Goldman Sachs vs Infosys: Interview Question Comparison

When preparing for technical interviews at major companies, understanding their specific focus areas and difficulty distribution is crucial for efficient study. Goldman Sachs and Infosys, while both prominent, have distinct profiles in their technical interview question banks. Goldman Sachs presents a larger volume of questions (270) compared to Infosys (158), and their difficulty distributions and primary topic emphases reveal different priorities for candidates.

Question Volume and Difficulty

Goldman Sachs's question bank is significantly larger, with 270 questions categorized as 51 Easy, 171 Medium, and 48 Hard. This 63% Medium, 18% Hard split indicates a strong focus on intermediate problem-solving, with a substantial portion requiring advanced optimization. You must be comfortable not just solving problems, but crafting highly efficient solutions under pressure.

Infosys's bank is smaller at 158 questions, with a distribution of 42 Easy, 82 Medium, and 34 Hard. This translates to a similar emphasis on Medium-difficulty problems (52%), but with a notably higher proportion of Hard questions (22%) relative to its total size. While the absolute number of Hard questions is lower than Goldman's, its relative weight is greater, suggesting that for Infosys, mastering a core set of challenging problems is particularly important.

Topic Overlap

Both companies heavily test Array, String, and Dynamic Programming (DP), making these the highest-yield topics for a candidate interviewing at either.

• Array & String: Problems often involve two-pointers, sliding window, and prefix sum techniques.
• Dynamic Programming: Expect classic problems like knapsack, longest common subsequence, and coin change, as well as variations applied to strings and arrays.

The key difference lies in their fourth most frequent topic. Goldman Sachs emphasizes Hash Table problems, which are fundamental for optimizing lookups and solving frequency-counting and pair-finding problems common in financial data processing. Infosys prioritizes Math problems, which include number theory, combinatorics, and modular arithmetic, reflecting its engineering and systems integration background.

# Example: A Hash-Table heavy 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 Math-adjacent problem (Prime Check)
def is_prime(n):
    if n <= 1:
        return False
    i = 2
    while i * i <= n:
        if n % i == 0:
            return False
        i += 1
    return True

Which to Prepare for First

Prepare for Goldman Sachs first if you are targeting both. Its larger question bank, especially the high count of Medium problems, provides a comprehensive workout in core data structures and algorithms. Mastering this material will inherently cover a vast majority of Infosys's core topics (Array, String, DP). Once comfortable with Goldman's focus, you can then efficiently pivot to fill the specific gaps for Infosys: deepening your practice on a selection of Hard problems and brushing up on mathematical algorithms and number theory problems that are less emphasized at Goldman.

This strategy ensures you build a broad, strong foundation first, then specialize, making your overall preparation more efficient.

For detailed question lists, visit the Goldman Sachs and Infosys company pages.