DSA Mastery

DSA Interview: FAANG Style Coding Challenges

1 Views Updated 5/4/2026

Architect DSA Interview

Congratulations. You have mastered the internals of computer science. In this final module, we summarize the 3 types of questions you will face at FAANG level interviews.

1. The "Scale Up" Question

Question: "You have a list of 1 billion IDs. How do you find the unique IDs in under 500MB of RAM?"

Architect Answer: "I wouldn't use a HashSet (too much RAM). I would use a Bloom Filter (probabilistic) or a Bitset (bit-map) if the IDs are numeric. This allows me to trade a tiny bit of accuracy for a 99% reduction in memory footprint."

2. The Algorithmic Trade-off

Question: "Why would you choose a slower algorithm on purpose?"

Architect Answer: "Reliability and Maintainability. For example, replacing a complex Recursive DP with a simple iterative loop might lose 5% speed but prevents **Stack Overflow crashes** and makes the code 10x easier for the dev team to debug at 3 AM. A senior architect optimizes for the **System**, not just the algorithm."

DSA MASTERY COMPLETE.

You are now equipped with the tools to solve any engineering problem. Use them wisely. Build something great.

DSA Mastery
1. Algorithmic Foundations
Big O Notation: Analyzing Time and Space Complexity Memory Management: Stack vs Heap in C# Recursion: The foundation of modern algorithms
2. Linear Data Structures
Arrays Deep Dive: Static vs Dynamic (List<T> Internals) Linked Lists: Singly, Doubly, and Circular Stacks and Queues: Implementing Undo/Redo & Message Buffers Hash Tables: Handling Collisions like a Pro
3. Non-Linear Data Structures
Binary Trees & BST: Searching at Log(N) speed Balanced Trees: AVL and Red-Black Trees Internals Heaps: Implementing a Priority Queue Tries (Prefix Trees): Optimizing Auto-complete features Graphs (Part 1): Representation (Matrix vs List) Graphs (Part 2): BFS vs DFS Traversal
4. Searching & Sorting
Binary Search: The power of Divide & Conquer Elementary Sorts: Bubble, Selection, and Insertion Merge Sort: Stable sorting for massive datasets Quick Sort: In-place sorting and Pivot selection Heap Sort: Leveraging the Priority Queue
5. Algorithmic Patterns
Sliding Window Pattern: Optimizing array performance Two Pointers Pattern: Reversing and Finding cycles Fast & Slow Pointers (Hare & Tortoise) Backtracking: Solving Sudoku and N-Queens
6. Dynamic Programming (DP)
Memoization vs Tabulation: Top-down vs Bottom-up The Knapsack Problem: 0/1 DP optimization Longest Common Subsequence (LCS) Matrix Chain Multiplication
7. Advanced Graphs & Interview
Dijkstra's Algorithm: Shortest path in weighted graphs Prim's and Kruskal's: Minimum Spanning Trees Disjoint Set Union (DSU) / Union-Find DSA Interview: FAANG Style Coding Challenges