Salesforce vs Morgan Stanley: Interview Question Comparison

When preparing for technical interviews at top companies, understanding the specific focus and expectations of each can dramatically improve your efficiency. Salesforce and Morgan Stanley represent two distinct sectors—cloud computing and investment banking—yet their technical interviews share surprising common ground in core computer science fundamentals. This comparison analyzes their question patterns, difficulty distribution, and topic emphasis to help you tailor your preparation strategy.

Question Volume and Difficulty

Salesforce's question bank is significantly larger at 189 questions compared to Morgan Stanley's 53. This reflects both the breadth of roles at Salesforce and their heavier emphasis on algorithmic problem-solving across engineering levels.

The difficulty distribution reveals distinct hiring philosophies:

• Salesforce: 27 Easy (14%), 113 Medium (60%), 49 Hard (26%)
• Morgan Stanley: 13 Easy (25%), 34 Medium (64%), 6 Hard (11%)

Salesforce places substantial weight on Medium and Hard problems, indicating they expect candidates to handle complex algorithmic challenges. Their 26% Hard questions suggest they're testing for senior roles or particularly strong problem-solving abilities. Morgan Stanley's distribution is more moderate, with nearly two-thirds Medium questions and minimal Hard content—this aligns with financial firms typically prioritizing practical coding skills over advanced algorithms.

Topic Overlap

Both companies focus intensely on four core data structures: Array, String, Hash Table, and Dynamic Programming. This overlap means mastering these topics serves dual purposes.

Array and String problems form the foundation at both companies. Expect manipulation, searching, and transformation tasks.

# Two Sum variation - common at both companies
def two_sum_variant(nums, target):
    seen = {}
    for i, num in enumerate(nums):
        complement = target - num
        if complement in seen:
            return [seen[complement], i]
        seen[num] = i
    return []

Hash Table questions frequently appear as part of optimization problems, especially for lookups and frequency counting.

Dynamic Programming deserves special attention. While both companies include it, Salesforce's larger question count means you'll encounter more DP variations. Master fundamental patterns like knapsack, LCS, and Fibonacci-style problems.

# DP example - Fibonacci style
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 Morgan Stanley if you're earlier in your interview preparation journey. Their lower volume (53 questions) and reduced emphasis on Hard problems make for a more manageable initial target. The 64% Medium questions provide substantial challenge while remaining accessible with focused study. Mastering Morgan Stanley's patterns will build confidence and core competency.

Prepare for Salesforce when you need more comprehensive algorithmic coverage or are specifically targeting tech companies. Their 189 questions demand greater time investment, but the payoff is broader problem exposure. The 26% Hard questions require deeper mastery—tackle these after establishing solid Medium problem proficiency.

The shared focus on Array, String, Hash Table, and Dynamic Programming means your preparation compounds. Solve these core topics thoroughly, and you'll cover significant ground for both companies. Prioritize Medium problems across all four topics, then branch to company-specific question banks.

Remember that both companies also assess system design and behavioral competencies—this analysis covers only the algorithmic component. Balance your technical preparation with these other interview dimensions.

For detailed company-specific question lists and patterns, visit Salesforce Questions and Morgan Stanley Questions.