DSA Competitive Programming - Practice Questions 2026
Course Description
Master DSA Competitive Programming: Ultimate Practice Exams
Welcome to the definitive practice resource designed to help you master Data Structures and Algorithms (DSA) for Competitive Programming. Whether you are preparing for top-tier coding contests or technical interviews at FAANG companies, these practice exams provide the rigorous environment you need to sharpen your problem-solving skills and improve your time complexity analysis.
Why Serious Learners Choose These Practice Exams
Competitive programming is not just about knowing the syntax; it is about recognizing patterns and applying the most efficient algorithm under pressure. Serious learners choose this course because it mimics the environment of platforms like Codeforces, LeetCode, and CodeChef.
Retakeability: You can retake the exams as many times as you want to ensure total mastery.
Original Question Bank: This is a huge, original question bank curated by industry experts.
Expert Support: You get direct support from instructors if you have questions regarding specific logic or edge cases.
Detailed Explanations: Every single question includes a comprehensive breakdown of the logic used.
On-the-go Learning: Fully mobile-compatible via the Udemy app.
Risk-Free: We offer a 30-day money-back guarantee if you are not satisfied with the content.
Course Structure
This course is meticulously organized to take you from foundational logic to high-level algorithmic mastery.
Basics / Foundations: Focuses on the building blocks of programming including Time and Space Complexity (Big O notation), recursion fundamentals, and basic array manipulations.
Core Concepts: Covers essential data structures like Linked Lists, Stacks, Queues, and Binary Trees. You will learn how to implement and traverse these structures efficiently.
Intermediate Concepts: Dives into sorting and searching algorithms, Heaps (Priority Queues), and Hashing techniques. This section bridges the gap between simple data storage and optimized retrieval.
Advanced Concepts: Explores complex topics such as Dynamic Programming (DP), Graph Theory (Dijkstra’s, MST, Flow), Segment Trees, and Fenwick Trees.
Real-world Scenarios: Challenges you with problems that simulate actual software engineering hurdles, requiring you to combine multiple data structures for an optimal solution.
Mixed Revision / Final Test: A comprehensive evaluation featuring a random mix of all topics to test your ability to identify the correct approach without topical hints.
Sample Practice Questions
QUESTION 1
What is the time complexity of building a binary heap from an unsorted array of $n$ elements?
$O(1)$
$O(\log n)$
$O(n)$
$O(n \log n)$
$O(n^2)$
CORRECT ANSWER: 3
CORRECT ANSWER EXPLANATION: While inserting $n$ elements one by one takes $O(n \log n)$, the "Build-Heap" algorithm (bottom-up heapify) runs in $O(n)$ because the work decreases as you move up the tree.
WRONG ANSWERS EXPLANATION:
Option 1: Building a heap requires processing all elements, so it cannot be constant time.
Option 2: This is the complexity of a single insertion or deletion, not the whole build process.
Option 4: This is the complexity if you use the naive method of $n$ successive insertions.
Option 5: This is inefficient and would only occur in poorly implemented sorting algorithms like bubble sort.
QUESTION 2
Which of the following data structures is most efficient for checking if a cycle exists in an undirected graph?
Stack
Queue
Disjoint Set Union (DSU)
Linked List
Min-Heap
CORRECT ANSWER: 3
CORRECT ANSWER EXPLANATION: DSU with path compression and union by rank provides near-constant time operations to detect cycles by checking if two vertices already belong to the same set.
WRONG ANSWERS EXPLANATION:
Option 1: While a Stack can be used in DFS for cycle detection, it is not a "data structure for checking" but rather a tool for traversal.
Option 2: Queues are used in BFS; while BFS can detect cycles, DSU is generally more specialized and efficient for this specific property.
Option 4: A Linked List has no inherent properties to manage graph connectivity or cycles efficiently.
Option 5: Heaps are used for ordering elements by priority, not for tracking connectivity.
QUESTION 3
In Dynamic Programming, what is the main difference between "Tabulation" and "Memoization"?
Tabulation is Top-Down; Memoization is Bottom-Up.
Tabulation uses recursion; Memoization uses loops.
Tabulation is Bottom-Up; Memoization is Top-Down.
Tabulation uses more memory than Memoization.
There is no difference; they are synonyms.
CORRECT ANSWER: 3
CORRECT ANSWER EXPLANATION: Tabulation starts from the base cases and builds up to the solution using iterations (Bottom-Up). Memoization starts from the main problem and caches results of subproblems using recursion (Top-Down).
WRONG ANSWERS EXPLANATION:
Option 1: This is the exact opposite of the correct definitions.
Option 2: Generally, Tabulation uses loops and Memoization uses recursion.
Option 4: Both typically use $O(n)$ space for storage, though Tabulation can sometimes be optimized further.
Option 5: They are different strategies for implementing DP solutions.
We hope that by now you're convinced! There are hundreds of additional questions inside the course designed to push your limits.
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