Goldman Sachs vs Airbnb: Interview Question Comparison

Preparing for technical interviews requires understanding each company's specific focus areas. While Goldman Sachs and Airbnb both test core algorithmic skills, their approaches differ significantly in volume, difficulty distribution, and practical application. This comparison breaks down their question patterns to help you prioritize your study.

Question Volume and Difficulty

Goldman Sachs presents a massive dataset of 270 questions, heavily weighted toward medium difficulty. With 171 medium (M), 48 hard (H), and 51 easy (E) problems, the sheer volume indicates a broad, endurance-focused screening process. You can expect a multi-round interview covering a wide range of standard algorithm problems, with an emphasis on reliably solving medium-difficulty challenges under time constraints.

Airbnb's dataset is smaller and more curated at 64 questions. The distribution—34 medium (M), 19 hard (H), and 11 easy (E)—shows a higher concentration of challenging problems relative to its total. This suggests interviews may dive deeper into fewer, more complex problems, potentially involving system design or real-world scenario modeling alongside pure algorithms.

Topic Overlap

Both companies heavily prioritize Array, String, Hash Table, and Dynamic Programming. This core set is non-negotiable. Mastery here is essential for both.

Goldman Sachs, given its volume, will test these fundamentals exhaustively. Expect many variations on array manipulation, string parsing, and classic DP problems like knapsack or longest common subsequence. The large question bank means you must be adaptable.

Airbnb, while covering the same core, often applies these topics to practical, domain-relevant scenarios. Problems may involve date/time manipulation for bookings, matching algorithms for listings, or designing data structures for user trips. The coding is frequently intertwined with system design thinking.

For a core topic like Two Sum (a Hash Table classic), the implementation is similar, but the context may differ.

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 []

Which to Prepare for First

Prepare for Goldman Sachs first. Its vast question bank covers a wider surface area of standard algorithm problems. Success here requires building strong, fast, and reliable fundamentals across the core topics. The skills you develop—especially speed and accuracy on medium-difficulty problems—are directly transferable and will form an excellent foundation for Airbnb's more focused, in-depth interviews.

Tackling Goldman's volume forces you to solidify patterns. Once you can efficiently solve a large number of array, string, and DP problems, you can then layer on Airbnb's specific nuances: the practical twists, the integration with system design concepts, and the stamina for fewer but harder problems.

Start with the core topics common to both. Drill array manipulations, hash map implementations for lookups and counting, string algorithms, and fundamental DP patterns. Use the Goldman Sachs list for breadth and repetition, then use the Airbnb list to practice applying those patterns to more open-ended, scenario-based questions.

For targeted practice, visit the company pages: Goldman Sachs and Airbnb.