Apple vs LinkedIn: Interview Question Comparison

When preparing for technical interviews, company-specific question patterns matter. Apple and LinkedIn, while both tech giants, present distinct interview landscapes in volume, difficulty distribution, and focus areas. Understanding these differences allows you to allocate your preparation time strategically.

Question Volume and Difficulty

The most immediate difference is scale. Apple's known question pool is significantly larger, with approximately 356 questions categorized by difficulty: 100 Easy, 206 Medium, and 50 Hard. This suggests a vast and varied problem set where Medium questions form the core of the interview experience. Expect a broad test of fundamentals applied in different contexts.

In contrast, LinkedIn's pool is more curated at around 180 questions: 26 Easy, 117 Medium, and 37 Hard. The distribution skews even more heavily toward Medium difficulty, indicating a strong focus on problems that require multiple steps or concepts combined, without excessive emphasis on the most complex algorithmic puzzles.

Key Takeaway: Prepare for a wider range of scenarios for Apple. For LinkedIn, deep mastery of Medium problems is paramount.

Topic Overlap

Both companies heavily emphasize core data structures. Array, String, and Hash Table problems are foundational at both. You must be fluent in manipulation, searching, and optimization techniques for these.

The primary divergence is in the next layer of focus:

• Apple lists Dynamic Programming (DP) as a key topic. This aligns with its product work on operating systems and hardware-constrained environments where optimal performance and resource management are critical. Expect problems involving optimization, counting, or decision-making sequences.
• LinkedIn lists Depth-First Search (DFS) as a key topic. This reflects its domain—the social graph. Traversal algorithms (DFS, BFS) are essential for exploring connections, networks, hierarchies, or tree-like data structures prevalent in social platforms.

# Example Focus: Dynamic Programming (Apple-relevant)
def climb_stairs(n: int) -> int:
    """Classic DP problem: count ways to climb stairs."""
    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]

# Example Focus: Depth-First Search (LinkedIn-relevant)
def dfs(graph, node, visited):
    """Basic DFS traversal for a graph."""
    if node not in visited:
        visited.add(node)
        for neighbor in graph[node]:
            dfs(graph, neighbor, visited)

Which to Prepare for First

Start with LinkedIn. Its smaller, more concentrated question set allows you to build deep competency in the core topics (Array, String, Hash Table) and master graph traversal (DFS/BFS) efficiently. Achieving fluency here creates a strong foundation.

Then, move to Apple. Use your established core skills and expand into the additional breadth and specific advanced topics like Dynamic Programming. Tackling Apple's larger pool will feel more manageable after solidifying the fundamentals, and you'll be adding the DP pattern to your toolkit rather than learning everything at once.

This sequential approach builds from a focused core to a broader skill set, maximizing preparation efficiency for both interview styles.

For further details, explore the question lists directly: Apple Questions | LinkedIn Questions