Preparing for coding interviews in 2025? Whether you are a fresher aiming for your first job or an experienced developer targeting FAANG (Facebook, Amazon, Apple, Netflix, Google) or other top tech companies, mastering data structure interview questions is a must.
Most coding interviews test your ability to:
- Understand and apply core data structures such as arrays, linked lists, stacks, queues, trees, graphs, and hashing.
- Analyze time and space complexity of solutions.
- Write optimized, bug-free code in real-time under pressure.
In this comprehensive 2025 guide, we have compiled more than 100+ important questions covering basics, advanced concepts, and coding challenges. Each section is designed to help you not only understand theory but also practice coding problems in C++, Java, and Python — the three most popular languages for technical interviews.
Whether you are preparing for on-campus placements, top MNC hiring rounds, or FAANG-level coding interviews, this resource will help you master the most commonly asked data structure interview questions that recruiters use to evaluate problem-solving ability.
Tip: Don’t just read but practice solving these data structure coding interview questions daily. Consistency is the key to cracking technical interviews in 2025.
Table of Contents
Basic Data Structure Interview Questions
These questions are often asked to check your fundamental understanding before moving into advanced coding rounds.
What is a Data Structure?
Answer:
A data structure is a way of organizing, managing, and storing data so it can be accessed and modified efficiently.
- Examples: Array, Linked List, Stack, Queue, Tree, Graph.
What are the types of Data Structures?
Answer:
- Linear: Elements arranged sequentially (e.g., Array, Linked List, Stack, Queue).
- Non-Linear: Elements connected hierarchically (e.g., Tree, Graph).
- Homogeneous: Same type of data (e.g., Arrays).
- Non-Homogeneous: Different types (e.g., Structures in C).
What is the difference between Primitive and Non-Primitive Data Structures?
Answer:
- Primitive: Basic built-in types (int, char, float).
- Non-Primitive: Derived/custom types (arrays, stacks, queues, linked lists, graphs).
What is an Abstract Data Type (ADT)?
Answer:
An ADT defines what operations can be performed on a data structure without specifying how they’re implemented.
- Example: Stack ADT →
push(),pop(),peek().
Difference between Linear and Non-Linear Data Structures?
Answer:
- Linear: Data elements stored sequentially (Array, Stack, Queue).
- Non-Linear: Data stored hierarchically (Tree, Graph).
Explain Big-O Notation.
Answer:
Big-O describes the worst-case time complexity of an algorithm.
- Examples:
- Array search → O(n)
- Binary Search → O(log n)
What are Time and Space Complexity?
Answer:
- Time Complexity: Amount of time taken by an algorithm as input size grows.
- Space Complexity: Memory required by the algorithm.
Why are Arrays called Static Data Structures?
Answer:
Because their size is fixed at compile time and cannot be changed during execution.
What is the difference between Array and Linked List?
Answer:
| Array | Linked List |
|---|---|
| Fixed size | Dynamic size |
| Contiguous memory | Non-contiguous memory |
| Random access (O(1)) | Sequential access (O(n)) |
Define Stack and Queue.
Answer:
- Stack: LIFO (Last In, First Out).
- Queue: FIFO (First In, First Out).
What is the difference between Recursion and Iteration?
Answer:
- Recursion: Function calls itself until base condition is met. Uses stack.
- Iteration: Uses loops (
for,while) without function calls.
Explain Hashing.
Answer:
Hashing maps keys to values using a hash function.
- Example: Hash table → O(1) average lookup time.
What is a Binary Search Tree (BST)?
Answer:
A BST is a binary tree where:
- Left child < Parent
- Right child > Parent
What is Dynamic Programming (DP) in relation to Data Structures?
Answer:
DP is an optimization technique that stores results of subproblems (using arrays, hash tables, etc.) to avoid redundant calculations.
Name real-life applications of Data Structures.
Answer:
- Stack: Undo/Redo in editors.
- Queue: Printer queue.
- Graph: Social networks.
- Hashing: Password storage.
Arrays Data Structure Interview Questions
What is an Array?
Answer:
An array is a linear data structure that stores elements of the same type in contiguous memory locations.
- Access Time: O(1)
- Insertion/Deletion (worst case): O(n)
What are the advantages and disadvantages of Arrays?
Advantages:
- Random access (O(1))
- Simple to use
- Better cache locality
Disadvantages:
- Fixed size
- Expensive insertion/deletion
How do you find the largest element in an array?
Answer (Algorithm):
- Traverse array once, keep track of max.
- Time Complexity: O(n)
// C++
int largest(int arr[], int n) {
int max = arr[0];
for(int i=1; i<n; i++) if(arr[i] > max) max = arr[i];
return max;
}
// Java
int largest(int arr[]) {
int max = arr[0];
for(int i=1; i<arr.length; i++) if(arr[i] > max) max = arr[i];
return max;
}
# Python
def largest(arr):
return max(arr)
How do you reverse an array in place?
Answer: Swap elements from start and end.
- Time Complexity: O(n)
- Space Complexity: O(1)
def reverse(arr):
left, right = 0, len(arr)-1
while left < right:
arr[left], arr[right] = arr[right], arr[left]
left += 1; right -= 1
return arr
How to find the missing number in an array of 1 to n?
Answer: Use sum formula → n(n+1)/2 - sum(arr)
- Time Complexity: O(n)
What is the difference between one-dimensional and multi-dimensional arrays?
- 1D Array: Linear list (
int arr[5]). - 2D Array: Matrix (
int arr[3][3]). - 3D Array: Cube-like structure.
How do you rotate an array by k positions?
Answer:
- Use reversal algorithm or extra array.
- Time Complexity: O(n)
How do you find duplicates in an array?
Answer:
- Use hashing/sets.
- Time Complexity: O(n)
def find_duplicates(arr):
seen, dup = set(), []
for x in arr:
if x in seen:
dup.append(x)
seen.add(x)
return dup
How to merge two sorted arrays?
Answer:
- Use two pointers, merge like in merge sort.
- Time Complexity: O(n+m)
How to find the subarray with the maximum sum? (Kadane’s Algorithm)
Answer:
Kadane’s algorithm → O(n).
def maxSubArray(nums):
max_sum = cur_sum = nums[0]
for x in nums[1:]:
cur_sum = max(x, cur_sum + x)
max_sum = max(max_sum, cur_sum)
return max_sum
How do you check if an array is sorted?
Answer:
Loop through array → if arr[i] > arr[i+1], return False.
- Time Complexity: O(n)
How do you find the intersection of two arrays?
Answer:
- Use sets (Python) or HashMap (Java/C++).
- Time Complexity: O(n).
What is a dynamic array?
Answer:
- An array that grows dynamically when needed.
- Implemented as
vectorin C++,ArrayListin Java,listin Python.
How do you search for an element in an array?
Answer:
- Linear Search: O(n).
- Binary Search (sorted arrays): O(log n).
What are sparse arrays?
Answer:
- Arrays where most elements are zero/unused.
- Stored efficiently using a hash map or linked structure.
- Used in machine learning, matrix storage.
Linked List Interview Questions
This section is highly important since linked lists are a favorite in interviews (loop detection, reversal, merges). I’ll keep answers structured and add code snippets (C++, Java, Python) where needed.
What is a Linked List? How is it different from an Array?
Answer:
A linked list is a linear data structure where elements (nodes) are connected using pointers. Each node has:
- Data field (value)
- Pointer (next) to the next node
Difference from Array:
- Array: Contiguous memory, fixed size, O(1) access.
- Linked List: Non-contiguous memory, dynamic size, O(n) access.
What are the types of Linked Lists?
Answer:
- Singly Linked List – Node points to next.
- Doubly Linked List – Node points to next and previous.
- Circular Linked List – Last node points back to head.
How do you insert a node at the beginning of a linked list?
// C++
struct Node { int data; Node* next; };
Node* insertFront(Node* head, int val) {
Node* newNode = new Node();
newNode->data = val;
newNode->next = head;
return newNode;
}
// Java
class Node { int data; Node next; Node(int d){data=d;} }
Node insertFront(Node head, int val){
Node newNode = new Node(val);
newNode.next = head;
return newNode;
}
# Python
class Node:
def __init__(self, data):
self.data = data
self.next = None
def insert_front(head, val):
new_node = Node(val)
new_node.next = head
return new_node
How do you delete a node in a linked list?
Answer:
- Find the node.
- Adjust pointers of previous node.
- Free memory.
How do you reverse a linked list?
Answer:
- Iteratively or recursively.
- Time Complexity: O(n).
def reverse_list(head):
prev, curr = None, head
while curr:
nxt = curr.next
curr.next = prev
prev = curr
curr = nxt
return prev
How do you detect a loop in a linked list?
Answer:
Use Floyd’s Cycle Detection Algorithm (Tortoise & Hare).
- Time Complexity: O(n).
- Space Complexity: O(1).
How do you find the middle element of a linked list?
Answer:
- Use slow and fast pointers.
- When fast reaches end, slow is at middle.
How do you merge two sorted linked lists?
Answer:
- Use a dummy node and iterate both lists.
- Similar to merge step in Merge Sort.
What is the difference between a Singly and Doubly Linked List?
| Singly LL | Doubly LL |
|---|---|
| Each node points only to next | Each node points to next & previous |
| Requires less memory | Requires more memory |
| Traversal only forward | Traversal both ways |
How do you remove duplicates from a sorted linked list?
Answer:
- Traverse list → if
curr.data == curr.next.data, skip next node. - Time Complexity: O(n).
How do you check if a linked list is a palindrome?
Answer:
- Find middle of list.
- Reverse second half.
- Compare with first half.
How do you find the nth node from the end in a linked list?
Answer:
- Use two pointers.
- Move first pointer
nsteps ahead, then move both until end.
How do you detect the starting node of a loop in a linked list?
Answer:
- Use Floyd’s Cycle Detection.
- When fast=slow, move one pointer to head.
- Move both one step → they meet at loop start.
How do you implement a circular linked list?
Answer:
- Modify last node’s
nextto point to head. - Used in CPU scheduling, memory allocation.
What are the applications of Linked Lists?
Answer:
- Dynamic memory allocation.
- Implementing stacks and queues.
- Graph adjacency representation.
- Undo/redo in text editors.
Stack and Queue Interview Questions
Stacks and queues are among the most common interview questions because they test both theory + implementation skills.
What is a Stack?
Answer:
- A stack is a linear data structure based on LIFO (Last In, First Out) principle.
- Example: Pile of plates.
Operations:
push(x)→ insertpop()→ removepeek()→ view top element
What are the applications of Stack?
- Undo/Redo in text editors.
- Expression evaluation (postfix, prefix).
- Function call management (recursion).
- Browser back/forward navigation.
What is a Queue?
Answer:
- A queue is a linear data structure based on FIFO (First In, First Out) principle.
- Example: Ticket counter queue.
Operations:
enqueue(x)→ insert at reardequeue()→ remove from front
What are the types of Queues?
- Simple Queue – FIFO.
- Circular Queue – Last node connects back to first.
- Priority Queue – Elements dequeued based on priority.
- Double-Ended Queue (Deque) – Insert/delete from both ends.
How do you implement a Stack using Arrays?
#define MAX 100
class Stack {
int arr[MAX], top;
public:
Stack(){ top = -1; }
void push(int x){ if(top < MAX-1) arr[++top] = x; }
int pop(){ return (top >= 0) ? arr[top--] : -1; }
};
How do you implement a Queue using Arrays?
class Queue {
int front, rear, size;
int arr[];
Queue(int n){ arr=new int[n]; size=n; front=rear=-1; }
void enqueue(int x){ if(rear<size-1) arr[++rear]=x; }
int dequeue(){ return (front<rear) ? arr[++front] : -1; }
}
How do you implement a Stack using Queues?
Answer:
- Use two queues
q1,q2. - For
push(x): enqueue intoq1. - For
pop(): dequeue all except last fromq1intoq2, then swap.
How do you implement a Queue using Stacks?
Answer:
- Use two stacks
s1,s2. - For
enqueue(x): push intos1. - For
dequeue(): ifs2empty, pop all froms1intos2, then pop froms2.
What is a Circular Queue? Why is it used?
Answer:
- A queue where the last position connects back to first.
- Efficient memory usage → avoids “false overflow” problem in linear queues.
What is the difference between Stack and Queue?
| Stack | Queue |
|---|---|
| LIFO (Last In, First Out) | FIFO (First In, First Out) |
| One end operations | Both ends operations |
| Example: Function calls | Example: Printer queue |
Hashing Interview Questions
Hashing is a hot interview topic because it combines theory + coding and is widely used in real-world applications (databases, compilers, caching).
What is Hashing?
Answer:
Hashing is a technique that maps data (keys) to a fixed-size table (hash table) using a hash function.
- Example: Storing student records by roll number.
- Average case operations: O(1)
What is a Hash Function?
Answer:
- A function that converts input (key) → index in hash table.
- Example:
index = key % table_size. - Properties of a good hash function:
- Uniform distribution.
- Fast computation.
- Minimize collisions.
What are Collisions in Hashing? How are they handled?
Answer:
A collision occurs when two keys map to the same index.
Collision Resolution Techniques:
- Chaining: Store multiple values at same index using linked list.
- Open Addressing: Find another empty slot (linear probing, quadratic probing, double hashing).
Explain Linear Probing.
Answer:
- If collision occurs at index
i, check(i+1), (i+2)…until empty slot found. - Problem: Primary clustering.
Explain Quadratic Probing.
Answer:
- Instead of checking next slot, jump by squares →
(i+1²), (i+2²), (i+3²)…. - Reduces clustering.
What is Double Hashing?
Answer:
- Uses a second hash function to decide step size in probing.
- Formula:
(hash1(key) + i*hash2(key)) % table_size.
What are Load Factor and Rehashing?
Answer:
- Load Factor (α): Ratio of number of elements / table size.
- When load factor exceeds threshold (e.g., 0.75), rehashing doubles table size & re-inserts all elements.
What is the difference between HashMap and HashTable in Java?
| HashMap | HashTable |
|---|---|
| Not synchronized | Synchronized (thread-safe) |
Allows one null key |
Doesn’t allow null keys |
| Faster (no overhead) | Slower due to sync |
What are the applications of Hashing?
- Database indexing.
- Password storage.
- Caching (LRU, Redis).
- Symbol tables in compilers.
- Search engines.
Implement a simple Hash Table using Python (with Chaining).
class HashTable:
def __init__(self, size=10):
self.size = size
self.table = [[] for _ in range(size)]
def _hash(self, key):
return hash(key) % self.size
def insert(self, key, value):
index = self._hash(key)
for pair in self.table[index]:
if pair[0] == key:
pair[1] = value
return
self.table[index].append([key, value])
def get(self, key):
index = self._hash(key)
for pair in self.table[index]:
if pair[0] == key:
return pair[1]
return None
Tree Data Structure Interview Questions
Trees are one of the most important DSA topics for coding interviews (Amazon, Google, Microsoft love them) — especially BSTs, heaps, and traversals.
What is a Tree Data Structure?
Answer:
- A tree is a non-linear hierarchical data structure consisting of nodes.
- Each tree has:
- Root (top node)
- Edges (connections)
- Children (sub-nodes)
- Leaf nodes (no children)
What is the difference between Binary Tree and Binary Search Tree (BST)?
| Binary Tree | Binary Search Tree |
|---|---|
| Each node has ≤2 children | Each node has ≤2 children |
| No specific order | Left < Root < Right |
What are Tree Traversals?
Answer:
Tree traversals are ways of visiting all nodes:
- Inorder (L → Root → R) → BST gives sorted order.
- Preorder (Root → L → R) → Used for creating copies.
- Postorder (L → R → Root) → Used for deleting tree.
- Level Order (BFS) → Uses queue.
Implement Inorder Traversal of a Binary Tree (Python).
class Node:
def __init__(self, val):
self.val, self.left, self.right = val, None, None
def inorder(root):
if root:
inorder(root.left)
print(root.val, end=" ")
inorder(root.right)
What is the Height of a Tree?
Answer:
- The height of a tree is the length of the longest path from root to a leaf.
- Formula:
height = 1 + max(height(left), height(right)).
What is the Diameter of a Tree?
Answer:
- The longest path between any two nodes in the tree.
- Found using DFS.
What is a Balanced Binary Tree?
Answer:
- A binary tree where the height difference of left and right subtrees ≤1 for every node.
- Example: AVL Tree.
What is an AVL Tree?
Answer:
- A self-balancing BST.
- Balance Factor (BF) = Height(Left) – Height(Right) ∈ {-1, 0, 1}.
- Uses rotations (LL, RR, LR, RL) to stay balanced.
What is a Heap?
Answer:
- A complete binary tree with heap property:
- Max Heap: Parent ≥ Children.
- Min Heap: Parent ≤ Children.
- Used in priority queues, heapsort.
How do you implement Heap using Arrays?
Answer:
- Store root at index
0. - For node
i:- Left child =
2i+1 - Right child =
2i+2 - Parent =
(i-1)/2
- Left child =
What is a Trie (Prefix Tree)?
Answer:
- A tree-like DS used for storing strings.
- Each node represents a character.
- Applications: Autocomplete, spell check, search engines.
How do you find Lowest Common Ancestor (LCA) of two nodes in a BST?
Answer:
- If both nodes < root → LCA in left subtree.
- If both nodes > root → LCA in right subtree.
- Else root = LCA.
How do you check if a Binary Tree is a BST?
Answer:
- Inorder traversal should give sorted order.
- Or recursively check if node lies within valid range.
What is the difference between B-tree and B+ tree?
| B-tree | B+ tree |
|---|---|
| Keys + data stored in all nodes | Data only in leaf nodes |
| Slower range queries | Faster range queries |
| Used in databases | Used in file systems |
What are the applications of Trees?
- File system hierarchy.
- Database indexing (B-trees).
- Compilers (parse trees).
- AI (decision trees).
- Networking (routing tables).
Graph Interview Questions
Graphs are must-know for interviews (Google, Facebook, Microsoft love BFS/DFS, shortest paths, and graph modeling).
What is a Graph?
Answer:
A graph is a non-linear data structure consisting of:
- Vertices (nodes)
- Edges (connections between nodes)
Types of Graphs:
- Directed vs Undirected
- Weighted vs Unweighted
- Cyclic vs Acyclic
How do you represent a Graph in memory?
Answer:
- Adjacency Matrix: 2D array,
O(V²)space. - Adjacency List: Array of linked lists,
O(V+E)space. - Edge List: List of edges.
What is BFS (Breadth First Search)?
Answer:
- Traversal level by level (uses queue).
- Time Complexity: O(V+E).
from collections import deque
def bfs(graph, start):
visited, q = set(), deque([start])
while q:
node = q.popleft()
if node not in visited:
print(node, end=" ")
visited.add(node)
q.extend(graph[node] - visited)
What is DFS (Depth First Search)?
Answer:
- Traversal depth first (uses stack/recursion).
- Time Complexity: O(V+E).
def dfs(graph, node, visited=set()):
if node not in visited:
print(node, end=" ")
visited.add(node)
for nei in graph[node]:
dfs(graph, nei, visited)
How do you detect a cycle in a graph?
Answer:
- Directed Graph: Use DFS + recursion stack.
- Undirected Graph: Use DFS + parent tracking.
- Union-Find: Works for both.
Explain Dijkstra’s Algorithm.
Answer:
- Finds shortest path from source to all vertices (non-negative weights).
- Uses priority queue.
- Time Complexity: O((V+E) log V).
What is Bellman-Ford Algorithm?
Answer:
- Finds shortest paths even with negative weights.
- Detects negative weight cycles.
- Time Complexity: O(VE).
What is a Minimum Spanning Tree (MST)?
Answer:
- A spanning tree that connects all vertices with minimum edge weight.
- Algorithms:
- Kruskal’s Algorithm → Greedy, uses sorting + Union-Find.
- Prim’s Algorithm → Greedy, uses priority queue.
What is Topological Sorting?
Answer:
- Ordering of vertices in a Directed Acyclic Graph (DAG) such that for every edge
u → v,ucomes beforev. - Implemented using DFS or Kahn’s Algorithm.
- Applications: Task scheduling, compiler dependency resolution.
What are real-life applications of Graphs?
- Social networks (friend connections).
- Google Maps (shortest path).
- Web crawling (pages as nodes, links as edges).
- Recommendation systems.
- Network routing.
Advanced Data Structure Interview Questions
What is a Trie (Prefix Tree) and where is it used?
Answer:
- A tree-like data structure that stores strings character by character.
- Each node represents a prefix.
- Applications: Autocomplete, spell check, IP routing, search engines.
- Time Complexity (search/insert): O(L) where L = word length.
What is a Segment Tree? Why is it used?
Answer:
- A binary tree used for answering range queries (like sum/min/max) efficiently.
- Each node represents a segment (range) of the array.
- Time Complexity:
- Query: O(log n)
- Update: O(log n)
- Application: Range minimum/maximum queries in competitive programming.
What is a Fenwick Tree (Binary Indexed Tree)?
Answer:
- A DS that provides efficient prefix sum queries and updates.
- Time Complexity: O(log n) for both.
- Application: Cumulative frequency tables, range queries.
What is a Disjoint Set (Union-Find)?
Answer:
- A DS that keeps track of disjoint sets (non-overlapping groups).
- Supports:
- Find(x): Find which set x belongs to.
- Union(x, y): Merge two sets.
- Optimizations: Path compression + union by rank → near O(1).
- Applications: Kruskal’s MST, cycle detection in graphs.
What is a Red-Black Tree?
Answer:
- A type of self-balancing BST where each node has an extra bit (red/black).
- Properties:
- Root is black.
- Red node → children are black.
- Longest path ≤ 2 × shortest path.
- Applications: Used in Java
TreeMap, C++ STLmap&set.
What is a Splay Tree?
Answer:
- A self-adjusting BST where recently accessed elements move closer to root (splaying).
- Applications: Caching, memory allocators.
What is a Skip List?
Answer:
- A probabilistic DS that allows fast search, insert, delete in O(log n).
- Works like a linked list with multiple levels (fast-forward links).
- Used in in-memory DBs like Redis.
What is a K-D Tree (k-dimensional tree)?
Answer:
- A space-partitioning DS for organizing points in k-dimensional space.
- Applications: Range searches, nearest neighbor search (AI, computer vision).
What is a Suffix Tree and Suffix Array?
Answer:
- Suffix Tree: Compressed trie of all suffixes of a string.
- Suffix Array: Sorted array of all suffixes.
- Applications: Pattern matching, bioinformatics (DNA sequencing), search engines.
What is the difference between B-Tree and B+ Tree?
| B-Tree | B+ Tree |
|---|---|
| Keys & data in all nodes | Data only in leaf nodes |
| Slower range queries | Faster range queries |
| More flexible | Optimized for disk access |
- Application: Database indexing, file systems.
Real-Life Applications of Data Structures
Understanding real-world applications of DS helps answer conceptual interview questions like “Where do we use this in real life?”
1. Arrays
- Storing and accessing sequential data (images, database records).
- Used in CPU scheduling (ready queue).
2. Linked Lists
- Implementing dynamic memory allocation.
- Undo/Redo in text editors.
- Music/video playlists.
3. Stacks
- Browser back/forward navigation.
- Function call stack in programming languages.
- Undo feature in editors.
4. Queues
- CPU task scheduling (Round Robin).
- Printer job scheduling.
- Customer service systems.
5. Hashing
- Password storage and verification.
- Database indexing.
- Caching (Redis, Memcached).
6. Trees
- File system hierarchy.
- Database indexing (B-Trees, B+ Trees).
- Compilers (syntax trees).
7. Graphs
- Social networks (friend suggestions, LinkedIn connections).
- Google Maps (shortest path, GPS navigation).
- Web page ranking (PageRank algorithm).
Most Common Mistakes in DS Interviews
Many candidates lose out due to avoidable mistakes.
- Not analyzing time complexity
→ Always state Big-O after writing code. - Ignoring space complexity
→ Interviewers check memory trade-offs too. - Jumping straight to coding
→ Discuss your approach and edge cases first. - Not handling edge cases
→ Examples: empty array, single node tree, negative numbers. - Forgetting to optimize
→ Brute force works but interviewers expect optimization. - Weak communication
→ Think aloud; explain reasoning as you code.
Conclusion + Preparation Tips
Data structures are the foundation of coding interviews. To crack them in 2025:
- Revise Core Concepts: Arrays, Linked Lists, Stacks, Queues, Trees, Graphs, Hashing.
- Practice Coding: Use LeetCode, HackerRank, Codeforces for daily problem-solving.
- Analyze Time/Space: Always discuss complexity.
- Mock Interviews: Practice explaining your thought process.
- Stay Consistent: Solve 2–3 DS problems daily until interviews.
Remember: Companies don’t just test coding speed, they test problem-solving ability, clarity of thought, and communication.
Other Resources which may help to clear job interview
Things to Do Before During First Job Interview Tips & Checklist
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HTML Interview Questions and Answers
150+ Killer Core Java Interview Questions and Answers (Crack Any Job)
100+ Powerful React Interview Questions and Answers (2025) [From Freshers to FAANG]
FAQs
How many data structure questions should I practice for interviews?
At least 150–200 problems across arrays, linked lists, trees, graphs, DP.
Which is the most important data structure for coding interviews?
Arrays, Linked Lists, Trees, and Hashing → These appear in 80% of interviews.
Are data structures enough to crack FAANG interviews?
No. You also need algorithms (sorting, searching, DP, greedy) + system design for senior roles.
Which programming language is best for data structure interviews?
C++, Java, Python are most popular. Choose one you’re comfortable with.
How long does it take to prepare for DS interviews?
- For freshers: 3–4 months (daily practice).
- For experienced: 6–8 weeks (revision + mock interviews).