Free DSA Mock Tests

Let us be honest about the limits of this format first, because a page that oversells its own tool is not much use to you. You cannot learn to write code by answering multiple-choice questions. Implementation ability — getting the boundary conditions right, handling the empty input, not going one past the end of the array — comes only from writing the code, compiling it, watching it fail, and fixing it.

What multiple-choice questions are genuinely good for is the layer underneath implementation: recognising which structure or pattern a problem calls for, and knowing the cost of each operation without having to derive it. That recognition is what separates a candidate who solves an interview problem in twenty minutes from one who solves it in fifty, and it is exactly the kind of knowledge that is fast to test and fast to correct.

So use these tests as a diagnostic and a memory check alongside actual coding practice, not instead of it. If a question about the worst case of quicksort makes you hesitate, that hesitation would have cost you real time in an interview.

Topics covered

• Complexity analysis. Big-O, big-theta and big-omega; best, average and worst case; amortised cost; space complexity; and the recurrence relations behind divide-and-conquer algorithms.
• Arrays and strings. Two pointers, sliding window, prefix sums, in-place rearrangement, and the classic subarray problems.
• Linked lists. Singly, doubly and circular; reversal; cycle detection with Floyd's algorithm; merge and partition operations.
• Stacks and queues. Monotonic stacks, next-greater-element, expression evaluation, deques and circular buffers.
• Trees. Binary trees and binary search trees, traversal orders, height and balance, AVL and red–black rotations, heaps and priority queues, and tries.
• Graphs. Representations and their trade-offs, breadth-first and depth-first search, topological sort, Dijkstra, Bellman–Ford, Floyd–Warshall, and the minimum spanning tree algorithms.
• Hashing. Hash functions, collision resolution by chaining and open addressing, load factor, and why average-case constant time is not worst-case constant time.
• Sorting and searching. The comparison sorts and their stability and space characteristics, the linear-time counting sorts, binary search and its many variants on answer spaces.
• Recursion and dynamic programming. Memoisation versus tabulation, state definition, and the standard families: knapsack, longest common subsequence, longest increasing subsequence, matrix chain, and the coin-change problems.
• Greedy algorithms and backtracking. When a greedy choice is provably safe and when it quietly is not.

Complexities worth knowing without thinking

Interviewers ask for complexity almost reflexively, and hesitating on a well-known bound reads as shaky fundamentals even when your solution is correct. The following should be instant recall rather than something you derive at the whiteboard.

• Binary search on a sorted array: logarithmic time, constant space.
• Quicksort: linearithmic on average, quadratic in the worst case, and logarithmic stack space. It is not stable.
• Mergesort: linearithmic in all cases, linear extra space, and stable.
• Heapsort: linearithmic in all cases, constant space, not stable. Building a heap from an unsorted array is linear, not linearithmic — a favourite trick question.
• Hash table lookup: constant on average, linear in the worst case when everything collides.
• Balanced binary search tree operations: logarithmic. An unbalanced one degrades to linear, which is the whole reason AVL and red–black trees exist.
• Breadth-first and depth-first search: linear in vertices plus edges.
• Dijkstra with a binary heap: edges times the logarithm of vertices.

Recognising the pattern behind the problem

Most interview problems are variations on a small number of patterns, and the useful skill is mapping an unfamiliar statement onto a familiar one. A few of the mappings that recur most often:

• A contiguous subarray or substring satisfying some property almost always means a sliding window.
• A sorted array with a pair or triplet condition means two pointers.
• “The k largest” or “the k most frequent” means a heap of size k.
• A question asking for the number of ways, or the best over all choices, with overlapping subproblems, means dynamic programming.
• Anything about prerequisites, dependencies or ordering means topological sort.
• “The minimum value of x such that a condition holds”, where the condition is monotonic in x, means binary search on the answer — a pattern that catches out more candidates than any other.

Say the pattern out loud in an interview. Naming the approach before you start coding shows the interviewer you recognised the problem class rather than stumbling into a solution, and it gives them a chance to redirect you before you have written thirty lines in the wrong direction.