DSA & Competitive Programming

Q: Is the DSA & Competitive Programming quiz free?

Yes. The DSA & Competitive Programming quiz is completely free with no signup. Answer the questions, get instant scoring, and earn XP as you go.

Q: What level is the DSA & Competitive Programming quiz?

This is a Expert-level quiz. Each question includes the correct answer so you learn as you test yourself, whatever your starting point.

Q: How can I prepare for this quiz?

Review the linked courses and notes for the topic first, then take the quiz to check your understanding. You can retake it anytime to track improvement.

Question 1

A greedy algorithm always produces the optimal solution:

Accepted Answer

Greedy works for problems like activity selection or Huffman coding, but fails on many others (e.g., 0/1 knapsack) where a locally-best choice isn’t globally optimal.

Question 2

Dynamic programming is applicable when a problem has:

Accepted Answer

DP caches solutions to overlapping subproblems (memoization/tabulation), avoiding recomputation — the opposite of naive exponential recursion.

Question 3

Binary search requires the search space to be sorted (or monotonic in the property being checked).

Accepted Answer

True. Binary search relies on being able to discard half the space based on a comparison — it needs sortedness/monotonicity to be valid.

Question 4

What is the time complexity of building a heap from n elements?

Accepted Answer

A common trick question: naive insertion gives O(n log n), but bottom-up heapify builds a heap in O(n) — a classic complexity gotcha.

Question 5

Why can a naive recursive Fibonacci be exponential while a DP version is linear?

Accepted Answer

Naive recursion branches into duplicate calls (fib(n-1), fib(n-2) both recompute overlapping work); memoizing collapses it to O(n).

Question 6

A Trie (prefix tree) is well-suited for fast prefix-based string lookups (autocomplete).

Accepted Answer

True. A Trie shares common prefixes across stored strings, enabling O(length of query) prefix search — ideal for autocomplete/dictionaries.

Question 7

Two-pointer technique is typically used on:

Accepted Answer

Two pointers moving inward/outward on a sorted array (e.g., two-sum, container-with-most-water) solve in linear time what brute force does in O(n²).

Question 8

Dijkstra’s algorithm fails to give correct shortest paths when the graph has:

Accepted Answer

Dijkstra assumes non-negative weights; with negative edges, use Bellman-Ford instead (which can also detect negative cycles).

Question 9

Amortized O(1) for dynamic array append means most appends are O(1), with occasional O(n) resizes averaging out.

Accepted Answer

True. Doubling the array on resize makes the total cost across n appends O(n), so each append is O(1) amortized despite occasional expensive resizes.

Question 10

A "sliding window" technique is best suited for problems about:

Accepted Answer

Sliding window expands/shrinks a contiguous range incrementally, avoiding recomputation — turning many O(n²) brute-force scans into O(n).

DSA & Competitive Programming

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DSA & Competitive Programming

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