Apple vs eBay: Interview Question Comparison

When preparing for technical interviews at top tech companies, understanding their specific question patterns and focus areas is crucial. Apple and eBay, while both requiring strong algorithmic skills, present distinct challenges in terms of volume, difficulty, and topic emphasis. This comparison breaks down their interview landscapes to help you prioritize your preparation effectively.

Question Volume and Difficulty

The sheer scale of preparation differs dramatically between the two companies.

Apple maintains a large, well-documented repository of approximately 356 questions, categorized by difficulty: 100 Easy, 206 Medium, and 50 Hard. This high volume indicates that Apple's interview process draws from a broad and deep pool of problems. Encountering a completely new, unrehearsed question is less likely, but the extensive catalog requires significant study time. The heavy weighting towards Medium-difficulty questions suggests interviews are designed to consistently assess strong, practical problem-solving skills on non-trivial challenges.

eBay has a significantly smaller public question bank of about 60 questions: 12 Easy, 38 Medium, and 10 Hard. The lower total volume makes comprehensive review more feasible. However, the higher proportion of Medium questions (over 63% compared to Apple's ~58%) means your preparation must still focus intensely on core, medium-level algorithmic concepts. The smaller pool could mean each question type is more representative of what you might see.

Topic Overlap

Both companies emphasize foundational data structures, but with different secondary focuses.

The core overlap is substantial: Array, String, and Hash Table are top topics for both. You must be exceptionally proficient in manipulating these structures. A problem combining a hash map for tracking with array or string traversal is highly likely at either company.

# Example: A common two-sum style problem using a Hash Table.
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 []

# Usage often involves array iteration.
result = two_sum([2, 7, 11, 15], 9)

The key divergence is in secondary topics. Apple explicitly lists Dynamic Programming (DP) as a top category, signaling that complex optimization problems involving memoization or tabulation are fair game and likely important. eBay's list highlights Sorting, indicating a focus on algorithms that involve ordering data, efficient comparisons, and possibly leveraging sorted properties for optimization (like two-pointer techniques on sorted arrays).

Which to Prepare for First

Your choice depends on your timeline and the companies you are targeting.

If you are interviewing with both, start with eBay. Its smaller, focused question set allows you to build core competency in the overlapping topics (Array, String, Hash Table) and master medium-difficulty sorting problems efficiently. This foundation will directly apply to Apple's vast question bank. Once comfortable, you can expand your preparation to tackle Apple's larger volume, adding dedicated study for Dynamic Programming and a wider variety of Hard problems.

If you are only targeting one company, tailor your plan directly. For Apple, begin with the high-frequency Medium problems across Array, String, and Hash Table, then integrate DP study early. Use the large question bank for breadth. For eBay, drill deeply into the listed 60 questions, ensuring you can solve each variant fluently, with special attention to sorting-based solutions and clean code for medium-difficulty challenges.

Ultimately, mastery of the common core—efficient array/string manipulation with hash tables—is the non-negotiable base for success at either company.

For focused practice, visit the Apple question list and the eBay question list.