Bloomberg vs LinkedIn: Interview Question Comparison

When preparing for technical interviews, company-specific question lists provide targeted practice. Bloomberg and LinkedIn both test core data structures and algorithms, but their question banks differ significantly in size, difficulty distribution, and focus areas. Understanding these differences helps you allocate your study time efficiently.

Question Volume and Difficulty

The most striking difference is scale. Bloomberg's list, with 1173 questions, is massive. Its difficulty distribution (391 Easy, 625 Medium, 157 Hard) indicates a strong emphasis on Medium-difficulty problems, which form the backbone of their technical screens and on-site interviews. Preparing for Bloomberg requires a broad and deep practice regimen to cover this extensive question bank.

In contrast, LinkedIn's list is more focused, with 180 total questions. The distribution (26 Easy, 117 Medium, 37 Hard) shows an even more pronounced focus on Medium-difficulty questions, which comprise nearly 65% of their list. While still challenging, the smaller volume makes the problem set more approachable for targeted study.

Key Takeaway: Bloomberg demands extensive, broad-range practice. LinkedIn requires deep, focused mastery of a smaller core set.

Topic Overlap

Both companies heavily test fundamental data structures. The shared emphasis on Array, String, and Hash Table problems means proficiency here is non-negotiable for either interview.

• Array/String/Hash Table: Expect questions on two-pointer techniques, sliding windows, prefix sums, and character/count mapping.
• Math: Bloomberg's specific inclusion of Math suggests a higher likelihood of numerical, combinatorial, or bit manipulation problems.
• Depth-First Search: LinkedIn's listing of DFS hints at a greater focus on tree and graph traversal problems, which often involve recursive or stack-based backtracking.

# Example: A classic overlapping topic - Two Sum (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: LinkedIn-favored topic - DFS on a Tree
def dfs_tree(node, target):
    if not node:
        return False
    if node.val == target:
        return True
    return dfs_tree(node.left, target) or dfs_tree(node.right, target)

Which to Prepare for First

Your priority depends on your timeline and goals.

Prepare for LinkedIn first if: You are new to company-specific practice or have a shorter timeline. Mastering their focused list builds confidence and core competency. The skills gained—especially in Arrays, Strings, Hash Tables, and DFS—translate directly to a large portion of Bloomberg's list, providing a strong foundation for later, more expansive study.

Prepare for Bloomberg first if: You have a longer runway or are interviewing at Bloomberg sooner. Tackling its vast question bank is a major undertaking that will inherently cover almost all of LinkedIn's focus areas. Successfully working through a significant portion of Bloomberg's list will make LinkedIn's focused list feel like a subset review.

A strategic hybrid approach is effective: start by solidifying the shared core topics (Array, String, Hash Table). Then, if targeting LinkedIn, integrate DFS practice. If targeting Bloomberg, integrate Math problems. This builds a versatile foundation adaptable to either interview.

For targeted practice, visit the Bloomberg question list and LinkedIn question list on CodeJeet.