Meta vs Goldman Sachs: Interview Question Comparison

When preparing for technical interviews, understanding the specific focus and expectations of each company is crucial. Meta and Goldman Sachs represent two distinct domains—tech and finance—but their technical interviews share common ground in core computer science fundamentals. However, the volume, difficulty distribution, and emphasis of questions differ significantly, requiring tailored preparation strategies.

Question Volume and Difficulty

Meta’s question bank is substantially larger and more heavily weighted toward medium-difficulty problems. With 1,387 total questions (414 Easy, 762 Medium, 211 Hard), Meta’s interview process tests breadth and depth across a wide spectrum. The high volume of Medium questions suggests interviews are designed to assess strong problem-solving skills on non-trivial, yet standard, algorithmic challenges. You must be proficient at tackling numerous variations under pressure.

Goldman Sachs has a more focused set of 270 questions (51 Easy, 171 Medium, 48 Hard). The proportion of Medium questions is even higher relative to its total, indicating a strong, consistent focus on this difficulty tier. The smaller overall volume means question repetition in interviews is more likely, and deep mastery of a narrower set of problems is advantageous. While still challenging, the scope is less expansive than Meta's.

Topic Overlap

Both companies heavily emphasize Array, String, and Hash Table problems. These form the essential toolkit for handling data manipulation and lookup efficiently.

Meta’s listed topics (Array, String, Hash Table, Math) suggest a strong focus on foundational data structures and logical reasoning. However, in practice, Meta interviews extensively cover Dynamic Programming, Graphs (BFS/DFS), Trees, and Recursion. The absence of DP in the listed summary is notable, but it remains a critical area.

Goldman Sachs explicitly includes Dynamic Programming in its top topics, alongside Array, String, and Hash Table. This highlights a key difference: finance-oriented technical interviews often prioritize optimization and efficient resource allocation, making DP a common theme. Questions may lean toward mathematical modeling, probability, or scenario-based problem-solving within financial contexts.

Here is a classic problem that both companies might use, approached with 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 []

# Example
print(two_sum([2, 7, 11, 15], 9))  # Output: [0, 1]

A Dynamic Programming problem, more emphasized at Goldman Sachs, might look like this:

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]

# Example
print(climb_stairs(5))  # Output: 8

Which to Prepare for First

Prepare for Goldman Sachs first if you are interviewing with both. The smaller, more concentrated question set allows you to build core competency efficiently. Achieving fluency in their high-frequency Medium problems and Dynamic Programming patterns will establish a strong foundation. This foundation will directly transfer to a significant portion of Meta's question bank.

Once comfortable with Goldman's focus areas, expand your preparation for Meta. Use the additional time to cover their vast question volume, ensuring you practice a wider variety of problems, especially in graphs, trees, and system design (for higher levels). Meta's interviews often require faster pattern recognition across a broader set of topics.

Master the shared core of Arrays, Strings, and Hash Tables, then branch out based on each company's distinct emphasis.

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