Tips for solving leetcode codings interview problems

If input array is sorted then
- Binary search
- Two pointers

If asked for all permutations/subsets then
- Backtracking

If given a tree then
- DFS
- BFS

If given a graph then
- DFS
- BFS

If given a linked list then
- Two pointers

If recursion is banned then
- Stack

If must solve in-place then
- Swap corresponding values
- Store one or more different values in the same pointer

If asked for maximum/minimum subarray/subset/options then
- Dynamic programming

If asked for top/least K items then
- Heap

If asked for common strings then
- Map
- Trie

Else
- Map/Set for O(1) time & O(n) space
- Sort input for O(nlogn) time and O(1) space

Let's understand how Arrays & Strings can be asked in coding interviews

Arrays and strings are the building blocks of most coding interview problems. They test your logic, optimization skills, and your ability to recognize patterns — and they pop up in everything from system design to algorithm rounds.

*1.1. Rotation*

You may be asked to rotate an array left or right by k positions, in-place and with O(1) space.

Example:

> Rotate [1, 2, 3, 4, 5] right by 2 → Output: [4, 5, 1, 2, 3]

It tests how well you manage array indices and edge cases like k > n.

*1.2. Sliding Window*

Used to reduce brute-force O(n²) solutions to O(n). Interviewers love this for problems around subarrays, substrings, or fixed windows.

*Example* :

> Find the max sum of a subarray of size 3 in [4, 2, 1, 7, 8, 1, 2, 8, 1, 0] → Output: 17

It's commonly used in anagram detection, maximum subarray sum, and longest substring without repeating characters.


*1.3. Two Pointers*

Two indices scanning the array — from start and end or moving in sync. Great for reducing space/time complexity.

Example:

> Given [1, 2, 4, 4] and target = 8, return true if two numbers sum up to target → Output: True (4+4)

Common interview problems:

- Reverse a string/array

- Check for palindrome

- Remove duplicates in-place

- Merge two sorted arrays


*1.4. Prefix Sum*

Precompute cumulative sums to answer range queries in O(1) instead of O(n).

Example:

> For nums = [1, 2, 3, 4, 5], find sum from index 1 to 3 quickly → Output: 9 (2+3+4)



*Popular problems:*

- Subarray sum equals k
- Range sum queries
- Balanced subarrays


React with ❤️ once you're ready for the next concept Linked Lists

Top 7 Coding Interview Concepts: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X/720

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Let's now move to next important concept asked in coding interviews: Linked Lists:

Linked Lists test your ability to handle pointers, edge cases, and memory efficiency. They show up in both beginner and advanced interview rounds.

2.1. Reverse a Linked List

Example:
Reverse this list:
1 → 2 → 3 → 4
Output: 4 → 3 → 2 → 1

Concept tested:
Rewiring the .next pointers — often asked with follow-ups like iterative vs. recursive solutions.

2.2. Detect Cycle in a Linked List

Example:
In 1 → 2 → 3 → 4 → 2 (back to second node), detect the cycle.

Solution:
Use Floyd’s Cycle Detection Algorithm (fast and slow pointers).

It tests how well you manage infinite loops and pointer traversal without modifying the list.

2.3. Merge Two Sorted Linked Lists

Example:
Merge 1 → 3 → 5 and 2 → 4 → 6
Output: 1 → 2 → 3 → 4 → 5 → 6

Concept tested:
Efficient pointer traversal with dummy nodes or recursion. A classic sub-task in linked list sorting.

2.4. Find the Middle of a Linked List

Example:
In 1 → 2 → 3 → 4 → 5 → 6, the middle node is 4.

Solution:
Fast and slow pointer — when the fast pointer reaches the end, the slow one is at the middle.

2.5. Remove N-th Node from End

Example:
Remove the 2nd node from the end of 1 → 2 → 3 → 4 → 5
Output: 1 → 2 → 3 → 5

Trick:
Create a gap of n between two pointers and move them together — when the first hits the end, the second is at the right spot.

You’ll see linked lists hidden in many real-world structures — like undo-redo functionality, LRU cache, or browser history stacks.


React with ❤️ once you're ready for the next concept Hashing & Maps

Top 7 Coding Interview Concepts: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X/720

ENJOY LEARNING 👍👍

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Let’s dive into the frequently asked coding interview topic now: Hashing & Maps

Hashing helps us access data quickly, making it a critical topic in coding interviews. HashMaps (or dictionaries) provide constant-time access and are used to solve problems like counting elements, finding duplicates, and mapping data efficiently.

3.1. Use Hash Maps for Fast Lookups

Example:
Check if two strings are anagrams.
Given s1 = "listen", s2 = "silent", are they anagrams?

Solution:
Use a hash map to count the frequency of characters in both strings and compare the counts.

Concept tested:
Efficient searching and counting with O(1) average time complexity.


3.2. Count Frequency of Elements

Example:
Given an array nums = [1, 2, 2, 3, 3, 3, 4], count the frequency of each element.

Solution:
Use a hash map to store the counts:
{1: 1, 2: 2, 3: 3, 4: 1}

It tests how well you can group data efficiently and manage it using hash-based structures.


3.3. Find Duplicates

Example:
Given arr = [4, 5, 6, 7, 5, 8], find the first duplicate.

Solution:
Use a hash map to track seen elements as you traverse the array. If you encounter an element already in the map, it’s a duplicate.

It tests your ability to solve problems with constant time lookups for duplicates.


3.4. Two Sum Problem

Example:
Given nums = [2, 7, 11, 15] and target = 9, return indices of the two numbers that add up to the target.

Solution:
Use a hash map to track the difference between the target and the current number. When you find a match, return the indices.

Concept tested:
Efficient search for pairs in a single pass with O(n) time complexity.


3.5. Implement LRU Cache

Example:
Design a cache that stores the most recently used items, evicting the least recently used when it exceeds its capacity.

Solution:
Use a hash map to store the cache and a doubly linked list to keep track of the order of usage. Combine both to make retrieval and eviction O(1).

This tests your ability to combine hash maps with other data structures like linked lists, and to implement efficient solutions with constraints.


Hash maps are frequently used in problems involving counting, grouping, and mapping, especially when you need to reduce time complexity from quadratic to linear.


React with ❤️ once you're ready for the next topic: Recursion & Backtracking

Top 7 Coding Interview Concepts: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X/720

Top 7 Python Concepts: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1452

ENJOY LEARNING 👍👍

General tips for coding interviews

Always validate input first. Check for inputs that are invalid, empty, negative, or different. Never assume you are given the valid parameters. Alternatively, clarify with the interviewer whether you can assume valid input (usually yes), which can save you time from writing code that does input validation.

Are there any time and space complexities requirements or constraints?

Check for off-by-one errors.

In languages where there are no automatic type coercion, check that concatenation of values are of the same type: int,str, and list.

After you finish your code, use a few example inputs to test your solution.

Is the algorithm supposed to run multiple times, perhaps on a web server? If yes, the input can likely be pre-processed to improve the efficiency in each API call.

Use a mix of functional and imperative programming paradigms:

🔹 Write pure functions as often as possible.
🔹 Use pure functions because they are easier to reason with and can help reduce bugs in your implementation.
🔹 Avoid mutating the parameters passed into your function, especially if they are passed by reference, unless you are sure of what you are doing.
🔹 Achieve a balance between accuracy and efficiency. Use the right amount of functional and imperative code where appropriate. Functional programming is usually expensive in terms of space complexity because of non-mutation and the repeated allocation of new objects. On the other hand, imperative code is faster because you operate on existing objects.
🔹 Avoid relying on mutating global variables. Global variables introduce state.
🔹 Make sure that you do not accidentally mutate global variables, especially if you have to rely on them.