Apple vs Snapchat: Interview Question Comparison

When preparing for technical interviews, company-specific question patterns matter. Apple and Snapchat, while both testing core data structures and algorithms, present distinct challenges in volume, difficulty distribution, and focus areas. Understanding these differences helps you allocate your preparation time strategically.

Question Volume and Difficulty

The sheer scale of Apple's question bank is its defining characteristic. With 356 questions cataloged, it dwarfs Snapchat's 99 questions. This volume reflects Apple's broader range of roles and teams, from iOS and macOS to services and silicon.

More telling is the difficulty distribution. Apple's questions are categorized as Easy (100), Medium (206), and Hard (50). This indicates a strong emphasis on medium-difficulty problems, which often require combining 2-3 core concepts. The significant number of easy questions suggests they may screen for fundamental fluency.

Snapchat's distribution is Easy (6), Medium (62), and Hard (31). The profile is starkly different: very few easy questions, a heavy majority of medium, and a substantial portion (nearly one-third) of hard problems. This signals an interview process that quickly moves to complex problem-solving, expecting candidates to handle challenging scenarios involving optimization or intricate logic.

Topic Overlap

Both companies heavily test Array, String, and Hash Table problems. These form the essential toolkit for most interview questions.

# Example Hash Table problem (Two Sum)
def two_sum(nums, target):
    seen = {}
    for i, num in enumerate(nums):
        complement = target - num
        if complement in seen:
            return [seen[complement], i]
        seen[num] = i
    return []

The key divergence is in the next layer of focus. Apple's list highlights Dynamic Programming (DP). Expect problems involving optimization, counting, or decision-making over sequences (strings, arrays) or grids.

# Example DP problem (Climbing Stairs)
def climb_stairs(n):
    if n <= 2:
        return n
    dp = [0] * (n + 1)
    dp[1], dp[2] = 1, 2
    for i in range(3, n + 1):
        dp[i] = dp[i-1] + dp[i-2]
    return dp[n]

Snapchat's data highlights Breadth-First Search (BFS). This points toward questions involving graphs, trees, level-order traversal, or finding shortest paths in unweighted grids—topics relevant to social networks and spatial features.

# Example BFS problem (Binary Tree Level Order)
from collections import deque
def level_order(root):
    if not root:
        return []
    result = []
    queue = deque([root])
    while queue:
        level_size = len(queue)
        level = []
        for _ in range(level_size):
            node = queue.popleft()
            level.append(node.val)
            if node.left:
                queue.append(node.left)
            if node.right:
                queue.append(node.right)
        result.append(level)
    return result

Which to Prepare for First

Start with Snapchat. Its smaller, more concentrated question set allows for efficient, targeted preparation. Mastering the core trio (Array, String, Hash Table) and then diving deeply into BFS and graph-related problems will cover a high percentage of their question pool. The high density of medium and hard problems means your practice should prioritize quality and depth of problem-solving over speed.

Once comfortable with Snapchat's pattern, transition to Apple. The larger volume requires broader coverage. Solidify your foundation in the core topics, then allocate significant time to Dynamic Programming, which is a frequent and challenging theme. The wide range of difficulties means you must be equally prepared for quick, fundamental checks and lengthy, multi-step design problems.

In short: use Snapchat's focused list to build depth in specific algorithms, then use Apple's extensive catalog to build breadth and stamina.

For more detailed question lists and patterns, visit the Apple and Snapchat pages: Apple Interview Questions, Snapchat Interview Questions.