𝗔𝗜 & 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 (𝗡𝗼 𝗖𝗼𝗱𝗶𝗻𝗴 𝗡𝗲𝗲𝗱𝗲𝗱)

Apply Now👉:- https://pdlink.in/4aYWald

By E&ICT Academy, IIT Roorkee

Batch Closing Soon - 26th July 2026

🚀 Coding Interview Questions with Answers (Part 10)

9️⃣1️⃣ What is Quick Sort?

Answer:

Quick Sort is a Divide and Conquer sorting algorithm that selects a pivot element and partitions the array so that elements smaller than the pivot are placed on its left and larger elements on its right. The process is then repeated recursively for the left and right subarrays.

Time Complexity:

• Best Case: O(n log n)

• Average Case: O(n log n)

• Worst Case: O(n²) (when the pivot selection is poor)

Advantages:

• Fast in practice

• In-place sorting (requires little extra memory)

• Widely used for large datasets

9️⃣2️⃣ What is Bubble Sort?

Answer:

Bubble Sort repeatedly compares adjacent elements and swaps them if they are in the wrong order. This process continues until the array is sorted.

Time Complexity:

• Best Case: O(n) (optimized version)

• Average Case: O(n²)

• Worst Case: O(n²)

Advantages:

• Easy to understand and implement.

Disadvantages:

• Inefficient for large datasets.

9️⃣3️⃣ What is Insertion Sort?

Answer:

Insertion Sort builds the sorted array one element at a time by inserting each new element into its correct position.

Time Complexity:

• Best Case: O(n)

• Average Case: O(n²)

• Worst Case: O(n²)

Advantages:

• Simple implementation

• Efficient for small or nearly sorted datasets

• Stable sorting algorithm

9️⃣4️⃣ What is Selection Sort?

Answer:

Selection Sort repeatedly finds the smallest element from the unsorted portion of the array and places it at the beginning.

Time Complexity:

• Best Case: O(n²)

• Average Case: O(n²)

• Worst Case: O(n²)

Advantages:

• Simple to implement

• Performs fewer swaps compared to Bubble Sort

9️⃣5️⃣ What is Heap Sort?

Answer:

Heap Sort is a comparison-based sorting algorithm that uses a Binary Heap data structure.

Steps:

1. Build a Max Heap.

2. Swap the root with the last element.

3. Reduce the heap size.

4. Heapify the remaining elements.

5. Repeat until the array is sorted.

Time Complexity:

• Best Case: O(n log n)

• Average Case: O(n log n)

• Worst Case: O(n log n)

Advantages:

• Guaranteed O(n log n) performance

• In-place sorting algorithm

9️⃣6️⃣ What is Counting Sort?

Answer:

Counting Sort is a non-comparison-based sorting algorithm that counts the occurrences of each element and uses these counts to determine their correct positions.

Time Complexity: O(n + k)

Where:

• n = Number of elements

• k = Range of input values

Advantages:

• Extremely fast for small ranges

• Stable sorting algorithm

Limitation:

• Not suitable when the range of values is very large.

9️⃣7️⃣ What is Radix Sort?

Answer:

Radix Sort sorts numbers digit by digit, starting from either the least significant digit (LSD) or the most significant digit (MSD).

It commonly uses Counting Sort as the intermediate sorting algorithm.

Time Complexity: O(n × d)

Where:

• n = Number of elements

• d = Number of digits

Advantages:

• Very efficient for sorting integers and strings with fixed lengths.

9️⃣8️⃣ What is Divide and Conquer?

Answer:

Divide and Conquer is an algorithm design technique that solves a problem by: 

1. Dividing it into smaller subproblems. 

2. Solving each subproblem recursively. 

3. Combining their solutions to solve the original problem. 

Examples: 

• Merge Sort 

• Quick Sort 

• Binary Search 

9️⃣9️⃣ What is a Greedy Algorithm?

Answer:

A Greedy Algorithm builds a solution step by step by always choosing the locally optimal option at each stage, hoping it leads to the global optimum.

Examples: 

• Kruskal's Algorithm 

• Prim's Algorithm 

• Dijkstra's Algorithm 

• Huffman Coding 

Advantages: 

• Fast and easy to implement 

Limitation: 

• Does not always produce the optimal solution.

1️⃣0️⃣0️⃣ What is Dynamic Programming?

Answer:

Dynamic Programming (DP) is an optimization technique used to solve problems by breaking them into smaller overlapping subproblems and storing their solutions to avoid repeated computations.

Two Approaches: 

• Memoization (Top-Down): Uses recursion with caching. 

• Tabulation (Bottom-Up): Solves subproblems iteratively using a table. 

Applications: 

• Fibonacci Sequence 

• Longest Common Subsequence 

• Knapsack Problem 

• Coin Change Problem 

• Matrix Chain Multiplication 

Benefits: 

• Reduces time complexity 

• Avoids redundant calculations 

• Improves performance for complex recursive problems 

🔥 Double Tap ❤️ For Part-11

🚀 𝗖𝗶𝘀𝗰𝗼 𝗙𝗥𝗘𝗘 𝗧𝗲𝗰𝗵 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 | 𝟱 𝗠𝘂𝘀𝘁-𝗗𝗼 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 🎓

Cisco offers learning opportunities covering some of the most valuable foundations for careers in Cybersecurity, Networking, Linux and IoT.

✅ Beginner-Friendly Tech Skills
✅ Learn In-Demand IT Concepts
✅ Build Practical Knowledge
✅ Strengthen Your Resume
✅ Great for Students & Freshers

𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:- 

https://pdlink.in/4fhCSKo

🔥 Learn from Cisco • Build Skills • Upgrade Your Resume • Get Career-Ready!

🎓 𝐀𝐜𝐜𝐞𝐧𝐭𝐮𝐫𝐞 𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 😍

Boost your skills with 100% FREE certification courses from Accenture!

📚 FREE Courses Offered:
1️⃣ Data Processing and Visualization
2️⃣ Exploratory Data Analysis
3️⃣ SQL Fundamentals
4️⃣ Python Basics
5️⃣ Acquiring Data

𝐋𝐢𝐧𝐤 👇:- 

https://pdlink.in/4hfxyIX

✅ Learn Online | 📜 Get Certified

If I wanted to get my opportunity to interview at Google or Amazon for SDE roles in the next 6-8 months…

Here’s exactly how I’d approach it (I’ve taught this to 100s of students and followed it myself to land interviews at 3+ FAANGs):

► Step 1: Learn to Code (from scratch, even if you’re from non-CS background)

I helped my sister go from zero coding knowledge (she studied Biology and Electrical Engineering) to landing a job at Microsoft.

We started with:
- A simple programming language (C++, Java, Python — pick one)
- FreeCodeCamp on YouTube for beginner-friendly lectures
- Key rule: Don’t just watch. Code along with the video line by line.

Time required: 30–40 days to get good with loops, conditions, syntax.

► Step 2: Start with DSA before jumping to development

Why?
- 90% of tech interviews in top companies focus on Data Structures & Algorithms
- You’ll need time to master it, so start early.

Start with:
- Arrays → Linked List → Stacks → Queues
- You can follow the DSA videos on my channel.
- Practice while learning is a must.

► Step 3: Follow a smart topic order

Once you’re done with basics, follow this path:

1. Searching & Sorting
2. Recursion & Backtracking
3. Greedy
4. Sliding Window & Two Pointers
5. Trees & Graphs
6. Dynamic Programming
7. Tries, Heaps, and Union Find

Make revision notes as you go — note down how you solved each question, what tricks worked, and how you optimized it.

► Step 4: Start giving contests (don’t wait till you’re “ready”)

Most students wait to “finish DSA” before attempting contests.
That’s a huge mistake.

Contests teach you:
- Time management under pressure
- Handling edge cases
- Thinking fast

Platforms: LeetCode Weekly/ Biweekly, Codeforces, AtCoder, etc.
And after every contest, do upsolving — solve the questions you couldn’t during the contest.

► Step 5: Revise smart

Create a “Revision Sheet” with 100 key problems you’ve solved and want to reattempt.

Every 2-3 weeks, pick problems randomly and solve again without seeing solutions.

This trains your recall + improves your clarity.

Coding Projects:👇
https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502

ENJOY LEARNING 👍👍

🚀 𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝗤𝗟 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘! 🗄️💻

Start learning SQL with these 100% FREE resources and build one of the most in-demand skills in tech!

✅ Beginner-Friendly SQL Tutorials
✅ FREE Online SQL Courses
✅ Interactive SQL Practice Platforms
✅ Real-World Database Projects
✅ Interview Preparation Resources
✅ Hands-on Exercises & Challenges

𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:- 

https://pdlink.in/4yLrNci

🚀 Start your SQL journey today and unlock exciting career opportunities!

🚀 Coding Interview Questions with Answers (Part 11)

1️⃣0️⃣1️⃣ What is Memoization?

Answer:

Memoization is a top-down Dynamic Programming technique where the results of previously solved subproblems are stored (cached). When the same subproblem appears again, the stored result is returned instead of recomputing it.

Advantages:

• Avoids repeated calculations

• Improves performance

• Reduces time complexity

Example: Fibonacci sequence using recursion with caching.

1️⃣0️⃣2️⃣ What is Tabulation?

Answer:

Tabulation is a bottom-up Dynamic Programming approach that solves smaller subproblems first and stores their results in a table. The final solution is built iteratively without recursion.

Advantages:

• No recursion overhead

• Avoids stack overflow

• Often faster than memoization

Example: Fibonacci sequence using an array.

1️⃣0️⃣3️⃣ What is Backtracking?

Answer:

Backtracking is an algorithmic technique that builds a solution step by step and abandons a path as soon as it determines that the path cannot lead to a valid solution.

Applications:

• N-Queens Problem

• Sudoku Solver

• Maze Solving

• Permutations and Combinations

Time Complexity: Depends on the problem, often exponential.

1️⃣0️⃣4️⃣ What is Branch and Bound?

Answer:

Branch and Bound is an optimization technique used to solve combinatorial problems by systematically exploring all possible solutions while eliminating branches that cannot produce a better result.

Applications:

• Travelling Salesman Problem

• Job Scheduling

• Knapsack Problem

Benefit: Reduces unnecessary computations compared to brute force.

1️⃣0️⃣5️⃣ What is Recursion?

Answer:

Recursion is a programming technique where a function calls itself to solve smaller instances of the same problem.

Every recursive function must have:

• Base Case: Stops recursion.

• Recursive Case: Calls itself with a smaller input.

Examples:

• Factorial

• Fibonacci

• Tree Traversal

1️⃣0️⃣6️⃣ What is Tail Recursion?

Answer:

Tail recursion is a special type of recursion where the recursive call is the last operation performed by the function.

Advantages:

• More memory efficient

• Can be optimized into iteration by some compilers

• Reduces stack usage

1️⃣0️⃣7️⃣ What is the Sliding Window Technique?

Answer:

Sliding Window is an algorithmic technique used to solve problems involving arrays or strings by maintaining a window of elements and moving it across the data.

Applications:

• Maximum sum subarray

• Longest substring without repeating characters

• Minimum window substring

Benefit: Often reduces time complexity from O(n²) to O(n).

1️⃣0️⃣8️⃣ What is the Two Pointers Technique?

Answer:

The Two Pointers technique uses two indices that move through an array or string to solve problems efficiently.

Applications:

• Two Sum (sorted array)

• Remove duplicates

• Reverse an array

• Check palindrome

Benefit: Frequently reduces time complexity from O(n²) to O(n).

1️⃣0️⃣9️⃣ What is Prefix Sum?

Answer:

Prefix Sum is a technique where each element stores the cumulative sum of all previous elements, allowing fast range sum queries.

Formula:

Prefix[i] = Prefix[i-1] + Array[i]

Applications:

• Range sum queries

• Subarray problems

• Competitive programming

Benefit: Range sums can be calculated in O(1) after preprocessing.

1️⃣1️⃣0️⃣ What is Binary Lifting?

Answer:

Binary Lifting is an advanced algorithm used to efficiently answer ancestor-related queries in trees by precomputing ancestors at powers of two.

Applications:

• Lowest Common Ancestor (LCA)

• Tree Queries

• Competitive Programming

Time Complexity:

• Preprocessing: O(n log n)

• Query: O(log n)

Double Tap ❤️ For Part-12

🚀 𝗖𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 & 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀

Build job-ready skills in two of the most in-demand technology fields and strengthen your résumé with valuable certifications! 🎓

🔐 Cyber Security :- https://pdlink.in/4bHIF9K
​
☁️ Cloud Computing :- https://pdlink.in/4yXs8bU
​
Perfect for Students, Freshers & Working Professionals looking to launch or upgrade their tech careers. 💼

🔗 Enroll for FREE & Get Certified

FREE RESOURCES TO PREPARE FOR YOUR NEXT INTERVIEW

Coding Interview Preparation

https://interviewgpt.ai

https://www.freecodecamp.org/learn/coding-interview-prep/#take-home-projects

http://Leetcode.com/

https://www.hackerrank.com/domains/data-structures

Python Interview Q&A

https://t.me/dsabooks/75

Beginner's guide for DSA

https://www.geeksforgeeks.org/the-ultimate-beginners-guide-for-dsa/amp/

Cracking the coding interview FREE BOOK

https://www.pdfdrive.com/cracking-the-coding-interview-189-programming-questions-and-solutions-d175292720.html

DSA Interview Questions and Answers

https://t.me/crackingthecodinginterview/77

Cracking the Coding interview: Learn 5 Essential Patterns
[4.5 star ratings out of 5]

https://bit.ly/3GUBk56

Data Science Interview Questions and Answers

https://t.me/datasciencefun/958

Java Interview Questions with Answers

https://t.me/Curiousprogrammer/106

SQL INTERVIEW Questions and Answers

https://t.me/sqlanalyst/61

Use Chat GPT to prepare for your next Interview
👇👇
https://t.me/getjobss/1483

Data Engineering Interview Questions

https://t.me/crackingthecodinginterview/691

ENJOY LEARNING 👍👍