Uber vs LinkedIn: Interview Question Comparison

When preparing for technical interviews at top tech companies, understanding their specific question patterns and focus areas is crucial. Both Uber and LinkedIn are known for rigorous coding interviews, but they differ significantly in volume, difficulty distribution, and topic emphasis. This comparison breaks down the key differences to help you strategize your preparation.

Question Volume and Difficulty

Uber's question bank is substantially larger, with 381 total questions compared to LinkedIn's 180. This volume suggests a broader range of problems you might encounter or a more extensive historical dataset.

The difficulty distribution reveals a key strategic insight. Uber's questions are heavily weighted toward Medium difficulty (224 out of 381, or ~59%). Hard questions also make up a significant portion (~27%), with only about 14% being Easy. This indicates that Uber's interviews are notoriously challenging, expecting candidates to solve complex algorithmic problems under pressure.

LinkedIn's distribution, while still Medium-heavy (117 out of 180, or ~65%), shows a different profile. The proportion of Hard questions is notably lower (~21%), and Easy questions are slightly more common (~14%). This suggests that while LinkedIn interviews are still demanding, they may place a slightly greater emphasis on foundational correctness and clarity alongside problem-solving, rather than exclusively on tackling the most complex algorithms.

Topic Overlap

Both companies heavily test core data structures. Array, String, and Hash Table problems are fundamental for interviews at both Uber and LinkedIn. You must be proficient in manipulating these structures, using two-pointer techniques, sliding windows, and hashing for efficient lookups.

# Example: Two-pointer with Array/String (common to both)
def is_palindrome(s: str) -> bool:
    left, right = 0, len(s) - 1
    while left < right:
        if s[left] != s[right]:
            return False
        left += 1
        right -= 1
    return True

The primary divergence in topic focus is notable. Uber has a significant emphasis on Dynamic Programming (DP), a topic not highlighted in LinkedIn's top list. DP questions (like knapsack, longest common subsequence, or unique paths) are classic Hard and Medium problems, aligning with Uber's difficulty skew. Mastering DP patterns is non-negotiable for Uber.

Conversely, LinkedIn shows a distinct focus on Depth-First Search (DFS), often associated with tree and graph traversal problems. This implies a stronger emphasis on recursive thinking, navigating hierarchical data, and solving problems related to connectivity or search in non-linear structures.

# Example: DFS on a graph (pertinent for LinkedIn)
def dfs(node, graph, visited):
    if node in visited:
        return
    visited.add(node)
    for neighbor in graph[node]:
        dfs(neighbor, graph, visited)

Which to Prepare for First

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

If your primary target is Uber, start there. The larger question bank and higher proportion of Hard problems, especially in DP, require more dedicated study time. Building a strong foundation in arrays, strings, and hash tables is essential, but you must allocate significant practice to mastering Dynamic Programming patterns. Expect a more intense algorithmic grind.

If your primary target is LinkedIn, you can start with a slightly more focused approach. Ensure mastery of the common core topics (Array, String, Hash Table) and then deepen your understanding of graph and tree traversal algorithms, particularly DFS and its iterative implementations. The slightly lower volume and Hard-question count might allow for a more concentrated preparation period.

For generalists preparing for both, begin with the common core. Achieve fluency in array/string manipulation and hash table applications. Once solid, branch out: dedicate deep practice sessions to Dynamic Programming for Uber and to graph/tree algorithms for LinkedIn. Since Uber's overall demands are greater, its preparation will cover a wider spectrum of difficulty, which will also benefit you for LinkedIn interviews.

Ultimately, success at either company requires rigorous practice. Use the company-specific question lists to tailor your final weeks of preparation.

• Practice Uber questions: CodeJeet Uber Prep
• Practice LinkedIn questions: CodeJeet LinkedIn Prep