly followed a process or are taking credit for work they weren’t very involved in. Not every decision requires exhaustive analysis (most don't), but thoughtful responses like, “I chose a random forest because it performs well on our data with minimal tuning” or “Doing that would add complexity but only affect 4% of users, which wasn’t worth the effort” indicate genuine engagement with the project. The goal is to unearth the nuanced decisions that only a hands-on contributor would know and understand how they make decisions. If someone struggles to explain a project they claim to be proud of, that’s a significant red flag. I’m sharing a mini-series on testing competency in an interview, this is just one method I use - follow me for more insights.