Amazon vs Goldman Sachs: Interview Question Comparison

When preparing for technical interviews at top-tier companies, understanding the specific focus and patterns of each firm’s question bank is a strategic advantage. Amazon and Goldman Sachs, while both demanding strong algorithmic skills, present distinct profiles in terms of volume, difficulty distribution, and topical emphasis. A direct comparison of their tagged questions on coding platforms reveals clear differences that should guide your study plan.

Question Volume and Difficulty

The sheer scale of Amazon’s question pool is its most defining characteristic. With 1,938 questions cataloged, it dwarfs Goldman Sachs's 270 questions. This volume reflects Amazon's vast, standardized interview process across countless teams and roles, particularly for software development engineers.

The difficulty distribution further highlights their different approaches:

• Amazon (E530/M1057/H351): The spread is relatively even, with a significant majority in the Medium category (1,057 questions). This indicates a strong focus on core problem-solving under typical interview constraints. The substantial number of Hard questions (351) suggests depth is required for higher-level roles or specific domains.
• Goldman Sachs (E51/M171/H48): The distribution is heavily skewed toward Medium difficulty (171 of 270 questions). The counts for Easy (51) and Hard (48) are much lower and relatively balanced. This points to a more targeted interview process, likely emphasizing solid fundamentals and applied problem-solving over extreme algorithmic depth.

Key Takeaway: Amazon requires broader preparation to cover a larger surface area of potential problems, while Goldman Sachs demands deep, confident mastery of medium-core concepts.

Topic Overlap

Despite their different scales, both companies concentrate on the same fundamental data structures and techniques. The top topics are identical: Array, String, Hash Table, and Dynamic Programming.

• For Amazon: Mastery of these four areas is non-negotiable. The high question count means you will encounter numerous variations on array manipulation, string algorithms, hash map-based lookups/optimizations, and DP patterns. You must be able to recognize and implement solutions quickly.
• For Goldman Sachs: While the topics are the same, the expectation is precision and clarity. With a smaller pool, questions may be more classic or directly applied to financial/data processing scenarios. A firm grasp of how to use hash tables to achieve O(1) lookups or how to break down a problem into optimal substructures (DP) is critical.

The overlap is a major efficiency win for preparation. Strengthening these core areas simultaneously benefits your readiness for both companies.

# Example: A common Hash Table/Array 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 []

# Example: A classic Dynamic Programming pattern (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]

Which to Prepare for First

Start with Goldman Sachs. Its smaller, more concentrated question set allows you to build a robust foundation in the core shared topics efficiently. Achieving fluency with ~200 medium-difficulty problems will make you competitive for Goldman Sachs and establish the essential skill set needed for any technical interview.

Once this base is solid, pivot to Amazon. Use the expansive Amazon question bank to scale your preparation. The goal here is not to memorize 1,900 problems, but to expose yourself to a wider variety of patterns and edge cases within the same core topics. This builds the adaptability and speed required to handle Amazon's broader and potentially more unpredictable question set.

In essence, use Goldman Sachs's focused list to build your core strength, then use Amazon's vast list for stress-testing and broadening your pattern recognition. This sequential approach is the most efficient path to being prepared for both.

For targeted question lists and reported experiences, visit the company pages: Amazon and Goldman Sachs.