Roblox vs Morgan Stanley: Interview Question Comparison

When preparing for technical interviews at top companies, understanding their specific question patterns is crucial for efficient study. Roblox and Morgan Stanley, while operating in vastly different industries (gaming/fintech vs. investment banking), both assess core algorithmic and data structure proficiency. A direct comparison of their question banks reveals distinct priorities in difficulty and topic focus, guiding how you should allocate your preparation time.

Question Volume and Difficulty

Roblox's question set is slightly larger at 56 questions, with a notable emphasis on harder problems: 12 Hard (H) questions compared to 8 Easy (E) and 36 Medium (M). This distribution suggests Roblox interviews frequently push into complex problem-solving, testing not just correctness but optimal, elegant solutions under pressure.

Morgan Stanley's set contains 53 questions, but with a dramatically different difficulty spread: 13 Easy, 34 Medium, and only 6 Hard. This indicates Morgan Stanley's technical screens often focus on foundational competency and reliable implementation of standard patterns, with less frequent deep dives into the most challenging algorithmic territory.

Key Takeaway: Roblox's interview is likely more demanding from a pure algorithmic difficulty standpoint. Morgan Stanley's process may place greater weight on other factors (e.g., system design, financial knowledge, or behavioral fit) once core coding competency is established.

Topic Overlap

Both companies heavily test the fundamental trio: Array, String, and Hash Table operations. Mastery here is non-negotiable for either interview.

The critical divergence is in the fourth-most frequent topic. Roblox lists Math, which often encompasses number theory, combinatorics, or simulation problems. Morgan Stanley lists Dynamic Programming (DP), a classic topic for assessing optimization and problem decomposition skills.

This difference is telling. Roblox's "Math" focus aligns with game development logic, physics, or procedural generation-adjacent problems. Morgan Stanley's DP focus is common in finance-adjacent tech roles for solving optimization and recursive calculation problems.

Example: A Topical Problem A Roblox "Math" problem might involve calculating trajectories or scoring systems:

# Python: Simulate a circular game elimination (Josephus problem)
def find_winner(n, k):
    pos = 0
    for i in range(2, n + 1):
        pos = (pos + k) % i
    return pos + 1

A Morgan Stanley DP problem might focus on maximizing profit or minimizing cost:

# Python: Classic DP - House Robber (maximizing sum without adjacent elements)
def rob(nums):
    prev1, prev2 = 0, 0
    for num in nums:
        prev1, prev2 = max(prev2 + num, prev1), prev1
    return prev1

Which to Prepare for First

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

If you are aiming for a generalist software engineering role and have interviews with both, start with Morgan Stanley's question bank. Its lower proportion of Hard questions and strong focus on Array, String, Hash Table, and DP provides a solid, high-yield foundation. Conquering these will build confidence and cover the bulk of what Roblox also tests. Once comfortable, you can layer on Roblox's more challenging Hard problems and Math-specific practice.

If you are specifically targeting a gameplay, engine, or tools engineer role at Roblox, reverse the order. Begin with Roblox's problems to acclimate to the higher difficulty ceiling and mathematical reasoning. This will inherently cover the Morgan Stanley fundamentals, leaving you to simply polish DP patterns afterward.

Regardless of order, drill the shared core topics (Array, String, Hash Table) until solutions are instinctive. Then branch into the company-specific specialty: Math and complex optimization for Roblox, Dynamic Programming and clean, robust code for Morgan Stanley.

For targeted practice, visit the company pages: Roblox Interview Questions and Morgan Stanley Interview Questions.