Atlassian vs Morgan Stanley: Interview Question Comparison

When preparing for technical interviews at top companies, understanding their specific focus areas can dramatically improve your efficiency. Atlassian and Morgan Stanley represent two distinct industry domains—software development and financial services—yet their technical interviews share surprising common ground while maintaining key differences in emphasis and difficulty distribution.

Question Volume and Difficulty

Atlassian’s question pool is slightly larger at 62 questions, with a difficulty distribution of 42 Easy, 43 Medium, and 12 Hard questions. This indicates a balanced emphasis on Medium-level problems, with a significant portion dedicated to fundamentals. The presence of 12 Hard questions suggests they expect candidates to handle complex algorithmic challenges, particularly for senior roles.

Morgan Stanley’s set contains 53 questions, distributed as 13 Easy, 34 Medium, and 6 Hard. The proportion of Medium questions is notably higher (64% vs. Atlassian’s 69% when considering Medium+Hard), but the absolute number of Hard questions is half that of Atlassian. This suggests Morgan Stanley’s technical screening may prioritize consistent performance on moderately challenging problems over solving extreme edge cases.

Topic Overlap

Both companies heavily emphasize Array, String, and Hash Table problems. These form the core of their technical assessments, testing fundamental data manipulation and lookup efficiency.

Atlassian includes Sorting as a distinct focus area, indicating they value algorithmic efficiency and the ability to organize data effectively. Many of their problems likely involve optimizing operations on sorted data or implementing custom comparison logic.

Morgan Stanley uniquely highlights Dynamic Programming in their top topics. This aligns with financial modeling and optimization problems common in quantitative finance. Expect problems involving optimization, sequence analysis, or resource allocation.

Here’s a typical problem that could appear at either company, solved using 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 []

A Dynamic Programming problem, more characteristic of Morgan Stanley:

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 Atlassian’s question set if you are preparing for both. Its broader coverage, including more Hard problems and explicit Sorting focus, creates a stronger foundation. Mastering these will naturally cover the core topics (Array, String, Hash Table) that dominate Morgan Stanley’s list. The additional challenge of Hard problems builds resilience.

Once comfortable with Atlassian’s problems, shift focus to Dynamic Programming to address Morgan Stanley’s specific emphasis. Practice common DP patterns like Fibonacci-style sequences, knapsack problems, and grid traversal. Since Morgan Stanley has fewer Hard questions, solid performance on Medium problems with strong DP skills will cover most of their technical bar.

This sequential approach ensures you build from a wider, more challenging base to a more specialized one, maximizing preparation efficiency for both interview styles.

For targeted practice, visit the company pages: Atlassian and Morgan Stanley.