What is DSA and why is it important for interviews?

DSA (Data Structures and Algorithms) forms the foundation of coding interviews at top tech companies. It tests problem-solving skills, coding ability, and understanding of time/space complexity. Companies like Google, Amazon, Meta, and Microsoft heavily focus on DSA in their technical interviews.

How many DSA problems should I solve before interviews?

For FAANG-level interviews, aim to solve 150-200 quality problems covering all major topics. Focus on understanding patterns rather than memorizing solutions. Cover: 30+ Array/String problems, 20+ Tree problems, 15+ Graph problems, 25+ DP problems, and 10+ problems each for Stack, Heap, and Binary Search.

What topics are most important for DSA interviews?

Key DSA topics: Arrays & Strings (Two Sum, Sliding Window), Binary Search, Linked Lists, Trees & BST (traversals, LCA), Graphs (BFS, DFS, shortest path), Dynamic Programming (1D & 2D), Stack & Queue, Heap/Priority Queue, Backtracking, and Greedy Algorithms.

Is InstaMock DSA practice free?

Yes! InstaMock offers 150+ DSA problems for free with instant code execution in Java, Python, and C++. You can run test cases, submit solutions, and get immediate feedback without any payment.

How is InstaMock different from LeetCode?

InstaMock combines DSA practice with AI mock interviews. While you can solve coding problems like LeetCode, you also get AI-powered interview simulations, real-time feedback, and comprehensive preparation for System Design (HLD/LLD) along with DSA.

Course Schedule Feasibility II

Given numCourses and prerequisite pairs, return true if it is possible to finish all courses. Pattern focus: DFS Approach. Use visit states to detect back-edges and prove acyclicity.

Input Format

numCourses = course count, prerequisites = prerequisite pairs [course, prerequisite]

Output Format

true if all courses can be completed

Constraints

1 <= input size <= 10^5
-10^9 <= numeric values <= 10^9

Examples

Example 1:

Input:

numCourses = 2
prerequisites = [[1,0]]

Output:

true

Explanation:

A simple dependency chain is valid.

Example 2:

Input:

numCourses = 2
prerequisites = [[1,0],[0,1]]

Output:

false

Explanation:

The DFS will revisit a node currently in the recursion stack.

Example 3:

Input:

numCourses = 4
prerequisites = [[1,0],[2,1],[3,2]]

Output:

true

Explanation:

A chain without a back-edge is valid.