Technical Interview Prep — The Last-Minute Engineer's Cheat Sheet

You've got 72 hours until your technical interview. Grinding 500 LeetCode hards is mathematically impossible. You need a targeted strategy that prioritizes patterns over memorization.

As someone who sits on the other side of the desk, let me tell you what actually moves the needle. It's not about writing flawless, bug-free code on the first try. It's about demonstrating structured thinking, pattern recognition, and effective communication.

Triage: What to Study Based on Time

If you have 3 days, focus entirely on the top 15 Data Structures and Algorithms (DSA) patterns. Don't learn new algorithms; learn how to map problems to algorithms you vaguely know.

If you have 1 week, add 2 days of mock interviews. Practicing talking aloud is crucial.

If you have 2 weeks, spend the second week on domain-specific knowledge (e.g., React hooks for frontend, multithreading for backend).

The 15 Most-Asked DSA Patterns

Stop doing random problems. Recognize the pattern, and the code writes itself:

• Sliding Window: Used for arrays/strings when asked to find a subarray, substring, or max/min of a contiguous sequence. (e.g., Maximum sum subarray of size K).
• Two Pointers: Used for sorted arrays or linked lists. (e.g., Two Sum II, Container With Most Water).
• Fast & Slow Pointers: Used for cyclic data structures like linked lists or arrays. (e.g., Linked List Cycle, Find the Duplicate Number).
• Merge Intervals: Dealing with overlapping intervals. (e.g., Insert Interval, Merge Intervals).
• Cyclic Sort: Used for problems dealing with numbers in a given range. (e.g., Find the Missing Number).
• In-place Reversal of a LinkedList: Essential for manipulating lists without extra memory.
• Tree BFS & DFS: Master Level Order Traversal (BFS) and Pre/In/Post order traversals (DFS).
• Two Heaps: Used for median or percentile problems. (e.g., Find Median from Data Stream).
• Subsets: Used for permutations and combinations (Backtracking).
• Modified Binary Search: Not just for finding a number, but finding bounds or peaks in a rotated array.
• Top K Elements: Always involves a Heap. (e.g., Top K Frequent Elements).
• K-way Merge: Used when dealing with multiple sorted arrays.
• 0/1 Knapsack (Dynamic Programming): The foundation for many optimization problems.
• Topological Sort (Graphs): Used for scheduling or dependency problems. (e.g., Course Schedule).
• Trie (Prefix Tree): Used for string searches, autocomplete.

How to Think Aloud (The Part That Actually Matters)

Silence is the enemy of the technical interview. If I can't hear your thoughts, I can't grade your problem-solving skills, only your final code. Use this framework:

1. Clarify: Repeat the problem back. Ask about edge cases (empty inputs, negative numbers, duplicates).
2. Brute Force: State the naive solution immediately. "The easiest way is O(N^2) using nested loops..." Get it out of the way.
3. Optimize: Say, "But we can do better. Since the array is sorted, maybe two pointers? Or we could trade space for time with a Hash Map."
4. Code: Talk while you type. "I'm initializing a set here to track visited nodes..."
5. Test: Walk through your code with a small example input. This catches 90% of off-by-one errors before I have to point them out.

Language-Specific Gotchas

Pick one language and know its standard library inside out. Do not switch languages during prep.

• Python: Know `collections.deque` (for queues), `heapq` (for min/max heaps), and `defaultdict`. Understand that `string` concatenation in a loop is O(N^2).
• Java: Know `PriorityQueue`, `HashMap`, `ArrayList`, and `StringBuilder`. Be prepared to write lots of boilerplate.
• JavaScript/TypeScript: Remember that JS arrays are dynamic and `sort()` converts elements to strings by default (use `sort((a,b) => a-b)`). Understand Promises and Event Loop for frontend roles.