10 Advanced Top DSA Interview Questions with Answers: Part-2

Following up on our basic guide, this second installment of Top DSA Interview Questions with Answers dives into complex structures like Graphs, Tries, and the logic of Dynamic Programming. If you want to crack interviews at companies like Google, Amazon, or Microsoft, mastering these concepts is the ultimate requirement.

11. What is the difference between BFS and DFS?

Graph traversal is a core topic in any technical interview.

• BFS (Breadth-First Search): Explores all neighbor nodes at the present depth before moving to nodes at the next depth level. It uses a Queue data structure.
• DFS (Depth-First Search): Starts at the root and explores as far as possible along each branch before backtracking. It uses a Stack (or recursion).
• Key Use Case: BFS is ideal for finding the shortest path in an unweighted graph, while DFS is great for cycle detection and topological sorting.

12. What is a Heap?

A Heap is a specialized, complete binary tree.

• Max-Heap: The value of the parent node is always greater than or equal to the values of its children. The root is the maximum element.
• Min-Heap: The value of the parent node is less than or equal to its children. The root is the minimum element.
• Why use it? Heaps allow for O(1) access to the max/min element and O(log n) insertion, making them perfect for Priority Queues.

13. What is a Trie?

A Trie (pronounced “try”), also known as a prefix tree, is an ordered tree data structure used to store a dynamic set or associative array where the keys are usually strings.

• Functionality: Each node represents a single character of a word.
• Real-world Apps: Autocomplete in search engines, spell-checkers, and IP routing.

14. What is a Graph?

A Graph is a non-linear data structure consisting of Vertices (Nodes) and Edges (Lines) that connect these vertices.

• Representation: Graphs can be represented using an Adjacency Matrix (2D array) or an Adjacency List (array of lists).
• Usage: Modelling social networks, Google Maps (pathfinding), and recommendation engines.

15. Difference between Directed and Undirected Graph?

• Directed Graph (Digraph): Edges have a specific direction. If there is an edge from A to B, you cannot travel from B to A unless another edge exists. (Example: Twitter followers).
• Undirected Graph: Edges are bidirectional. An edge between A and B means you can move both ways. (Example: Facebook friends).

16. Time Complexity: Arrays vs. Linked Lists

Interviewers love to test your knowledge of efficiency.

17. What is Recursion?

Recursion is a programming technique where a function calls itself to solve a larger problem by breaking it down into smaller, manageable sub-problems.

• Danger: Without a proper exit strategy, recursion leads to a StackOverflowError.
• Common Problems: Tower of Hanoi, Factorial calculation, and Tree traversals.

18. What are Base Case and Recursive Case?

Every recursive function must have two parts:

1. Base Case: The condition under which the function stops calling itself.
2. Recursive Case: The logic where the function calls itself with a modified argument, moving closer to the base case.

19. What is Dynamic Programming (DP)?

Dynamic Programming is an algorithmic technique used to solve complex problems by breaking them into overlapping subproblems.

• The Key: It follows the principle of “storing, not recomputing.” If you’ve solved a subproblem once, you save the answer for future use.
• Classic Example: Calculating Fibonacci numbers efficiently.

20. Difference between Memoization and Tabulation?

These are the two primary ways to implement DP:

• Memoization (Top-Down): You start with the main problem and break it down. You use recursion and a cache (like a Hashmap) to store results.
• Tabulation (Bottom-Up): You start by solving the smallest subproblems first and fill up a table (usually an array) iteratively until you reach the main problem.

Double Tap ♥️ For Part-3