Which DSA Questions Get Asked Most?

A small set of questions accounts for a large share of what gets asked. Arrays and hash maps dominate: two sum, group anagrams, longest substring without repeating characters, subarray sum equals k. Then linked lists: reverse a list, detect a cycle, merge two sorted lists, find the middle. Then trees and graphs: traversals, validate a search tree, lowest common ancestor, number of islands, course schedule.

from collections import Counter

recent = [
    "two sum", "two sum", "valid parentheses", "two sum",
    "reverse linked list", "valid parentheses", "binary search",
    "two sum", "merge intervals", "valid parentheses",
]


def most_common(items, n=3):
    return Counter(items).most_common(n)


print(most_common(recent))

The output is the three highest counts, two sum with four, valid parentheses with three and reverse linked list with one. The counts come from this sample, but the ranking reflects what interviewers really ask.

The mechanism is that those questions are asked because they are compact, they have a clear optimal solution, and they discriminate well between candidates. Two sum tests whether you reach for a hash map. Number of islands tests whether you can do a flood fill. Valid parentheses tests whether you know when a stack is the right structure.

The trade-off to name is the temptation to memorise solutions for the top ten and stop there. That works for one interview and fails for the next one, because the follow-up questions on a familiar problem are where the real skill is tested. A better allocation of time is to practise a smaller set deeply enough that you can solve variations, then cover the long tail by recognising the pattern rather than by memorising the code.

Separate the two halves of the question and treat them differently. The system design half is about scoping: clarify the requirements, estimate the scale, propose a high level architecture, and then dig into one component. The data structure half is where your preparation pays off, because once the interviewer picks a component such as a rate limiter, a feed, or a job queue, the question becomes a concrete algorithm problem.

That second half is where you should be ready with specific structures. A rate limiter wants a token bucket or a sliding window counter. A feed wants an ordered index with a fan out on write. A job queue wants a heap for priority and a durable log. A leaderboard wants a sorted structure with a rank lookup. Being able to name the structure and sketch its operation costs is exactly what is being tested at that point.

The mechanism is that the interviewer has already accepted the architecture and is now probing how you would implement one part of it properly. The complexity of the operations you propose is what they are checking, along with whether you understand the trade-offs, since a hash map is constant time but unordered while a tree is logarithmic and sorted.

The trade-off to name is the temptation to over-engineer the answer by describing every component in detail. A good candidate spends most of the time on one component, works it through carefully, and keeps the rest of the architecture at the level of a sketch. That shows judgement, and it is far more persuasive than a long tour of a system nobody needs at that scale.