LinkedIn vs Morgan Stanley: Interview Question Comparison

When preparing for technical interviews, understanding the specific patterns and expectations of each company can dramatically increase your chances of success. A comparison between LinkedIn, a major tech firm, and Morgan Stanley, a leading investment bank, reveals distinct differences in their technical interview landscapes, despite some surface-level similarities in common topics.

Question Volume and Difficulty

The most striking difference is the sheer volume of questions. LinkedIn's list of 180 questions is over three times larger than Morgan Stanley's 53. This suggests that LinkedIn's question bank is broader and more varied, requiring candidates to be prepared for a wider range of scenarios and edge cases.

The difficulty distribution also tells a story:

• LinkedIn (E26/M117/H37): The majority of questions are Medium difficulty (65%), with a significant portion of Hard questions (21%). This aligns with the expectation for senior software engineering roles at top-tier tech companies, where problem-solving under pressure and handling complex algorithms are key.
• Morgan Stanley (E13/M34/H6): The focus is also on Medium difficulty (64%), but the number of Hard questions is much lower (11%). The profile skews slightly more toward fundamentals, with a quarter of questions being Easy. This may reflect a broader range of technical roles or a stronger emphasis on clean, correct implementation of core concepts over highly optimized, obscure algorithms.

Topic Overlap

Both companies heavily test Array, String, and Hash Table problems. These are foundational data structures for coding interviews, and proficiency here is non-negotiable for both.

The key differentiator lies in the next layer of topics:

• LinkedIn prominently features Depth-First Search (DFS). This indicates a strong emphasis on tree and graph traversal problems, which are common in designing features for social networks (e.g., connection degrees, feed propagation).
• Morgan Stanley explicitly lists Dynamic Programming (DP). This points toward problems involving optimization, counting, or financial modeling scenarios (e.g., maximizing profit, counting ways to make change), which are highly relevant in a quantitative finance context.

While both companies will likely test across all common patterns, their listed "headline" topics signal where they place additional weight.

Example: A "House Robber" style problem (Dynamic Programming)

def rob(nums):
    if not nums:
        return 0
    dp = [0] * (len(nums) + 1)
    dp[1] = nums[0]
    for i in range(2, len(nums) + 1):
        dp[i] = max(dp[i-1], dp[i-2] + nums[i-1])
    return dp[-1]

Example: A "Clone Graph" style problem (Depth-First Search)

def cloneGraph(node):
    if not node:
        return None
    clone_map = {}
    def dfs(original):
        if original in clone_map:
            return clone_map[original]
        clone = Node(original.val)
        clone_map[original] = clone
        for neighbor in original.neighbors:
            clone.neighbors.append(dfs(neighbor))
        return clone
    return dfs(node)

Which to Prepare for First

Your preparation priority should be dictated by your target role and timeline.

Prepare for Morgan Stanley first if: You are new to technical interviews or are on a tighter schedule. The smaller, more fundamental question set allows you to build core competency in arrays, strings, hash tables, and dynamic programming efficiently. Mastering this list will give you a solid foundation that is directly applicable to many other companies, including LinkedIn.

Prepare for LinkedIn first if: You are aiming for senior-level software engineering roles or have more preparation time. The larger volume and higher proportion of hard questions, especially in graph traversal (DFS), demand deeper practice. Successfully tackling LinkedIn's question bank will inherently cover the core topics needed for Morgan Stanley, making the subsequent preparation lighter.

Ultimately, a strong candidate for either company must master the shared fundamentals. The strategic difference lies in the depth required for graph problems (LinkedIn) versus the specific focus on optimization patterns (Morgan Stanley).

For detailed question lists, visit the LinkedIn interview guide and the Morgan Stanley interview guide.