Tech Interview Preparation

Git Mastery: From Novice to Expert - A Technical Deep Dive and Roadmap Roadmap to Git Mastery: 1. Fundamentals    - Basic commands: init, add, commit, push, pull    - Understanding the working directory, staging area, and repository    - Branching and merging basics 2. Intermediate Concepts    - Rebasing vs. Merging    - Interactive rebasing    - Cherry-picking    - Reflog for recovery    - Stashing changes 3. Advanced Git    - Submodules and subtrees    - Git hooks    - Custom Git commands    - Git internals: objects, refs, and packfiles    - Git attributes and smudge/clean filters 4. Collaboration and Workflow    - Pull requests and code review processes    - Git flow vs. GitHub flow vs. GitLab flow    - Handling merge conflicts efficiently    - Conventional commits and semantic versioning 5. Git at Scale    - Monorepos vs. multi-repo strategies    - Git LFS for large files    - Shallow clones and partial clones    - Optimizing Git performance for large repositories Advanced Git Commands and Techniques: 1. Bisecting: 𝚐𝚒𝚝 𝚋𝚒𝚜𝚎𝚌𝚝 𝚜𝚝𝚊𝚛𝚝 <𝚋𝚊𝚍> <𝚐𝚘𝚘𝚍>    Use binary search to find the commit that introduced a bug. 2. Debugging with Git:    𝚐𝚒𝚝 𝚋𝚕𝚊𝚖𝚎 -𝙻 <𝚜𝚝𝚊𝚛𝚝>,<𝚎𝚗𝚍> <𝚏𝚒𝚕𝚎>    𝚐𝚒𝚝 𝚕𝚘𝚐 -𝚂 <𝚜𝚝𝚛𝚒𝚗𝚐> --𝚙𝚊𝚝𝚌𝚑 3. Rewriting history:    𝚐𝚒𝚝 𝚛𝚎𝚋𝚊𝚜𝚎 -𝚒 𝙷𝙴𝙰𝙳~<𝚗>    𝚐𝚒𝚝 𝚌𝚘𝚖𝚖𝚒𝚝 --𝚊𝚖𝚎𝚗𝚍    𝚐𝚒𝚝 𝚏𝚒𝚕𝚝𝚎𝚛-𝚋𝚛𝚊𝚗𝚌𝚑 4. Reflog for recovery:    𝚐𝚒𝚝 𝚛𝚎𝚏𝚕𝚘𝚐 𝚜𝚑𝚘𝚠 <𝚋𝚛𝚊𝚗𝚌𝚑>    𝚐𝚒𝚝 𝚛𝚎𝚜𝚎𝚝 --𝚑𝚊𝚛𝚍 <𝚋𝚛𝚊𝚗𝚌𝚑>@{<𝚗>} 5. Advanced merging:    𝚐𝚒𝚝 𝚖𝚎𝚛𝚐𝚎 --𝚜𝚚𝚞𝚊𝚜𝚑 <𝚋𝚛𝚊𝚗𝚌𝚑>    𝚐𝚒𝚝 𝚖𝚎𝚛𝚐𝚎 -𝚇 𝚒𝚐𝚗𝚘𝚛𝚎-𝚜𝚙𝚊𝚌𝚎-𝚌𝚑𝚊𝚗𝚐𝚎 <𝚋𝚛𝚊𝚗𝚌𝚑> 6. Submodules:    𝚐𝚒𝚝 𝚜𝚞𝚋𝚖𝚘𝚍𝚞𝚕𝚎 𝚊𝚍𝚍 <𝚛𝚎𝚙𝚘_𝚞𝚛𝚕>    𝚐𝚒𝚝 𝚜𝚞𝚋𝚖𝚘𝚍𝚞𝚕𝚎 𝚞𝚙𝚍𝚊𝚝𝚎 --𝚒𝚗𝚒𝚝 --𝚛𝚎𝚌𝚞𝚛𝚜𝚒𝚟𝚎 7. Git hooks:    Customize in .𝚐𝚒𝚝/𝚑𝚘𝚘𝚔𝚜/    Example: pre-commit for linting 8. Plumbing commands:    𝚐𝚒𝚝 𝚌𝚊𝚝-𝚏𝚒𝚕𝚎 -𝚙 <𝚘𝚋𝚓𝚎𝚌𝚝>    𝚐𝚒𝚝 𝚕𝚜-𝚝𝚛𝚎𝚎 <𝚝𝚛𝚎𝚎-𝚒𝚜𝚑>    𝚐𝚒𝚝 𝚛𝚎𝚟-𝚙𝚊𝚛𝚜𝚎 <𝚛𝚎𝚏> Deep Dive: Git Internals Understanding Git's object model is crucial: - Blobs: File contents - Trees: Directory structures - Commits: Snapshots with metadata - Tags: Named references to commits Core Concepts: 1. Distributed Version Control System (DVCS) 2. Snapshots, not differences 3. Nearly every operation is local 4. Git has integrity (SHA-1 hash) 5. Git generally only adds data Pro Tips: 1. Use 𝚐𝚒𝚝 𝚌𝚘𝚗𝚏𝚒𝚐 --𝚐𝚕𝚘𝚋𝚊𝚕 𝚊𝚕𝚒𝚊𝚜.<𝚊𝚕𝚒𝚊𝚜-𝚗𝚊𝚖𝚎> '<𝚐𝚒𝚝-𝚌𝚘𝚖𝚖𝚊𝚗𝚍>' for custom shortcuts. 2. Leverage 𝚐𝚒𝚝 𝚠𝚘𝚛𝚔𝚝𝚛𝚎𝚎 for managing multiple working copies. 3. Implement a commit message convention (e.g., Conventional Commits) for clarity and automation. 4. Use 𝚐𝚒𝚝 𝚏𝚎𝚝𝚌𝚑 --𝚊𝚕𝚕 --𝚙𝚛𝚞𝚗𝚎 regularly to keep your local references clean. 5. Master 𝚐𝚒𝚝 𝚛𝚎𝚋𝚊𝚜𝚎 --𝚘𝚗𝚝𝚘 for complex branch management. What's your biggest Git challenge?

I was a Data Analyst at Amazon for 2 years. If I were to start my Analytics career again, I would study for these 5 interviews. 1️⃣ 𝗦𝗤𝗟 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 I would get comfortable with the basics of SQL. Then move to advanced concepts (e.g window functions, optimization techniques). → 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: The 𝘥𝘢𝘪𝘭𝘺_𝘴𝘢𝘭𝘦𝘴 table has the columns 𝘥𝘢𝘵𝘦 and 𝘥𝘢𝘪𝘭𝘺_𝘴𝘢𝘭𝘦𝘴. How would you calculate 7-day moving average of daily sales? → 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: Write a SQL query to find the top 5 customers by total purchase amount. The 𝘰𝘳𝘥𝘦𝘳𝘴 table has columns 𝘰𝘳𝘥𝘦𝘳_𝘪𝘥, 𝘤𝘶𝘴𝘵𝘰𝘮𝘦𝘳_𝘪𝘥, 𝘱𝘶𝘳𝘤𝘩𝘢𝘴𝘦_𝘢𝘮𝘰𝘶𝘯𝘵, 𝘥𝘢𝘵𝘦. 2️⃣ 𝗗𝗮𝘁𝗮 𝘃𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 I would study data viz best practices and put that into a cheat sheet. If I got stuck in an interview, I would reference my cheat sheet. → 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: You have a dataset of customer demographics and purchase history. How would you visualize customer segmentation? → 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: You have daily data on a social media platform's user engagement. How would you visualize trends and patterns in user activity? 3️⃣ 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝗮𝗹 𝗰𝗮𝘀𝗲 𝘀𝘁𝘂𝗱𝗶𝗲𝘀 Learn analytics techniques and how to apply them to business problems. I would aim to do hands-on projects at my current job. → 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: A e-commerce company is experiencing a decline in customer retention. How would you analyze this problem? → 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: Our company's website traffic has plateaued. How would you investigate the causes and suggest improvements? 4️⃣ 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗰𝗮𝘀𝗲 𝘀𝘁𝘂𝗱𝗶𝗲𝘀 I would learn about how businesses use data to craft strategy. Then build projects to apply those learnings. → 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: A coffee chain wants to increase revenue. Can you suggest strategies to do so? → 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: Estimate the market size for electric scooters in New York City. 5️⃣ 𝗕𝗲𝗵𝗮𝘃𝗶𝗼𝗿𝗮𝗹 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 I would practice telling 5-10 stories using the STAR method. → 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: Describe a time when you had to explain complex data to non-technical stakeholders. → 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: Tell me about a time when you faced a challenge with data quality. How did you handle it? ——— PS: Level-up your SQL skills on Interview Master. We have 200+ SQL, Python & Case interview questions. Questions about Amazon, Google, Meta 𝘢𝘯𝘥 𝘮𝘰𝘳𝘦! ➡️ www.InterviewMaster.ai ——— ♻️ Repost if you found this useful please :)

𝗜 𝗼𝗳𝘁𝗲𝗻 𝗴𝗲𝘁 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀 𝗳𝗿𝗼𝗺 𝗳𝗶𝗻𝗮𝗻𝗰𝗲 𝘀𝘁𝘂𝗱𝗲𝗻𝘁𝘀 𝗹𝗼𝗼𝗸𝗶𝗻𝗴 𝘁𝗼 𝗰𝗿𝗮𝗰𝗸 𝗶𝗻𝘁𝗼 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗿𝗼𝗹𝗲𝘀 - “Ma’am, What kind of questions should I expect during the interview?” 𝗦𝗼, 𝗜𝗳 𝘆𝗼𝘂'𝗿𝗲 𝗽𝗿𝗲𝗽𝗮𝗿𝗶𝗻𝗴 𝗳𝗼𝗿 𝗮𝗻 𝗮𝗻𝗮𝗹𝘆𝘀𝘁 𝗿𝗼𝗹𝗲 𝗮𝗻𝗱 𝗷𝘂𝘀𝘁 𝘀𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗰𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝗳𝗶𝗻𝗮𝗻𝗰𝗲, 𝘁𝗵𝗲𝘀𝗲 𝗮𝗿𝗲 𝘀𝗼𝗺𝗲 𝗸𝗲𝘆 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗵𝗲𝗹𝗽 𝘆𝗼𝘂 𝗽𝗿𝗲𝗽𝗮𝗿𝗲. 1. Walk me through the three financial statements. Understand the Income Statement, Balance Sheet, and Cash Flow Statement and how they connect. 2. What are the key financial ratios you would use to analyze a company? Profitability, liquidity, and leverage ratios are key indicators. 3. How would you value a company? Brush up on DCF, comparable company analysis, and precedent transactions. 4. What is EBITDA, and why is it important? It's more than just a number—understand how it reflects operating performance. 5. What is working capital, and how do you calculate it? Knowing this can highlight your grasp on liquidity and operational efficiency. 6. What’s the difference between equity value and enterprise value? This is a must-know for anyone entering corporate finance or investment banking. 7. What is the Discounted Cash Flow (DCF) method, and how do you calculate free cash flow? Understanding this is essential for company valuations. 8. What is the WACC and why is it important? Know the weighted average cost of capital, it’s critical for discounting future cash flows. ✅ 𝗣𝗿𝗼 𝗧𝗶𝗽: Interviewers are not just testing your technical skills but also how you think and communicate. Be sure to explain concepts clearly, showing that you understand both the details and the bigger picture. ✅ 𝗙𝗶𝗻𝗮𝗹 𝗧𝗶𝗽: Don't hesitate to ask clarifying questions during the interview. It's a sign that you're engaged and eager to understand the problem at hand. Best of luck! #finance #hiring #interview #freshers

I can tell in 5 minutes during an interview if someone understands data (based on how they describe past projects they worked on) Most data candidates that I've interviewed focus on the wrong details entirely during this interview stage and this is definitvely and area that is easy to prepare for. They spend 10 minutes walking through their tech stack: "We used Snowflake with dbt, Fivetran for ingestion, and Airflow for orchestration." But they never explain why those choices mattered, what trade-offs they considered, or what business problem they actually solved. The strongest candidates I interview do the opposite. They start with the business context: "Our sales team was making decisions based on 3-day-old data because our reporting pipeline took 72 hours to run." Then they explain their approach: "We had to balance speed versus cost, so we implemented incremental loading but kept full refreshes for critical accuracy checks." What I'm actually listening for: Do you understand the business impact of your technical decisions? Can you articulate trade-offs you made and why? Do you think about data quality, reliability, and maintainability, not just getting it to work? Can you explain complex systems in simple terms? The candidates who get hired don't have the most impressive projects - they have the clearest thinking about data problems. They talk about monitoring, error handling, and edge cases. They mention stakeholder feedback and iteration cycles. They understand that great data engineering is invisible to end users. Next time you're asked about a past project, spend less time on the tech stack and more time on the problems you solved, decisions you made, and lessons you learned. That's what reveals your actual engineering judgment. What's your experience with this? Share in the comments, follow for more insights on data engineering careers, and ♻️ repost if your network could benefit! #DataEngineering #Interviews #CareerAdvice #TechCareers #JobSearch

You cannot master modern DevOps without understanding the difference between traditional CI/CD and GitOps CI/CD. Whether you're a Cloud Engineer, DevOps Engineer, SRE, or getting started with Kubernetes, here’s a step-by-step DevOps Deployment workflow you should know: Common steps in both the pipelines: Source Code Commit ↳ Developer writes and commits code to a Git repository. Unit Tests: ↳ Automated tests run to validate code functionality and quality. Build Artifacts: ↳ Code is compiled and packaged into deployable artifacts. Build Image: ↳ Docker images are built from artifacts for containerized deployment. Push Image to Registry: ↳Built images are pushed to container registries like ECR, Azure Artifact, or GCR. Key Distinction in Deployment: → DevOps CI/CD: Direct push of deployments from CI pipeline to the cluster (EKS, AKS, GKE). → GitOps CI/CD: Git is the single source of truth — the cluster pulls the desired state from Git, enabling drift detection and automated reconciliation; and then deploys. How does GitOps do that? → Git stores deployment manifests with the desired container image tags or digests. → The GitOps operator (ArgoCD / FluxCD) watches the Git repo for changes. → It syncs with the container registry to fetch the exact image version specified in Git. → The operator deploys the image to the cluster, keeping it aligned with Git. → If there’s a drift, the operator reconciles to match the Git-defined state. Beyond Push vs Pull — there are other benefits of GitOps too, like every deployment change is versioned; automatic rollbacks, easier to manage microservices etc. Hope this helped in getting some clarity on modern DevOps pipelines; also — understanding these workflows is a common discussion point in cloud and devops interviews these days, so make sure you can explain both. If you found this useful: • • • I regularly share bite-sized insights on Cloud & DevOps (through my newsletter as well) — if you're finding them helpful, hit follow (Vishakha) and feel free to share it so others can learn too!

Today, I witnessed my team lead interviewing a Full-Stack Web Developer with 2 years of experience, and I observed some interesting insights. The interview started with basic technical questions, which the candidate answered well. Then, my team lead gave him a coding task: 👉 "Write a function that removes duplicate values from a nested array while preserving its structure (PHP)." The candidate struggled and couldn’t solve it. So, my lead gave him another task: 👉 "Write a function that groups an array of associative arrays based on a specific key." Again, he couldn’t solve it. At this point, he admitted: "I don’t always have syntax and logic rules memorized, as I usually rely on resources like Stack Overflow and ChatGPT. Could you assign me a real-world task instead?" Here,I decided to step in. Since I was working on WooCommerce project, I gave him this challenge: 💡 "Implement a functionality where a logged-in user gets a 15% discount if they place two orders in a single day." We allowed him to use Google, ChatGPT, or any other resources. To our surprise, within 1.5 hour, he understood WooCommerce functionality, analyzed the existing code, and successfully implemented the feature. ✨ We ended up hiring him! ✨ Key Takeaway Many traditional interview techniques overemphasize logical puzzles and syntax-heavy questions, yet modern developers approach problem-solving differently. Rather than memorizing solutions, they adapt, learn, and implement on the spot using the tools and resources available to them. 💡 Instead of testing how well candidates remember syntax, we should focus on how effectively they apply problem-solving skills to practical challenges. What’s your take on this? Let’s discuss in the comments! 👇 #HiringTechTalent #TechCommunity #WhatDoYouThink

In the last 15 years, I have interviewed 800+ Software Engineers across Google, Paytm, Amazon & various startups. Here are the most actionable tips I can give you on how to approach  solving coding problems in Interviews  (My DMs are always flooded with this particular question) 1. Use a Heap for K Elements      - When finding the top K largest or smallest elements, heaps are your best tool.      - They efficiently handle priority-based problems with O(log K) operations.      - Example: Find the 3 largest numbers in an array.   2. Binary Search or Two Pointers for Sorted Inputs      - Sorted arrays often point to Binary Search or Two Pointer techniques.      - These methods drastically reduce time complexity to O(log n) or O(n).      - Example: Find two numbers in a sorted array that add up to a target.   3. Backtracking    - Use Backtracking to explore all combinations or permutations.      - They’re great for generating subsets or solving puzzles.      - Example: Generate all possible subsets of a given set.   4. BFS or DFS for Trees and Graphs      - Trees and graphs are often solved using BFS for shortest paths or DFS for traversals.      - BFS is best for level-order traversal, while DFS is useful for exploring paths.      - Example: Find the shortest path in a graph.   5. Convert Recursion to Iteration with a Stack      - Recursive algorithms can be converted to iterative ones using a stack.      - This approach provides more control over memory and avoids stack overflow.      - Example: Iterative in-order traversal of a binary tree.   6. Optimize Arrays with HashMaps or Sorting      - Replace nested loops with HashMaps for O(n) solutions or sorting for O(n log n).      - HashMaps are perfect for lookups, while sorting simplifies comparisons.      - Example: Find duplicates in an array.   7. Use Dynamic Programming for Optimization Problems      - DP breaks problems into smaller overlapping sub-problems for optimization.      - It's often used for maximization, minimization, or counting paths.      - Example: Solve the 0/1 knapsack problem.   8. HashMap or Trie for Common Substrings      - Use HashMaps or Tries for substring searches and prefix matching.      - They efficiently handle string patterns and reduce redundant checks.      - Example: Find the longest common prefix among multiple strings.   9. Trie for String Search and Manipulation      - Tries store strings in a tree-like structure, enabling fast lookups.      - They’re ideal for autocomplete or spell-check features.      - Example: Implement an autocomplete system.   10. Fast and Slow Pointers for Linked Lists      - Use two pointers moving at different speeds to detect cycles or find midpoints.      - This approach avoids extra memory usage and works in O(n) time.      - Example: Detect if a linked list has a loop.   💡 Save this for your next interview prep!

After taking 75 Software Engineer interviews at Google in < 7 months, I’ve seen a range of mistakes all of us make in coding interviews. Here’s a compiled list to help you (and me) avoid these pitfalls in our future interviews! 1️⃣ Not Clarifying Requirements > Many candidates jump straight into coding. Often without fully understanding the problem. This can waste time and lead to errors. Tip: Always ask clarifying questions. To ensure you get the requirements. Confirm edge cases and input constraints early on. 2️⃣ Overcomplicating Solutions > In the heat of the moment, it is easy to overthink a problem. And this complicates the solution, both for you and your interviewer. Tip: Start with a brute-force approach (just explain it), then iterate towards optimization (code it up). Easy-to-understand solutions get bonus points. 3️⃣ Under-Communication > Interviews are not just about coding. They’re also about conveying your thought process. Silence takes away the only help you have during the interview—your interviewer. Tip: Think out loud! Explain your reasoning and approach as you code. This helps the interviewers understand you and even guide you if needed. 4️⃣ Ignoring Edge Cases > Many candidates create a working solution. But fail to consider edge cases. This can lead to catastrophic failures. Tip: After arriving at a solution, always discuss potential edge cases. Explain how your code handles them. This shows your thoroughness. 5️⃣ Neglecting to Optimize > Even if your solution works, failing to consider optimization can cost you points. Tip: After solving the problem, re-read your solution and discuss ways to improve time and space complexity. No micro-optimizations. Interviewers appreciate candidates who think about efficiency in big-oh notation. 6️⃣ Skipping Dry Runs > 80%+ candidates skip the dry run of their code, leading to overlooked mistakes. Tip: Walk through your code with sample inputs. This helps catch errors early and makes you look proactive. 7️⃣ Getting Flustered > Interviews are stressful. And it is easy to panic if you hit a roadblock. Tip: If you’re stuck, ask for a minute or 2 to gather your thoughts. Ask for hints if necessary—interviewers appreciate candidates who are willing to seek help. Those were my 2 cents on how to tackle coding interviews. But believe it or not, the best way to realize your interview mistakes would be to start taking interviews (even mock ones). After conducting so many interviews at Google, I realized how I often fell into the same traps as everyone. Like going completely silent or forgetting to do a dry run for the interviewer. Taking interviews altered my perspective, and now I advise everyone preparing for interviews to take a couple of them first. Total game changer! #codingInterviews #jobPrep #softwareEngineering #Google #interviewTips

🔥 After 17+ years in Salesforce development, I still keep reference guides close — because no one memorizes everything, and that's okay. Sharing my LWC Cheat Sheet — a no-fluff quick reference for Lightning Web Component concepts I find myself revisiting regularly. ⚡ What's inside: ✅ LWC Fundamentals ✅ @wire vs Imperative Apex — when to use which ✅ Lifecycle Hooks (and why they actually matter) ✅ Component Communication patterns ✅ Pagination & Debouncing ✅ Error Handling done right ✅ Performance Best Practices ✅ Common interview topics (yes, they still ask about connectedCallback) Built this for fast revision, refresher and those moments mid-sprint when you just need a quick answer. #Salesforce #LWC #LightningWebComponents #SalesforceDeveloper #Apex #SalesforceArchitect #CRM #WebDevelopment #Coding #SalesforceAdmin #Developer #Tech #AI #Tech #SoftwareEngineering #Learning #Trailblazer

You must use the REACT framework in Coding Interviews. It does not matter whether you're a front-end developer or not. Because I'm not talking about the JavaScript library here. Here is what REACT means: R stands for Repeat. Always repeat the question back to the interviewer. This simple step prevents your brain from jumping to conclusions and ensures you fully understand the problem. E is for Examples. Ask for specific input and output examples. Many candidates skip this important step. But verbalizing these examples can uncover hidden requirements. A means Approach. Explain your solution strategy before writing a single line of code. This shows your problem-solving skills and gives the interviewer a chance to guide you if needed. C stands for Code. Only start coding after you've completed the previous steps. This organized approach will make your implementation smoother and more efficient. T is for Test. Always test your code against the examples you discussed earlier. This shows attention to detail and can catch errors you might have missed. Good luck! 👍