The FAANG Coding Challenge Vault

In this coding challenge, you're given two input strings, word1 and word2, and your task is to merge them in an alternating fashion.

That means you will take one character from word1, then one from word2, and continue alternating back and forth. If one word is longer than the other, simply append the remaining characters from the longer word to the end of the merged result.

Example

swiftCopyEditword1 = "abc"

word2 = "xyz"

Output: "axbycz"

Constraints

• Both strings have a length between 1 and 100.

• Strings contain only lowercase English letters.

Learning Objectives

• Practice looping logic and conditional bounds checking

• Convert a String to a [Character] array for index-based access

• Learn how to build strings dynamically using String.append()

Your Task

Implement the mergeAlternately(_:_:) method so that it returns the correctly merged string. Pay attention to edge cases where one string is shorter than the other.

In this lesson, you'll learn two foundational algorithm techniques for solving array-based problems:

• the brute-force approach, and

• the two-pointer technique.

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  Part 1 – Finding Pairs That Sum to a Target

We begin by solving a classic problem:

Given an array of integers, find all pairs of numbers that add up to 10.

You'll see two versions of the solution:

1. Brute Force Approach (O(n²)) – Using nested for loops to compare all possible pairs.

2. Two-Pointer Technique (O(n)) – Leveraging a sorted array and moving two pointers inward based on the current sum.

These two methods are great for understanding how performance can dramatically improve with the right algorithm.

Part 2 – Finding Triplets That Sum to Zero

We then transition to a more challenging problem:

Given an array of integers, return all unique triplets [nums[i], nums[j], nums[k]]such that the sum is zero.

You'll implement two approaches:

1. Brute Force Triple Loop (O(n³)) – A straightforward but inefficient method that tries all combinations.

2. Optimized Two-Pointer Strategy (O(n²)) – After sorting the array, we fix one number and use two pointers to find pairs that complete the triplet.

You’ll also learn how to:

• Avoid duplicates in your results,

• Implement efficient sorting and pointer-based search strategies,

• Understand the difference between time complexities.

In this lesson, we’ll build a binary tree from an array and explore how to extract the right side view— the nodes you would see if you looked at the tree from the right.

You’ll learn:

• How to construct a tree from a level-order array (including nil values).

• How to use Depth-First Search (DFS) to traverse the tree.

• Why we visit the right child before the left to capture the visible node at each level.

In the end, the result will be a clean list of the rightmost nodes — one from each level of the tree.

This is a practical example of tree traversal used in UI layouts, visibility logic, and summarizing hierarchical data.

Welcome to this quick and clear explanation of palindromic subsequences! In this video, you'll learn what a palindrome is, what makes a subsequence different from a substring, and how they come together in one of the most interesting string manipulation problems in coding.

We’ll break down:

• What is a palindrome?

• What is a subsequence (and how is it different from a substring)?

• How to identify palindromic subsequences in a string

• Real coding examples to solidify your understanding

Whether you're prepping for coding interviews or brushing up on your Swift and algorithm fundamentals, this video will give you the clarity and confidence to tackle these problems head-on.

In this video, we break down a fun and challenging algorithm problem: ranking teams based on votes! ?

You’ll learn how to:

• Understand a ranking system where each letter represents a team

• Interpret voter input where each string is a full team ranking

• Apply a smart sorting strategy using vote positions and tie-breaking rules

Perfect for beginners and intermediate coders looking to sharpen their skills with string manipulation, custom sorting, and logic building.

In this video, you'll learn how to solve a classic algorithm problem: spiral order traversal of a matrix ?

We break it down step-by-step:

• What spiral order means

• How to use boundary pointers to navigate

• Looping logic to collect all elements

• Key edge cases to watch for

Perfect for coding interview prep or strengthening your 2D array skills. Clear visuals and a simple strategy make this an easy one to follow!

In this video, we tackle a popular string manipulation problem: reorganizing characters so that no two adjacent characters are the same. ?

You'll learn:

• How to count character frequencies

• When a solution is possible — and when it's not

• A greedy approach using a max heap (priority queue) strategy

• Tips to avoid common pitfalls

Perfect for coding interviews, algorithm practice, or improving your Swift skills!

In this lesson, we walk through how to group anagrams from an array of strings using a Swift extension on Array where Element == String.

You'll learn:

• What an anagram is and how to detect one

• How to use sorted strings as keys in a dictionary to group words

• How to build a clean and reusable method using an extension

• Debugging tips and how to fix common Swift type inference errors

• Best practices for returning a 2D array ([[String]]) from a dictionary of grouped anagrams

This is a great problem to solidify your understanding of dictionaries, array extensions, and type safety in Swift.

Whether you're prepping for interviews or improving your Swift fluency, this solution is practical and elegant!

In this lesson, we explored a classic grid traversal problem: counting the number of islands in a 2D binary matrix.

Key Concepts Covered:

• A 2D grid contains only '1's (land) and '0's (water).

• An island is a group of '1's connected horizontally or vertically (not diagonally).

• The task is to determine how many separate land masses (islands) exist in the grid.

What You Learned:

• How to recognize and count connected components in a grid.

• Why diagonal connections don’t count as part of an island.

• How to use Depth-First Search (DFS) or Breadth-First Search (BFS) to explore and mark visited land.

• A practical way to approach real-world problems like map regions, flood fill, or network connectivity.

This lesson helps strengthen your understanding of matrix traversal, recursion or queue usage, and problem decomposition — skills essential in both interviews and real-world engineering tasks.

In this video, we break down the popular coding challenge: finding the maximum path sum in a binary tree ?

You'll learn:

• What a "path" means in a tree (and how it’s not just root-to-leaf)

• How paths can go through any node — not just the root

• How to calculate the best sum using recursion

• Why handling negative values and avoiding reused nodes is key

Perfect for coding interviews and strengthening your recursion + tree traversal skills!

This function calculates the diameter of a binary tree, which is defined as the longest path between any two nodes in the tree, measured by the number of edges.

Key points:

• The path doesn’t have to pass through the root.

• It recursively calculates the depth of each subtree.

• At each node, it updates the maximum diameter found by summing the left and right subtree depths.

• The function returns the maximum number of edges across all possible paths in the tree.

Perfect for understanding tree traversal, recursion, and how to track global state while navigating a recursive structure.

Video Title: Find the Number of Subarrays with Product Less Than K – Explained!

Description:

In this video, we break down how to solve a popular sliding window problem: counting contiguous subarrays with a product less than a given value k.

You’ll learn:

• What "contiguous subarrays" really means

• How to use the sliding window technique to avoid brute force

• Why we divide out elements to shrink the window when the product gets too big

• The logic behind counting subarrays ending at each position

Perfect for coding interviews or practicing efficient array traversal!

In this video, we break down the concept of an LRU (Least Recently Used) Cache, a popular system design and coding interview question.

You'll learn:

• What an LRU cache is and how it works

• Why it's used to manage limited memory or storage

• How to implement get and put operations in O(1) time

Whether you're prepping for interviews or learning how caching works under the hood, this lesson simplifies the logic and architecture of a powerful algorithmic tool.

In this video, we explain how to check for and fix unmatched parentheses in a string that contains both letters and braces like ( and ).

You'll learn:

• What makes a string's parentheses "valid"

• How to track opening and closing braces using a stack

• How to remove the minimum number of unmatched parentheses

• Real examples and visual breakdowns to make it click

Perfect for beginners learning string processing, or interview prep involving balanced symbols.

Well, I've covered everything from anagrams and palindromes to subsequences, and almost everything in between. This will give you a major advantage in the field as you pursue opportunities in iOS mobile development. Study this thoroughly, and don't stop grinding for the opportunity you want.