🎯 💻 Coding Interview Questions (With Answers)

🧠 1️⃣ Tell me about yourself
✅ Sample Answer:
"I have 4+ years as a software engineer specializing in full-stack development and algorithms. I've built scalable systems handling 1M+ daily users at a fintech startup using MERN stack and microservices. Expert in JavaScript/Python, system design, and competitive programming (LeetCode 2000+/2800). I love writing clean, testable code and optimizing for performance under scale."

📊 2️⃣ What is the difference between a stack and a queue?
✅ Answer:
A stack follows LIFO (Last In, First Out) principle with operations push (add to top) and pop (remove from top). Use cases: function call stack, undo/redo features.
A queue follows FIFO (First In, First Out) with enqueue (add to rear) and dequeue (remove from front). Use cases: breadth-first search, task scheduling, printers.
Both O(1) operations with arrays/linked lists.

🔗 3️⃣ What is the difference between time complexity and space complexity?
✅ Answer:
Time complexity measures how runtime grows with input size n (e.g., O(n²) quadratic loops).
Space complexity measures memory usage growth (e.g., O(n) array stores all elements).
Tradeoffs exist: recursion uses stack space O(n), iteration uses O(1). Always analyze both.

🧠 4️⃣ How do you find duplicates in an array?
✅ Answer:
Optimal: Hash Set O(n) time/space

function findDuplicates(arr) {
    const seen = new Set();
    const dups = new Set();
    for (let num of arr) {
        if (seen.has(num)) dups.add(num);
        else seen.add(num);
    }
    return Array.from(dups);
}

Space optimized: Sort O(n log n) then scan adjacent equals.

📈 5️⃣ What is binary search and when would you use it?
✅ Answer:
Binary search finds target in sorted array in O(log n) by repeatedly dividing search interval in half:
mid = (left + right) / 2
If arr[mid] == target return mid
If arr[mid] < target search right half
Else search left half
Use when: Data naturally sorted or sorting cost acceptable. Iterative version avoids recursion stack overflow.

📊 6️⃣ How do you reverse a linked list?
✅ Answer:
Iterative O(n) solution flipping next pointers:

function reverseList(head) {
    let prev = null, curr = head;
    while (curr) {
        let nextTemp = curr.next;
        curr.next = prev;
        prev = curr;
        curr = nextTemp;
    }
    return prev;
}

Recursive: reverseList(curr.next).then(curr.next.prev = curr, curr.next = null).

📉 7️⃣ What is recursion and why is the base case important?
✅ Answer:
Recursion is a function calling itself with modified arguments until base case stops it. Without base case → stack overflow.
Example Fibonacci:

function fib(n) {
    if (n <= 1) return n; // Base case
    return fib(n-1) + fib(n-2);
}

Memoization optimizes overlapping subproblems.

📊 8️⃣ How do you merge two sorted arrays?
✅ Answer:
Two-pointer technique O(n+m):

function mergeSorted(a1, a2) {
    let i=0, j=0, result = [];
    while (i < a1.length && j < a2.length) {
        if (a1[i] < a2[j]) result.push(a1[i++]);
        else result.push(a2[j++]);
    }
    return result.concat(a1.slice(i)).concat(a2.slice(j));
}

Handles unequal lengths cleanly.

🧠 9️⃣ How do you detect a cycle in a linked list?
✅ Answer:
Floyd's Tortoise & Hare: Slow moves 1 step, fast moves 2. If they meet → cycle.
To find start: Reset slow to head, move both 1 step until meet.

function hasCycle(head) {
    let slow = head, fast = head;
    while (fast && fast.next) {
        slow = slow.next;
        fast = fast.next.next;
        if (slow === fast) return true;
    }
    return false;
}

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