🚀 Coding Interview Questions with Answers — Part 1

🧠 1. What is an array and how is it stored in memory?
An array is a data structure used to store multiple elements of the same data type in a contiguous block of memory.

Example: arr = [10, 20, 30, 40]

🔹 Key Features
- Fixed size (in most languages)
- Fast access using index
- Stores elements sequentially

🔹 Memory Representation
If an integer takes 4 bytes:

Index | Value | Memory Address
0 | 10 | 1000
1 | 20 | 1004
2 | 30 | 1008
3 | 40 | 1012

Each element is stored next to the previous one.

🔹 Time Complexity
Operation | Complexity
Access | O(1)
Search | O(n)
Insert/Delete (middle) | O(n)

🔹 Interview Tip
Arrays are preferred when:
- Fast indexing is needed
- Memory efficiency matters
- Data size is mostly fixed

🚀 2. What is the difference between an array and a linked list?

Feature | Array | Linked List
Memory | Contiguous | Non-contiguous
Access Speed | O(1) | O(n)
Insert/Delete | Slow | Fast
Size | Fixed | Dynamic
Extra Memory | Less | More (pointer storage)

🔹 Array Example: arr = [1, 2, 3]
🔹 Linked List Example: 1 → 2 → 3 → NULL
Each node stores: Data + Pointer to next node

🔹 When to Use
✅ Use Arrays: Random access needed, Cache-friendly operations
✅ Use Linked Lists: Frequent insertions/deletions, Dynamic memory allocation

🔹 Interview Tip
Linked lists solve resizing problems of arrays but sacrifice fast access speed.

🚀 3. Explain time complexity using Big-O notation
Big-O notation measures how an algorithm grows as input size increases.

🔹 Common Complexities
Complexity | Meaning
O(1) | Constant
O(log n) | Logarithmic
O(n) | Linear
O(n log n) | Efficient sorting
O(n²) | Nested loops
O(2ⁿ) | Exponential

🔹 Example:
for i in range(n):
print(i)
This runs n times. ➡️ Complexity = O(n)

🔹 Nested Loop Example:
for i in range(n):
for j in range(n):
print(i, j)

➡️ Complexity = O(n²)

🔹 Why It Matters
Interviewers use Big-O to evaluate: Scalability, Efficiency, Optimization skills

🔹 Interview Tip
Always discuss: Time complexity, Space complexity, Trade-offs

🚀 4. How do you implement a stack using an array?
A stack follows the LIFO principle: Last In, First Out

Operations: Push, Pop, Peek

🔹 Python Implementation:
class Stack:
def init(self):
self.stack = []

def push(self, value):
self.stack.append(value)

def pop(self):
if self.is_empty():
return "Stack Underflow"
return self.stack.pop()

def peek(self):
if self.is_empty():
return None
return self.stack[-1]

def is_empty(self):
return len(self.stack) == 0

🔹 Example:
s = Stack()
s.push(10)
s.push(20)
print(s.pop()) # 20

🔹 Complexity
Operation | Complexity
Push | O(1)
Pop | O(1)
Peek | O(1)

🔹 Real-World Uses
Undo feature, Browser history, Function call stack, Expression evaluation

🚀 5. How do you implement a queue using an array or linked list?
A queue follows the FIFO principle: First In, First Out

Operations: Enqueue, Dequeue

🔹 Queue Using Array:
class Queue:
def init(self):
self.queue = []

def enqueue(self, value):
self.queue.append(value)

def dequeue(self):
if not self.queue:
return "Empty Queue"
return self.queue.pop(0)

⚠️ Problem: pop(0) takes O(n) because elements shift.

🔹 Queue Using Linked List:
from collections import deque

q = deque()
q.append(10)
q.append(20)
print(q.popleft())

🔹 Complexity
Operation | Complexity
Enqueue | O(1)
Dequeue | O(1)

🔹 Real-World Uses
CPU scheduling, Task queues, Messaging systems, BFS traversal

🚀 6. How does a hash table work?
A hash table stores key-value pairs using a hash function.

🔹 Example:
student = {
"name": "John",
"age": 22
}

🔹 Working: 
1. Key goes into hash function 
2. Hash function generates index 
3. Value stored at that index 

🔹 Example Flow 
hash("age") → index 5 
Store: table[5] = 22