Which Data structure Interview Questions to prepare for experienced engineers?

Preparing for data structure interview questions as an experienced engineer requires a deeper understanding of complex data structures, efficient algorithms, and optimization strategies. Here’s a guide to help you focus on advanced topics that often come up in interviews for experienced roles, along with some recommended resources to enhance your preparation.

1. Arrays and Strings

Even though arrays and strings are fundamental, interview questions for experienced candidates tend to focus on more complex operations and optimizations.

Key Concepts

• Subarray Problems: Finding maximum or minimum sum subarrays, sliding window techniques, and prefix sums.
• String Manipulation: Pattern matching, palindromic substrings, longest common substring, and substring search algorithms.
• Matrix Operations: Rotating matrices, finding paths in matrices, and using matrices in dynamic programming problems.

Example Questions

• Find the maximum sum of a subarray using Kadane’s Algorithm.
• Determine if a string can be rearranged into a palindrome.
• Rotate a matrix by 90 degrees clockwise in place.

2. Linked Lists

Questions on linked lists for experienced engineers often involve custom operations or combining data structures to solve complex problems.

Key Concepts

• Cycle Detection: Using fast and slow pointers to detect cycles (Floyd’s Cycle-Finding Algorithm).
• Reverse Operations: Reversing linked lists or parts of them.
• Complex List Structures: Solving problems with linked lists that have additional pointers, like flattening a multilevel linked list.

Example Questions

• Detect a cycle in a linked list and return the node where the cycle begins.
• Merge two sorted linked lists without using extra space.
• Reverse a linked list in groups of k.

3. Stacks and Queues

For experienced roles, stack and queue questions usually involve using these data structures in conjunction with others to implement complex functionalities.

Key Concepts

• Monotonic Stack/Queue: Used to solve range-based maximum or minimum problems.
• Deque Operations: Applying deque for sliding window maximum/minimum.
• Custom Stack/Queue Implementations: Implementing LRU cache using a queue or stack, implementing a stack with a min or max function.

Example Questions

• Design a stack that supports push, pop, top, and retrieving the minimum element in constant time.
• Find the maximum element in every subarray of size k using a deque.
• Implement an LRU (Least Recently Used) Cache.

4. Trees and Graphs

For experienced engineers, tree and graph questions often focus on complex traversals, dynamic programming on trees, and graph-based optimizations.

Key Concepts

• Binary Trees: Balanced binary trees, binary search tree operations, and traversals (in-order, pre-order, post-order).
• Dynamic Programming on Trees: Solving problems like finding the diameter of a tree, the longest path, or subtree-related problems.
• Graph Algorithms: Depth-first search (DFS), breadth-first search (BFS), Dijkstra's algorithm, Bellman-Ford algorithm, and A* algorithm for pathfinding.

Example Questions

• Given a binary tree, find the lowest common ancestor (LCA) of two nodes.
• Find the shortest path between two nodes in an unweighted graph.
• Implement a solution to detect cycles in a directed graph.

5. Heaps and Priority Queues

These data structures are commonly used in scenarios requiring efficient retrieval of minimum or maximum elements, and for problems involving scheduling or order-based retrieval.

Key Concepts

• Heap Operations: Insertion, deletion, and extracting the maximum or minimum efficiently.
• Applications: Finding the k largest or smallest elements in a dataset, merging k sorted lists.
• Indexed Heaps: Using a priority queue with custom sorting or indexing.

Example Questions

• Find the k largest elements in an array using a min-heap.
• Merge k sorted linked lists into a single sorted linked list using a priority queue.
• Implement a median finder using two heaps.

6. Hash Tables and Hashing

Experienced-level questions involving hash tables are often combined with other data structures to solve complex lookups and handle collisions.

Key Concepts

• Collision Handling: Implementing hash functions with chaining or open addressing techniques.
• Applications of Hashing: Counting frequencies, finding duplicates, and efficient lookups.
• Advanced Hash Techniques: Using hash maps with rolling hash algorithms for substring search.

Example Questions

• Find the first non-repeating character in a string using a hash map.
• Given an array of integers, find all pairs that sum up to a specific target using hashing.
• Implement a data structure with insert, delete, and getRandom functions, all in constant time.

7. Advanced Data Structures

For experienced roles, interviewers often expect familiarity with specialized data structures that can solve specific types of problems more efficiently.

Key Concepts

• Trie: Often used for efficient retrieval of words in dictionaries, autocomplete systems, and prefix-based searches.
• Segment Tree/Fenwick Tree: Used for range queries and dynamic updates.
• Disjoint Set (Union-Find): Essential for problems involving connected components, detecting cycles in undirected graphs, and network connectivity.

Example Questions

• Implement a trie with insert, search, and startsWith functions.
• Use a segment tree to solve range minimum query problems.
• Apply union-find to detect cycles in an undirected graph.

8. Dynamic Programming (DP)

Experienced engineers are often asked to solve problems using dynamic programming techniques, especially those involving optimal substructure and overlapping subproblems.

Key Concepts

• DP on Arrays: Common problems like subset sum, longest increasing subsequence, and maximum subarray sum.
• DP on Strings: Longest common subsequence, edit distance, and palindrome partitioning.
• DP on Trees/Graphs: Problems involving paths, subtree calculations, and maximizing/minimizing values within constraints.

Example Questions

• Solve the longest increasing subsequence problem in O(n log n) time.
• Find the minimum edit distance between two strings.
• Calculate the maximum path sum in a binary tree.

Recommended Resources for Advanced Preparation

1. DesignGurus.io

Grokking Data Structures & Algorithms for Coding Interviews
This course covers essential data structures and algorithms with practical problem-solving exercises, focusing on efficiency and optimization.

Grokking the Coding Interview: Patterns for Coding Questions
This course emphasizes recurring coding patterns, helping you recognize the underlying pattern in complex problems—especially useful for senior-level interview questions.

Coding Mock Interview
Mock interviews with ex-FAANG engineers that simulate real-world interview scenarios, providing personalized feedback on your problem-solving approach.

2. LeetCode

LeetCode provides a comprehensive collection of problems specifically tagged with advanced topics like "trie," "segment tree," and "dynamic programming." It also has a premium subscription with company-specific questions and a mock interview feature.

• Access: LeetCode

3. GeeksforGeeks

GeeksforGeeks is ideal for learning data structures and algorithms from scratch. It also covers advanced concepts, provides problem-solving techniques, and offers solutions with step-by-step explanations.

• Access: GeeksforGeeks

4. Books

"Elements of Programming Interviews" by Adnan Aziz, Tsung-Hsien Lee, and Amit Prakash
This book is perfect for experienced candidates, offering a blend of coding, algorithmic problem-solving, and system design.

"The Algorithm Design Manual" by Steven S. Skiena
This book emphasizes algorithmic design with a focus on real-world applications, including many examples of advanced data structures and optimization strategies.

Conclusion

For experienced engineers, preparing for data structure interview questions involves going beyond the basics and mastering advanced concepts and optimizations. Focus on complex structures like tries, segment trees, and union-find, and practice questions involving advanced dynamic programming, graph algorithms, and custom data structure implementation.

Utilize DesignGurus.io courses for a structured learning path, complemented by resources like LeetCode for practice, GeeksforGeeks for in-depth explanations, and classic algorithm books. This holistic approach will ensure you’re prepared to tackle challenging interview questions with confidence.