What Coding Interviews Actually Test
Coding interviews test three things: problem-solving approach, communication, and code quality. They are not testing whether you've memorized the optimal solution to 300 LeetCode problems. The interviewer wants to see how you think when you're stuck, whether you ask clarifying questions, whether you can break a problem down, whether you communicate your thought process, and whether your code is readable. A slightly suboptimal solution with excellent communication beats a perfect solution written in silence every time.
Data Structures You Must Know
Every coding interview draws from a small set of data structures. Understanding how each one works and when to use it covers the vast majority of interview problems.
| Structure | Key Operations | Common Use Cases |
|---|---|---|
| Array | Access O(1), search O(n) | Most problems, sliding window |
| Hash Map/Object | Get/Set O(1) | Frequency counting, caching, lookup |
| Stack | Push/Pop O(1) | Balanced parentheses, undo/redo, DFS |
| Queue | Enqueue/Dequeue O(1) | BFS, task scheduling |
| Linked List | Insert O(1), Search O(n) | Pointer manipulation problems |
| Binary Tree | Search O(log n) | Hierarchical data, traversal problems |
The 10 Problem Patterns That Appear Most Often
- Two Pointers — two indices moving through an array, often toward each other. Used for: finding pairs, reversing arrays, palindrome checks.
- Sliding Window — a window of fixed or variable size that moves through an array. Used for: maximum subarray, substring problems.
- Hash Map for O(1) Lookup — trade space for time by storing values in a hash map. Used for: two sum, anagrams, frequency counts.
- BFS (Breadth-First Search) — level-by-level traversal using a queue. Used for: shortest path, level-order tree traversal.
- DFS (Depth-First Search) — deep traversal using recursion or a stack. Used for: tree/graph traversal, backtracking.
- Binary Search — eliminate half the search space each iteration. Used for: sorted array search, finding boundaries.
- Dynamic Programming — break problems into overlapping subproblems and cache results. Used for: Fibonacci, knapsack, longest subsequence.
- Greedy — make the locally optimal choice at each step. Used for: interval scheduling, coin change (certain variants).
- Recursion/Backtracking — explore all options and undo choices that don't work. Used for: permutations, combinations, N-Queens.
- Monotonic Stack — maintain a stack in sorted order. Used for: next greater element, stock prices.
The Practice Schedule That Works
Start on LeetCode. Set a timer for 20 minutes per problem. If you can't solve it in 20 minutes, read the solution and understand it — don't brute-force your way through frustration. The goal is to recognize patterns, not to eventually think of the solution through sheer willpower.
Week 1–2: Arrays and Hash Maps (Easy problems). Week 3–4: Strings and Two Pointers (Easy–Medium). Week 5–6: Trees and Recursion (Medium). Week 7–8: Dynamic Programming (Medium). Ongoing: Mixed practice with a timer, simulating real interview conditions. Do at least 3 mock interviews before your first real one.