Common Data Structure Questions

Hard Truth: Data Structures - The Unavoidable Interview Reality Here's a pattern I've noticed recently that every software professional needs to hear: Even if you haven't used a binary tree in years, you WILL face data structure questions in your next interview. Here's why this matters: The Interview Reality Check: 1. FAANG-level companies:    - Will absolutely grill you on data structures    - Expect implementation from scratch    - Want optimal solutions 2. Startups:    - May seem more relaxed    - Still include DS questions in their process    - Use them to evaluate problem-solving 3. Even Frontend Roles:    - React state management? That's a tree    - Event handling? Welcome to queues    - Browser history? That's a stack What I've Observed: - Brilliant developers failing interviews because they're rusty on basics - Senior engineers stumbling on LinkedList questions - Tech leads getting rejected for missing optimal solutions The Smart Approach: 1. Keep a "DS Emergency Kit":    - Arrays & String manipulation    - Hash Tables implementations    - Tree traversals    - Graph basics    - Stack & Queue operations 2. Monthly Refresh Routine:    - Solve one problem per structure    - Review time complexities    - Practice explaining your approach Common Mistakes: - Thinking "I don't use this at work, so I won't study it" - Starting interview prep too late - Focusing only on coding, ignoring theory Quick Tips: 1. LeetCode Medium is your friend 2. Always write clean code in interviews 3. Think aloud during problem-solving 4. Review basic implementations monthly Core Data Structures You MUST Know: 1. Arrays    - What: Continuous memory blocks    - Why: Foundation of most data operations    - Real use: Instagram's photo feed, Spotify's playlist management 2. Linked Lists    - What: Connected nodes with next/prev references    - Why: Dynamic memory allocation    - Real use: Undo/Redo functionality in text editors 3. Hash Tables    - What: Key-value pair storage    - Why: Lightning-fast O(1) lookups    - Real use: Database indexing, caching systems 4. Stacks (LIFO)    - What: Last-In-First-Out structure    - Why: Track execution context    - Real use: Browser history, Function call management 5. Queues (FIFO)    - What: First-In-First-Out structure    - Why: Order preservation    - Real use: Print spoolers, Message queues in distributed systems 6. Trees    - What: Hierarchical data structure    - Why: Organized data relationships    - Real use: File systems, DOM in web browsers 7. Graphs    - What: Nodes connected by edges    - Why: Complex relationship mapping    - Real use: Social networks, Google Maps, Netflix recommendations 1. Practice implementing from scratch 2. Study time complexity for each operation 3. Learn when to use which structure Action Items: 1. Pick one structure weekly 2. Implement it in your preferred language 3. Solve 2-3 related problems 4. Document real-world applications

Google Maps is a use case of Graphs. Autocomplete is a use case of Tries. Your browser’s Back button? That’s a Stack. Printing jobs queued up? That’s a Queue. Tracking unique visitors on a site? That’s a Set. Last week, I spoke to 6 fellow engineers working at Atlassian, Microsoft, Amazon, and Oracle. I asked them one simple question: I asked them: "If a student wants to prep seriously for interviews, what data structures must they master?" Here’s what they 👇 → Arrays & Strings Why: 70% of entry-level questions start here. Use case: Storing leaderboard scores, search results, text processing. Learn: Sliding window, prefix sums, two pointers. → Stacks & Queues Why: You need these for problems that involve order, backtracking, or recent state. Use case: Undo feature in editors (Stack), call center systems (Queue). Learn: Infix to postfix, balanced brackets, task scheduling. → HashMaps & Sets Why: Constant-time lookups. Essential for optimizing brute force solutions. Use case: Caching, de-duplication, frequency counters. Learn: Two sum, anagram groups, longest consecutive sequence. → Linked Lists Why: Pointer manipulation is a common test of your understanding of memory. Use case: Building LRU caches, managing memory buffers. Learn: Reverse a list, detect cycle, merge two lists. → Trees & BSTs Why: Teaches recursion + hierarchical data handling. Use case: File systems, compiler syntax trees, databases. Learn: Traversals (BFS, DFS), Lowest Common Ancestor, BST insertion/search. → Tries Why: Underused but powerful for text/prefix-based problems. Use case: Autocomplete systems, spell checkers, search engines. Learn: Insert/search/delete in Trie, word search. → Graphs Why: Real-world system modeling. Every advanced interview eventually hits this. Use case: Routing systems, service dependencies, friend suggestions. Learn: BFS/DFS, Dijkstra’s, cycle detection, topological sort. → Heaps (Priority Queues) Why: Efficient for sorting and finding the top/bottom 'k' items. Use case: Job schedulers, recommendation engines. Learn: Kth largest element, merge K sorted lists, sliding window max. DSA i╪ memorizing code. It’s about learning how to think. Every question is just a pattern + a use case. So before you jump into random Leetcode grinds, → Study these in depth → Practice deliberately → Understand the why behind every structure Only then do you build real skill. Not just memorized solutions. -- P.S: If you’re currently preparing for DSA, HLD, and LLD. Check out my one-stop resource guide on Topmate: → https://lnkd.in/eYHSjbys ( 700+ students are already using it) This guide will help you with: - DSA, HLD, and LLD for interviews - good resources that I used personally - lots of problems and case studies for DSA and system design

Last week, I had conversations with 8 engineers from Atlassian, Microsoft, Amazon, and Oracle. I asked them a simple question: If someone wants to crack top tech interviews, which data structures are non-negotiable? Here’s what came up, again and again: → Arrays & Strings Why: 70% of entry-level questions start here. Use case: Storing leaderboard scores, search results, text processing. Learn: Sliding window, prefix sums, two pointers. → Stacks & Queues Why: You need these for problems that involve order, backtracking, or recent state. Use case: Undo feature in editors (Stack), call center systems (Queue). Learn: Infix to postfix, balanced brackets, task scheduling. → HashMaps & Sets Why: Constant-time lookups. Essential for optimizing brute force solutions. Use case: Caching, de-duplication, frequency counters. Learn: Two sum, anagram groups, longest consecutive sequence. → Linked Lists Why: Pointer manipulation is a common test of your understanding of memory. Use case: Building LRU caches, managing memory buffers. Learn: Reverse a list, detect cycle, merge two lists. → Trees & BSTs Why: Teaches recursion + hierarchical data handling. Use case: File systems, compiler syntax trees, databases. Learn: Traversals (BFS, DFS), Lowest Common Ancestor, BST insertion/search. → Tries Why: Underused but powerful for text/prefix-based problems. Use case: Autocomplete systems, spell checkers, search engines. Learn: Insert/search/delete in Trie, word search. → Graphs Why: Real-world system modeling. Every advanced interview eventually hits this. Use case: Routing systems, service dependencies, friend suggestions. Learn: BFS/DFS, Dijkstra’s, cycle detection, topological sort. → Heaps (Priority Queues) Why: Efficient for sorting and finding the top/bottom 'k' items. Use case: Job schedulers, recommendation engines. Learn: Kth largest element, merge K sorted lists, sliding window max. DSA i╪ memorizing code. It’s about learning how to think. Every question is just a pattern + a use case. So before you jump into random Leetcode grinds, → Study these in depth → Practice deliberately → Understand the why behind every structure Only then do you build real skill. Not just memorized solutions. You need the right patterns, in the right order, with the right focus. That’s what I’ve built for you: ✅ Notes that are concise and clear ✅ Most-asked questions per topic ✅ Real patterns + approaches to master them 👉 Grab the DSA Guide → https://lnkd.in/d8fbNtNv 𝐅𝐨𝐫 𝐌𝐨𝐫𝐞 𝐃𝐞𝐯 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐉𝐨𝐢𝐧 𝐌𝐲 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 : Telegram - https://lnkd.in/d_PjD86B Whatsapp - https://lnkd.in/dvk8prj5 Built for devs who want to crack interviews — not just solve problems.

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

My Adobe Interview Experience (Part - 1/2) 𝐐𝟏. 𝐖𝐡𝐚𝐭 𝐰𝐚𝐬 𝐭𝐡𝐞 𝐡𝐢𝐫𝐢𝐧𝐠 𝐩𝐫𝐨𝐜𝐞𝐬𝐬? 𝐇𝐨𝐰 𝐦𝐚𝐧𝐲 𝐫𝐨𝐮𝐧𝐝𝐬 𝐰𝐞𝐫𝐞 𝐭𝐡𝐞𝐫𝐞? Adobe visited our campus(IGDTUW) for its annual on-campus pool drive in October Branches allowed to sit for the assessment: BTech- CSE/IT/ECE and MTech- VLSI/ISM/MPC Profile: Software Development Engineer Job Location: Noida/Bangalore Eligibility Criteria: PG candidates: 70% in post-graduation & graduation, 60% in 12th & 10th UG candidates: 70% in graduation, 60% in 12th & 10th. No current backlogs. Platform: Cocubes Format: Aptitude(30 mins) + Coding(2 questions & 30 minutes) with webcam enabled. Total no. of rounds: 5( 1 online assessment + 3 technical + 1 HR) 𝐐𝟐. 𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐜𝐨𝐦𝐦𝐨𝐧 𝐝𝐚𝐭𝐚 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐬 𝐚𝐬𝐤𝐞𝐝 𝐝𝐮𝐫𝐢𝐧𝐠 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬? I was asked questions from Arrays, LinkedList, Hashmap, HashSet, Binary Trees, String Builder, Stack & Queues, and Graphs. You can expect questions based on any of the basic data structures that are taught in the “Data Structures & Algorithms” course or the “Analysis and Design of Algorithms” course. 𝐐𝟑. 𝐖𝐡𝐚𝐭 𝐰𝐞𝐫𝐞 𝐭𝐡𝐞 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐚𝐬𝐤𝐞𝐝 𝐢𝐧 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐫𝐨𝐮𝐧𝐝𝐬? 𝐢𝐟 𝐫𝐞𝐦𝐞𝐦𝐛𝐞𝐫 𝐚𝐧𝐲. 𝐃𝐒𝐀 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬 ✔️ Given a singly linked list, reverse every k-nodes. Clone a linked list next and a random pointer. (with and without using extra space) ✔️ Zig-Zag traversal of a binary tree ( 2-3 approaches) ✔️ Activity Selection problem ✔️ Given a binary tree, determine if it is BST or not ✔️ An array of size n has values 1-n with 1 element missing. The missing element is replaced by -1. Find the missing element. ✔️ The internal implementation of Hashmap. String immutability, string interning, and string matching concepts in Java. ✔️ Discussion on some practical problems like Adhaar card and Passport Sewa requiring to override hashcode(), hasnext(), and next() build-in Java functions. ✔️ Given an array, print all the pairs with the given sum. (2-3 approaches) 𝐎𝐒 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 ✔️ A thorough discussion on OS topics like memory management, process synchronization, thread synchronization, deadlocks, segmentation, paging techniques, scheduling algorithms, etc. 𝐃𝐁𝐌𝐒 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 ✔️ Several DBMS questions like various keys in DBMS, 1-2 SQL queries, normalization, etc. The interviewers required proper (C++/Java) code, dry run, and stack trace. All the best! ❤️ P.S. - That's me attending Adobe's Diwali Mela 😍 ------------- Book a 1:1 mentorship session with me - https://lnkd.in/dBat2pfB Twitter - https://lnkd.in/deHrAPcF Instagram - https://lnkd.in/dFmiPh4G #adobe #interviewexperience #interviewquestions #linkedincreators