DSA Mock Interview & Coding Interview Practice

Preparing for a coding interview means mastering Data Structures and Algorithms — the cornerstone of technical hiring at top tech companies. InstaMock gives you access to 200+ carefully curated DSA problems spanning every topic you will encounter in a real interview: Arrays, Strings, Linked Lists, Stacks, Queues, Trees, Graphs, Heaps, Dynamic Programming, Greedy algorithms, Backtracking, Bit Manipulation, and more.

What sets InstaMock apart is its AI-powered evaluation. After you submit a solution, the AI analyzes your code for correctness, time complexity, space complexity, and coding style — then delivers instant, actionable feedback the same way a senior engineer would in a live interview.

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.

DSA Problem Categories

Arrays & Strings — Foundation Problems

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.

Linked Lists — Sequential Data Structures

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.

Stacks & Queues — LIFO & FIFO Structures

Implement stacks and queues, solve parentheses matching, sliding window maximum, and monotonic stack problems. Essential for understanding asynchronous operations and event handling.

Trees — Hierarchical Data Structures

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.

Graphs — Complex Relationships

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.

Dynamic Programming — Optimization Technique

Master DP patterns: memoization, tabulation, sequence problems (LCS, LIS, Edit Distance), knapsack problems, and matrix multiplication. Acing DP demonstrates strong problem-solving skills.

Bit Manipulation — Low-level Operations

Learn bitwise operations, bit masks, and problems like single number, power of two, and bit counting. Common in systems programming and optimization-focused interviews.

Backtracking — Exhaustive Search

Solve permutations, combinations, N-Queens, Sudoku solver, and subset generation. Tests your ability to explore solution spaces and prune inefficient branches.

Greedy Algorithms — Optimal Choices

Master greedy strategies: activity selection, huffman coding, fractional knapsack, and interval scheduling. Understand when greedy approach yields optimal solutions versus requiring dynamic programming.

Binary Search — Efficient Searching

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).

Hashing & Hash Tables — Fast Lookups

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.

Sorting & Searching — Fundamental Algorithms

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.

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