You’re preparing for a data structures interview. What are some common questions you should expect?

Candidates must learn Prefix Sum Technique which can simplify problems where any kind of Array Sum is used. Prefix sum can be done using additional space or by modifying the existing list. If the array sum's are sequential we can make use of Carry Forward technique to avoid additional space. While dealing with subarrays, the most common technique is the contribution technique. There is a fixed amount of time each element can contribute to entire set of all possible subarrays. The weight of each of those contributions can be easier to calculate than to brute-force all the possible subarrays.

Alrighty, let's see want you should expect for arrays/lists. There is a gazillion of questions that could be asked here. So, it is super inefficient to try solving them all OR even memorizing. A better approach is to categorize the questions into buckets and identify the patterns between them. Array patterns include (but not limited to): - Cyclic sort - for arrays with values in range [1, n] - Two pointers - Sliding window - Hash maps - Some array questions will need this DS. - Binary search - for O(logn) search on arrays. A solid understanding of the patterns mentioned above will set you up for success when it comes to array questions. Note: Some questions might not fall in the patterns listed above. Take away: Study patterns!

In data structure interviews, candidates often tackle questions on arrays and lists, foundational structures in programming. Topics cover traversal, search, sorting, insertion/deletion, finding max/min elements, handling duplicates or missing elements, and working with subarrays/sublists. Additional strategies like two pointers, sliding window, prefix sum, and addressing circular or sparse arrays are common. Candidates also encounter challenges with array/list rearrangement, rotation/reversal, intersection with other structures, and dynamic programming. Mastery of these concepts is vital for success in array and list-related interview questions.

Stacks and queues are core data structures often explored in data structure interviews. Common stack questions include implementing it using arrays or linked lists, explaining LIFO, solving problems like expression evaluation or parenthesis balancing, and designing special stacks like those supporting minimum/maximum element retrieval. Queue questions involve implementations, FIFO concepts, circular queues, and real-life applications. Miscellaneous queries cover comparisons between stacks and queues, priority queues using heaps, and the role of stacks in recursion and function calls. Understanding these, along with their complexities, is crucial for interviews.

Stacks and queues are a very important topic. In my experience, there might not be a direct question about it, but some part of the question will include a stack or a queue usage. It is essential to be thorough with basic operations of stacks and queues.

Understanding and implementation of Stack and Queues using other Data Structures such as Arrays, List, Linked List (Double and Single). Understand Last-In-First-Out (LIFO) principle. Learn basic operations: push (add element), pop (remove element), peek (view top element), and isEmpty. Use stacks for tasks like expression evaluation, backtracking in algorithms, or maintaining function call stack. Understanding Queues, First-In-First-Out (FIFO) order.Master enqueue and dequeue operations. Implement common variations like priority queues or double-ended queues (Deque). Apply queues for tasks like breadth-first search (BFS), job scheduling, or buffer management.

Tree and graphs are most common topic for big companies interviews. Most of the questions will use these algorithms - tree traversal , DFS/ BFS algorithms. Depth-First Search (DFS) is a graph traversal algorithm that explores as far as possible along each branch before backtracking. We can implement DFS using a stack and Breadth-First Search (BFS) is a graph traversal algorithm that explores all the vertices in a graph level by level. We can implement BFS using a queue. Other important question in graph will evolve around topological sort like task scheduling, course prerequisites, detect cycle in graph

In data structure interviews, questions revolving around trees and graphs are prevalent. Tree-related inquiries often include implementing traversal algorithms such as Inorder, Preorder, and Postorder, managing Binary Search Trees (BSTs), checking for balance, finding Lowest Common Ancestors (LCAs), and traversing levels or paths. Graph inquiries may involve traversal methods like Depth-First Search (DFS) and Breadth-First Search (BFS), shortest path algorithms like Dijkstra's or Bellman-Ford, minimum spanning trees, topological sorting, cycle detection, connectivity checks, and graph coloring problems. Understanding these concepts and practicing problems is crucial for interview preparation.

Trees and graphs are frequent topics in DSA interviews and are often favorites among interviewers. It requires a solid understanding of various techniques for handling trees and graphs questions. One must learn Tree Traversal using DFS and BFS. Understand Binary Search Trees (BST) and the common operations include finding minimum/maximum elements, searching for a key, insertion, and deletion. Binary Tree Properties like Height vs Depth, Balanced vs. Unbalanced and Proper vs Complete vs Perfect Binary Tree.

Hashtables are also considered an important topic from the perspective of interviews. Most questions revolve around the Internal working of Hashmap and also concepts like Hashing, Handling collisions, Separate chaining, and Load factor. Some of the important questions for deeper understanding of concepts are: find a pair with given sum/difference, number of subarrays having given XOR, Longest Consecutive Subsequence(LCS), longest substring without repeat, palindrome substring queries, and find all subarrays with given sum.

Preparing for hash table questions in a data structures interview involves mastering several key techniques: Hashing Methods: Understand different hash functions for efficient key mapping, ensuring uniform distribution to minimize collisions. Basic Operations: Practice insertion, deletion, and search operations efficiently, considering collision scenarios and appropriate resolution methods. When to use a HashSet and when to use a HashTable is crucial for effective problem-solving. Understanding how the O(1) time complexity for basic operations like insertion, deletion, and accessing can be leveraged to improve the efficiency.