Ace DSA Interviews for 20 LPA Salary

Introduction

Data Structures and Algorithms (DSA) form the backbone of technical interviews at top tech companies. Mastering DSA can open doors to high-paying jobs, with salaries ranging from 15 LPA to 20+ LPA. However, many aspiring developers struggle with where to start and how to stay consistent.

Let me share the story of Rahul, a software engineer who started with zero DSA knowledge but landed a 25 LPA package at a top product-based company. His journey was filled with failures, late-night coding sessions, and continuous learning. By following a structured roadmap, he was able to crack interviews at companies like Google and Microsoft.

In this guide, we will cover:

Types of DSA questions asked in interviews (Easy, Medium, Hard)
A structured roadmap to prepare for DSA
Recommended resources and platforms
Strategies to land a 20 LPA job
Frequently asked DSA interview questions with answers

1. Understanding DSA Questions

DSA questions in interviews typically fall into three categories:

Easy Questions

These test basic problem-solving and coding skills.

Two Sum: Find two numbers that add up to a target.
Balanced Parentheses: Check if a string has balanced brackets.
Invert Binary Tree: Flip a binary tree left to right.

Medium Questions

These require logical thinking and intermediate problem-solving skills.

Merge Overlapping Intervals: Merge overlapping ranges.
LRU Cache: Implement the Least Recently Used Cache.
Longest Common Subsequence (LCS): Find the LCS of two strings.

Hard Questions

These involve advanced problem-solving techniques and optimisations.

Sudoku Solver: Solve a partially filled Sudoku board.
N-Queens Problem: Place N queens on an NxN board.

Best Time to Buy and Sell Stock IV: Maximise stock trading profit with k transactions.

Common DSA Interview Questions & Answers

1. Two Sum (Easy)

Question: Given an array of integers nums and an integer target, return indices of the two numbers such that they add up to the target.

Answer (Python):

  def two_sum(nums, target):
      hashmap = {}
      for i, num in enumerate(nums):
          complement = target - num
          if complement in hashmap:
              return [hashmap[complement], i]
          hashmap[num] = i
      return []

Explanation: The solution uses a hashmap to store previously seen numbers for quick look-up, reducing the time complexity to O(n).

2. Merge Overlapping Intervals (Medium)

Question: Given a list of intervals, merge all overlapping intervals.

Answer (Python):

  def merge_intervals(intervals):
      intervals.sort(key=lambda x: x[0])
      merged = []
      for interval in intervals:
          if not merged or merged[-1][1] < interval[0]:
              merged.append(interval)
          else:
              merged[-1][1] = max(merged[-1][1], interval[1])
      return merged

Explanation: Sorting ensures we process intervals in order, merging overlapping intervals in O(n log n) time.

3. LRU Cache (Hard)

Question: Design a Least Recently Used (LRU) cache with get and put operations.

Answer (Python using ordereddict):

  from collections import OrderedDict

  class LRUCache:
      def __init__(self, capacity: int):
          self.cache = OrderedDict()
          self.capacity = capacity

      def get(self, key: int) -> int:
          if key not in self.cache:
              return -1
          self.cache.move_to_end(key)
          return self.cache[key]

      def put(self, key: int, value: int) -> None:
          if key in self.cache:
              self.cache.move_to_end(key)
          elif len(self.cache) >= self.capacity:
              self.cache.popitem(last=False)
          self.cache[key] = value

Explanation: The OrderedDict maintains insertion order, allowing quick updates while keeping operations O(1).

4. Longest Common Subsequence (LCS) (Medium)

Question: Given two strings, find the length of their longest common subsequence.

Answer (Python using DP):

  def lcs(X, Y):
      m, n = len(X), len(Y)
      dp = [[0] * (n + 1) for _ in range(m + 1)]

      for i in range(1, m + 1):
          for j in range(1, n + 1):
              if X[i - 1] == Y[j - 1]:
                  dp[i][j] = 1 + dp[i - 1][j - 1]
              else:
                  dp[i][j] = max(dp[i - 1][j], dp[i][j - 1])

      return dp[m][n]

2. How to Prepare for DSA Interviews

To crack DSA interviews, follow a structured approach:

Step 1: Master the Fundamentals

Before jumping into solving problems, get a firm grasp of fundamental concepts. Understanding how data structures work is key.

Arrays and Strings
Linked Lists
Stacks and Queues
Hashing and HashMaps
Trees and Graphs
Dynamic Programming

Rahul initially struggled with dynamic programming, often getting stuck on problems for hours. But he didn't give up. He watched YouTube tutorials, read GeeksforGeeks explanations, and repeatedly reattempted problems. Over time, his problem-solving ability improved drastically.

Step 2: Solve 250+ DSA Problems

Practice problems on:

LeetCode: Best for FAANG-level problems
CodeStudio: Topic-wise structured DSA roadmap
GeeksforGeeks: Most commonly asked questions

Step 3: Master Time & Space Complexity

For every problem you solve, analyse:

Best, Average, and Worst-case complexity
Optimize from brute force to the best possible solution

Step 4: Take Mock Interviews

Mock interviews are crucial. Platforms like Pramp, InterviewBit, and TechMock allow you to practice real interview scenarios.

3. Frequently Asked DSA Interview Questions & Answers

Q1: What is the difference between an array and a linked list?

A: Arrays provide fast access via indexing but have a fixed size, while linked lists allow dynamic memory allocation but have slower access times due to pointer traversal.

Q2: How do you detect a cycle in a linked list?

A: You can use Floyd’s Cycle Detection Algorithm (Tortoise and Hare method). If two pointers (slow and fast) meet, a cycle exists.

Q3: What is the time complexity of quicksort?

A: Best and average case: O(n log n); worst case: O(n^2) (when the pivot is poorly chosen).

Q4: Explain dynamic programming with an example.

A: Dynamic programming (DP) breaks problems into overlapping subproblems. Example: Fibonacci series using memoization reduces time complexity from O(2^n) to O(n).

Q5: What is the difference between BFS and DFS?

A: BFS (Breadth-First Search) explores level by level, using a queue (FIFO). DFS (Depth-First Search) explores deep paths first, using a stack (LIFO) or recursion.

4. How to Get a 20 LPA Salary in Tech

1. Target FAANG & Top Product Companies

Companies that offer 20 LPA+ salaries:

Google, Amazon, Meta, Microsoft, Apple
Uber, Flipkart, Goldman Sachs, Directi

2. Build Strong Problem-Solving Skills

Solve medium-hard problems daily.
Take part in Codeforces and LeetCode contests.

3. Learn System Design (For Senior Roles)

Low-Level Design (LLD): Design patterns, Object-Oriented Programming
High-Level Design (HLD): Scalable architectures

Rahul realised that just solving DSA problems wasn't enough. He started reading system design case studies, building scalable projects, and discussing solutions with peers. This gave him an edge in interviews.

4. Develop projects and contribute to Open Source

Add real-world projects to GitHub.
Contribute to Google Summer of Code (GSoC).

5. Prepare for Behavioral Rounds

Use the STAR method to answer HR questions.
Focus on leadership and problem-solving.

5. Best Platforms to Practice DSA

Here are some top resources:

LeetCode (FAANG-level problems)
CodeForces (Competitive programming)
GeeksforGeeks (Conceptual clarity)
Scaler Academy (Structured DSA courses)
NeetCode YouTube Channel (Best explanations)

6. Conclusion

Mastering DSA is a stepping stone to cracking top tech interviews and landing high-paying jobs. Stay consistent, follow a structured roadmap, and practice daily. With dedication, achieving a 20 LPA+ salary is well within reach!

Rahul’s journey proves that with persistence and the right strategy, anyone can achieve their dream job. Are you ready to take your DSA preparation to the next level? Start solving problems today! 🚀

DSA Interview Questions & How to Get a 20 LPA Salary