How to Prepare for Coding Challenges

I'm often asked what to do if one can't solve a coding problem after pondering it for 15-20 minutes. People often hit a wall while preparing for coding interviews. I did too. Here are my suggestions: 👉𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞 𝐀𝐜𝐭𝐢𝐯𝐞𝐥𝐲: Instead of passively reading the explanation after 15-20 minutes, try to struggle with the problem a bit longer. This is where the learning really happens. If you can't solve it, try to identify which part of the problem you find challenging. Is it the initial approach? Is it a tricky corner case? Once you've identified your weak point, you can then focus on solutions to that specific issue. 👉𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐭𝐡𝐞 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬, 𝐍𝐨𝐭 𝐉𝐮𝐬𝐭 𝐭𝐡𝐞 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬: While it's tempting to memorize solutions, the interviewers are more interested in how you think and approach a problem. So, focus on the underlying techniques, patterns, and algorithms. Once you deeply understand a concept, you'll be able to apply it to a variety of questions. 👉𝐒𝐩𝐚𝐜𝐞𝐝 𝐑𝐞𝐩𝐞𝐭𝐢𝐭𝐢𝐨𝐧: Instead of reviewing all questions every day, use the spaced repetition technique. For example: 1. Review a question you've solved today. 2. Review it again in two days. 3. If you solve it successfully, review it again in a week. 4. If you still solve it successfully, review it again in two weeks. This technique will help you remember the approach over the long term. 👉𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐰𝐢𝐭𝐡 𝐏𝐞𝐞𝐫𝐬: Talking through your solution, or even your confusion, with someone else can be very beneficial. This could be in online forums, study groups, or with friends preparing for similar interviews. Explaining your thought process to someone else can help solidify your understanding. 👉𝐂𝐚𝐭𝐞𝐠𝐨𝐫𝐢𝐳𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬: Many problems can be grouped together into certain categories like sliding window. Once you've solved a few problems in a category, try to summarize the general approach that apply to that category. This way, when faced with a new problem, you can try to fit it into a known category and apply the corresponding techniques. 👉𝐌𝐨𝐜𝐤 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬: Consider mock interviews with friends or using platforms that offer this service (check https://lnkd.in/gwrarnyD). This not only helps with problem-solving but also gets you comfortable with explaining your thought process. 👉𝐕𝐚𝐫𝐢𝐚𝐭𝐢𝐨𝐧 𝐢𝐬 𝐊𝐞𝐲: Instead of solving similar problems repeatedly in a short span, try a mix. For instance, after two-pointer problems, move on to recursion, then sliding window, and then come back to two-pointers. This cyclic variation helps cement your learning better. 👉𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐌𝐢𝐬𝐭𝐚𝐤𝐞𝐬: Whenever you can't solve a problem, instead of just reading the solution, ask yourself why you couldn't solve it. Is there a pattern or concept you're consistently missing? By recognizing your weak spots, you can focus on improving in those areas. #codinginterview #datastructures

🎯 400 LeetCode problems solved - but this isn't your typical "grinding problems" post. Like training a machine learning model, I approached algorithmic problem-solving with a focus on data quality and diversity. Just as ML models need varied, high-quality data points to generalize well, I found that solving diverse problems across different patterns and domains builds better problem-solving intuition. My systematic approach: Pre-coding Analysis (Link to sample doc in comments) • Document multiple potential approaches • Analyze time & space complexity for each approach • Think through tradeoffs before writing any code • Consider edge cases and constraints Practice Execution • Used stopwatch to measure performance • Aimed to solve while explaining clearly within: - Easy: 10 minutes - Medium: 15 minutes - Hard: 25 minutes • Focus on thinking aloud - crucial for interviews Deep Dive Process • Rigorous complexity analysis • Explore optimization opportunities • Document learnings and patterns • Regular mock interviews on Pramp The goal wasn't to solve all 3000+ problems, but to build a robust "model" that could generalize to new problems effectively. Each solved problem is like a new training data point, helping my brain recognize patterns and edge cases. Key learning: The magic happens in the pre-coding analysis. Writing down different approaches and analyzing tradeoffs before coding helped me: - Build stronger problem-solving intuition - Communicate my thought process clearly - Make better engineering decisions - Save time during actual coding I'll share a sample doc in the comments. It's been crucial for building a systematic approach to problem-solving. To those on this journey: Keep your head down, document your thinking, and remember - you're not just solving problems, you're building a framework for approaching any technical challenge.

My client passed 8 out of his next 10 technical assessments in just 4 weeks of working together They went from failing every technical assessment, hating and blaming the system… But the truth is: You don't rise to the occasion. You fall to the level of your preparation. So I taught him what I teach all my clients: Don’t cram for interviews, train like an athlete, and practice like a professional. Here’s how I help clients prep for interviews without burning out or waiting until an assessment shows up in their inbox. We build coding prep like a habit stack. Each layer trains a real-world interview skill. 𝟭) 𝗟𝗲𝘃𝗲𝗹 𝟭: 𝗗𝗮𝗶𝗹𝘆 𝗰𝗼𝗱𝗶𝗻𝗴 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲 (𝟯𝟬 𝗺𝗶𝗻𝘀) Daily coding practice on Platforms WHY? To start interview prep for the assessment and live coding rounds 𝟮) 𝗟𝗲𝘃𝗲𝗹 𝟮: 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 + 𝗽𝘂𝘀𝗵 𝘁𝗼 𝗚𝗶𝘁𝗛𝘂𝗯 Daily coding practice on Platforms + Git, committing progress\ WHY? To retain more information and be able to reflect on your progress 𝟯) 𝗟𝗲𝘃𝗲𝗹 𝟯: 𝗔𝗱𝗱 𝗰𝗹𝗲𝗮𝗿 𝗰𝗼𝗺𝗺𝗶𝘁 𝗺𝗲𝘀𝘀𝗮𝗴𝗲𝘀 Daily coding practice on Platforms + Git, committing progress with good commit messages WHY? To practice clearly communicating to your team with git 𝟰) 𝗟𝗲𝘃𝗲𝗹 𝟰: 𝗧𝗿𝗮𝗰𝗸 𝘁𝗶𝗺𝗲 𝗶𝗻 𝗰𝗼𝗺𝗺𝗶𝘁 𝗺𝗲𝘀𝘀𝗮𝗴𝗲𝘀 Daily coding practice on Platforms + Git, committing progress with good commit messages + Adding time, then adding how long it took in your commit message WHY? To practice like the interview (assessment or live coding) with a little more nervousness, so you can crush the interviews 𝟱) 𝗟𝗲𝘃𝗲𝗹 𝟱: 𝗧𝗮𝗹𝗸 𝗼𝘂𝘁 𝗹𝗼𝘂𝗱 𝘄𝗵𝗶𝗹𝗲 𝘀𝗼𝗹𝘃𝗶𝗻𝗴 Daily coding practice on Platforms + Git committing progress with good commit messages + Adding time, then adding how long it took in your commit message + Talking aloud WHY? To practice like the interview (live coding, case study, system design) by communicating your actions and reasoning, so your practice is more like the real thing, and you can crush the interviews 𝟲)𝗟𝗲𝘃𝗲𝗹 𝟲: 𝗦𝗵𝗮𝗿𝗲 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗹𝗲𝗮𝗿𝗻𝗲𝗱 (𝗮𝘀 𝗰𝗼𝗻𝘁𝗲𝗻𝘁) Daily coding practice on Platforms + Git committing progress with good commit messages + Adding time, then adding how long it took in your commit message + Talking aloud + Creating a mini content WHY? To start building the habit of creating content from your learnings. The easiest way to create content is to document your life This method has helped my clients pass 80 %+ of coding rounds and land roles in data, analytics, and tech. Take a look at what stage you are on, then, when you are consistent, you can move to the next one. Let’s build the habit, not the panic. ♻️ Repost if you found this helpful

6 years ago, I started my career at Microsoft as a SWE after interviewing at 30+ companies. 70-80% of people who apply never get their shot because they get rejected in the first round of FAANG+ companies because of the tough online assessments(OA). OAs are always harder than the coding rounds (that’s what most people don’t know) If you’re aiming for a good role at a product-based company, here’s your roadmap to crack OAs this year: ► Step 1: Know the Types of OA Questions   - DSA-Based Coding Problems (Most Common)     - Focuses on algorithms, data structures, and problem-solving skills.     - Topics: Dynamic Programming, Graphs, Binary Search, Greedy, Trees, etc.  - Aptitude & Logical Reasoning (Mainly for Service-Based Companies)     - Mathematical aptitude, puzzles, probability, permutations, etc.     - Important for Infosys, TCS, Accenture, and some startups.  - Domain-Specific Questions     - Operating Systems, DBMS, OOPS, Networking, System Design.     - Asked for backend, full-stack, or cloud roles.  - Debugging, Output & Code Optimization Questions     - Identify errors, expected output and optimize code for efficiency.     - Often seen in Meta, Amazon, and Microsoft OAs.  ► Step 2: Core DSA Topics You Must Master for OAs   — 1. Arrays & Strings – Sliding window, two pointers, prefix sums.   — 2. Binary Search – Finding min/max in sorted arrays, search space reduction.   — 3. Recursion & Backtracking – Subsets, permutations, N-Queens.   — 4. Linked Lists & Stacks/Queues – Fast-slow pointers, LRU cache.   — 5. Trees & Graphs – BFS, DFS, shortest paths, disjoint set union (DSU).   — 6. Sorting & Searching – QuickSort, MergeSort, custom comparators.   — 7. Dynamic Programming – Kadane’s Algorithm, Knapsack, DP on Trees.   — 8. Bit Manipulation – XOR tricks, counting set bits.  👉 Important: You don’t need to solve 1000+ questions, but solving 150 well-chosen problems covering all these topics will prepare you well.  ► Step 3: How to Prepare for OAs (7-Step Plan)  📌 Step 1: Master Data Structures & Algorithms (DSA)   - Start with basic DSA topics and then move to medium/hard problems.   - Use LeetCode, CodeForces, and GeeksforGeeks for practice.   - Solve pattern-based problems to recognize recurring techniques.  📌 Step 2: Master Binary Search & Sorting   - Product-based companies often ask Binary Search variations (rotated array, monotonic functions).   - Sorting techniques help in interval problems, greedy problems, and searching problems.  📌 Step 3: Learn Graphs & Trees Well   - Graphs appear in most FAANG OAs:     - BFS, DFS, Dijkstra’s, Kruskal’s, Prim’s, DSU.   - Trees involve:     - Binary Search Tree (BST), AVL trees, Segment Trees.  Continued Here: https://lnkd.in/g_beynNW

How I Prepared for Microsoft Loop Interviews in 10 Days 🚀 After completing my online assessment, I had only 10 days to prepare for the Microsoft loop interviews. Here’s the strategy I followed that helped me get ready: 1. Coding Rounds 💻 I had already completed around 150-200 Leetcode questions and was familiar with common coding patterns. So, for the coding rounds, I focused on Microsoft-tagged questions on Leetcode. My target was to solve at least 2 coding questions daily. This helped me stay sharp and practice the specific types of problems Microsoft tends to ask. 2. Low-Level Design (LLD) 🏗️ LLD was my primary focus, as I wasn’t very prepared for this area. It can seem intimidating at first, but with a solid understanding of OOP principles, it’s much easier. Over the first couple of days, I reviewed 12 key design patterns and aimed to solve 2 LLD problems daily. This really boosted my confidence. LLD Resource: https://lnkd.in/gt_SraQ4 📚 3. Behavioral Round 🗣️ One of the most underrated parts of the interview process! I recommend Googling common behavioral questions for FAANG and preparing your personal stories for each one based on your past experiences. If you're interested, I can make a separate post on how to structure answers for these questions—just let me know in the comments! 💬 4. Mock Interviews 🎤 I didn’t take any mock interviews because I’ve always been comfortable talking through my thoughts during coding and system design. Being extroverted, I don’t tend to get nervous in interviews. However, if you’re someone who struggles with nerves or articulating your thoughts clearly, mock interviews can be very helpful. Try asking a friend to conduct one with you. FAQs ❓ 1. Will this strategy work for me? It depends on your strengths and weaknesses, but try to balance your preparation accordingly. ⚖️ 2. Are mock interviews necessary? They can be helpful, but it depends on your personal preferences. I didn't take them, but if you're nervous or want more practice, mock interviews can be great. 👍 3. LLD Resources? Here’s a link to some useful resources. https://lnkd.in/gt_SraQ4 📖 4. Do I need Leetcode Premium? Not mandatory. If you can afford it, go for it, but remember that quality over quantity is key! 🔑 It's unlikely that you'll get the exact same Leetcode question in your interview—interviewers will typically modify them. So, focus on understanding the underlying patterns rather than memorizing solutions. This will help you adapt to any problem you encounter. 🧠💡 In conclusion, while everyone’s strategy may differ, the key is focus, consistency, and practicing with a purpose. Good luck to everyone preparing for their interviews! 💪 #Microsoft #Leetcode #SystemDesign #CodingInterviews #JobPrep #SoftwareEngineering #InterviewPrep #MicrosoftInterviews #LLD

#Keep #it #on #your #timeline When preparing for algorithm interview questions, especially in technical roles like QA Automation or SDET, it's important to demonstrate strong problem-solving abilities, logical thinking, and a solid grasp of data structures. Algorithm questions typically test your understanding of concepts such as arrays, strings, linked lists, stacks, queues, hash maps, recursion, sorting, and searching. These problems are designed to evaluate how you approach challenges, optimize solutions, and write clean, efficient code. In preparation, I focused on solving common problems on platforms like LeetCode, HackerRank, and GeeksforGeeks, covering topics like string reversal, finding duplicates, binary search, and sorting algorithms. For example, I practiced problems like "find the first non-repeating character," "reverse a linked list," and "merge two sorted arrays." I also strengthened my ability to analyze time and space complexity using Big-O notation, which is crucial when discussing performance during interviews. In real-world testing automation, these skills help optimize test logic, validate data transformation, and build more reliable test frameworks. By consistently practicing and writing code in Java or Python, I’ve improved my confidence in solving algorithm questions and explaining my approach clearly during interviews.