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Preparing for a coding interview means mastering Data Structures and Algorithms — the cornerstone of technical hiring at top tech companies. InstaMock has 670 DSA problems spanning every topic you will meet in a real interview: Arrays, Strings, Linked Lists, Stacks, Queues, Trees, Graphs, Heaps, Dynamic Programming, Greedy algorithms, Backtracking, Bit Manipulation, and more.
Every problem runs your code for real. Write a solution in Java, Python or C++, run it against the sample cases, then submit it against the full test set and see exactly which cases pass and which fail — the same feedback loop as an online assessment.
Filter problems by topic or difficulty (Easy, Medium, Hard) to build a structured study plan. Work through foundational Arrays and Trees problems first, then progress to advanced Graph traversals and Dynamic Programming challenges.
Campus and SDE-1 interviews concentrate on the foundations. Get fast and accurate at these topics first: arrays, strings, hashing, two pointers, sliding window, linked lists, stacks and binary search.
SDE-2 and senior loops assume the foundations and spend their time on harder patterns: trees, graph traversal, shortest paths, topological sort, union find, tries, dynamic programming and advanced DP. Solve each in the language you will interview in, then explain the complexity out loud as you would to an interviewer.
Master arrays and string manipulation. Solve Two Sum, 3Sum, longest substring problems, sliding window algorithms, and array rotation. Essential fundamentals tested in every tech interview from Google to Amazon.
Learn linked list operations: reversal, cycle detection, merging, and manipulation. Common in backend and systems engineering roles. Practice detecting when to use linked lists over arrays.
Implement stacks and queues, solve parentheses matching, sliding window maximum, and monotonic stack problems. Essential for understanding asynchronous operations and event handling.
Master binary trees, BSTs, and advanced tree problems: traversals (in/pre/post-order), path sum, lowest common ancestor, and tree serialization. Most fundamental data structure in tech interviews.
Solve graph traversal (DFS/BFS), cycle detection, topological sorting, Dijkstra, and connected components. Critical for system design and backend engineering roles at top tech companies.
Master DP patterns: memoization, tabulation, sequence problems (LCS, LIS, Edit Distance), knapsack problems, and matrix multiplication. Acing DP demonstrates strong problem-solving skills.
Learn bitwise operations, bit masks, and problems like single number, power of two, and bit counting. Common in systems programming and optimization-focused interviews.
Solve permutations, combinations, N-Queens, Sudoku solver, and subset generation. Tests your ability to explore solution spaces and prune inefficient branches.
Master greedy strategies: activity selection, huffman coding, fractional knapsack, and interval scheduling. Understand when greedy approach yields optimal solutions versus requiring dynamic programming.
Master binary search variants: standard search, search in rotated array, find peak element, and find first/last position. Essential for optimizing brute force solutions to O(log n).
Use hash maps/sets for fast O(1) lookups. Solve duplicate detection, intersection problems, and frequency maps. Master collision handling and when to use hashing over sorting.
Implement and understand sorting algorithms (merge sort, quick sort, heap sort), sorting comparators, and custom sort orders. Master when O(n log n) vs O(n) is required.
Structured from beginner to advanced. Master fundamentals first, then build up level by level.
Understand what data structures and algorithms are and why they matter in interviews.
Learn Big O notation and analyze how algorithm runtime scales with input size.
Understand memory usage and how to trade space for time (and vice versa).
Practice analyzing time and space complexity of code snippets and algorithms.
Learn how to read, break down, and approach any coding problem systematically.
Master array traversal, in-place modification, prefix sums, and core array patterns.
Sliding window, two pointers, frequency maps, and pattern matching on character sequences.
Use hash maps and sets for O(1) lookups, frequency counting, and grouping.
Traverse, search, and transform 2D grids using DFS, BFS, and in-place tricks.
Use two indices moving toward each other or in the same direction to solve pair and partition problems.
Efficiently process subarrays or substrings by maintaining a window that expands and contracts.
Precompute cumulative sums to answer range queries in O(1) after O(n) preprocessing.
Efficiently search sorted arrays and binary search on the answer space.
Apply sorting as a first step to unlock two-pointer, binary search, and greedy patterns.
Merge, insert, and count overlapping intervals using sorting and greedy strategies.
Pointer manipulation, cycle detection, reversal, and merge operations.
LIFO structure for bracket matching, expression evaluation, and next-greater problems.
FIFO structures powering BFS, level order traversal, and sliding window maximum.
Maintain a stack or queue in sorted order to answer next-greater and sliding window max in O(n).
DFS, BFS, height, diameter, and path problems on binary trees.
Use BST properties for O(log n) search, validation, and ordered traversal.
Use min/max heaps for top-K problems, scheduling, and efficient repeated minimum extraction.
Prefix tree for O(L) string insert/search and autocomplete problems.
Explore all possibilities recursively and prune invalid branches early.
Nodes, edges, directed/undirected, weighted, and adjacency representations.
Visit every node using depth-first or breadth-first strategies.
Find shortest paths using BFS (unweighted), Dijkstra (weighted), and Bellman-Ford.
Order nodes in a DAG so all dependencies come before dependents.
Track connected components efficiently with path compression and union by rank.
Make locally optimal choices that lead to a globally optimal solution.
XOR, AND, OR, and shifts for O(1) space solutions to number problems.
Integer manipulation, modular arithmetic, and number-theoretic problem solving.
Understand overlapping subproblems, optimal substructure, memoization, and tabulation.
Fibonacci-style, house robber, climbing stairs, and linear DP patterns.
Navigate 2D grids with DP for unique paths, minimum path sum, and dungeon problems.
LCS, edit distance, palindrome DP, and substring matching using 2D tables.
Classic 0/1 knapsack, unbounded knapsack, and subset partition problems.
LIS, interval DP, bitmask DP, and tree DP patterns.
Segment trees, Fenwick trees, and tree DP for range queries and complex tree problems.
MST, strongly connected components, bridges, and advanced shortest path algorithms.
Interview Ready!
You've completed the full DSA path