Google vs Apple: Interview Question Comparison

When preparing for technical interviews at top tech companies, understanding the differences in their question banks can help you allocate your study time effectively. Both Google and Apple ask questions covering core data structures and algorithms, but they differ significantly in volume, difficulty distribution, and focus. This comparison breaks down their patterns based on aggregated data from coding interview platforms.

Question Volume and Difficulty

The most striking difference is scale. Google’s question bank is substantially larger, with 2,217 questions cataloged, compared to Apple’s 356 questions. This suggests that Google interviews have a wider potential pool of problems, making pure memorization less effective and demanding deeper conceptual understanding.

The difficulty distribution also reveals distinct profiles:

• Google: 588 Easy (26.5%), 1,153 Medium (52.0%), 476 Hard (21.5%).
• Apple: 100 Easy (28.1%), 206 Medium (57.9%), 50 Hard (14.0%).

While both companies focus heavily on Medium-difficulty questions, Google has a significantly higher proportion of Hard problems. Apple’s interview process, while still rigorous, appears to place a stronger emphasis on foundational proficiency with a relatively smaller set of challenging questions. Google’s profile indicates a consistent expectation to solve complex algorithmic puzzles under pressure.

Topic Overlap

Despite the volume difference, both companies prioritize the same fundamental data structures. The top four topics for both are identical: Array, String, Hash Table, and Dynamic Programming (DP). This overlap means core preparation for one company directly benefits the other.

Arrays and Strings are the bedrock for most problems. Mastery here is non-negotiable for both.

# Example: Sliding Window on String (Medium)
def length_of_longest_substring(s: str) -> int:
    char_set = set()
    left = max_len = 0
    for right in range(len(s)):
        while s[right] in char_set:
            char_set.remove(s[left])
            left += 1
        char_set.add(s[right])
        max_len = max(max_len, right - left + 1)
    return max_len

Hash Tables are critical for efficient lookups and are ubiquitous in optimization. Dynamic Programming is a key differentiator for harder problems at both companies, though you'll encounter it more frequently at Google.

Which to Prepare for First

Start with Google. Its vast question bank and higher concentration of Hard problems make it the more comprehensive benchmark. If you can comfortably solve a wide range of Google-style questions—particularly Medium and Hard problems on core topics—you will be over-prepared for the algorithmic depth required at Apple.

A practical study path:

1. Solidify Foundations: Achieve fluency in Array, String, and Hash Table manipulations. These are the most common topics for both.
2. Tackle Dynamic Programming: Dedicate significant time to DP patterns (knapsack, LCS, Kadane's algorithm, etc.). This is often the hurdle for Hard problems.
3. Practice at Scale: Use Google's large problem set to expose yourself to a wide variety of scenarios and edge cases. This builds adaptability.
4. Refine for Apple: Once confident with Google-level problems, review Apple's specific question list. The reduced volume allows for targeted final preparation, focusing on their frequently asked problems.

Ultimately, preparing for Google builds a robust, transferable skill set. The core algorithmic thinking and coding clarity required will serve you well in any technical interview, including Apple's.

For specific question lists and patterns, visit the company pages: Google Interview Questions and Apple Interview Questions.