LinkedIn vs Infosys: Interview Question Comparison

When preparing for technical interviews, understanding the specific focus of each company can dramatically improve your efficiency. LinkedIn and Infosys, while both major tech employers, have distinct interview profiles based on their core business models—a social networking platform with complex data systems versus a global IT consulting and services firm. This comparison breaks down their question volume, difficulty, and topic emphasis to help you prioritize your study plan.

Question Volume and Difficulty

The total number of documented questions provides a rough gauge of the breadth of their question banks. LinkedIn's list is slightly larger at 180 questions compared to Infosys's 158. However, the difficulty distribution reveals a more significant difference in their technical screening intensity.

LinkedIn's breakdown (Easy: 26, Medium: 117, Hard: 37) shows a heavy emphasis on Medium-difficulty problems. This skew suggests their interviews are designed to thoroughly assess core problem-solving and algorithmic skills under typical constraints. The notable portion of Hard questions (over 20%) indicates that for senior or specialized roles, you can expect challenging, multi-step problems.

Infosys's distribution (Easy: 42, Medium: 82, Hard: 34) presents a more balanced curve, with a higher proportion of Easy questions. This pattern is common for large service-based firms where interviews assess fundamental programming competency and logical thinking across a vast pool of candidates, from new graduates to experienced hires. The presence of Hard questions, though smaller in proportion than LinkedIn's, still necessitates preparation for complex scenarios.

Topic Overlap

Analyzing the most frequent topics highlights common ground and key divergences.

Common Ground: Both companies heavily feature Array and String manipulation questions. These form the bedrock of algorithmic interviews, testing basic data structure handling, iteration, and edge-case management.

LinkedIn's Focus: Beyond arrays and strings, LinkedIn's list highlights Hash Table and Depth-First Search (DFS). The hash table emphasis points to a need for optimal lookup solutions and handling frequency counting problems. The prominence of DFS signals that tree and graph traversal—essential for features like network connections or hierarchical data—is a critical area.

# Example: DFS in a graph (common LinkedIn pattern)
def dfs(node, visited, graph):
    if not node or node in visited:
        return
    visited.add(node)
    # Process node
    for neighbor in graph[node]:
        dfs(neighbor, visited, graph)

Infosys's Focus: Infosys's list shows a strong emphasis on Dynamic Programming (DP) and Math. The DP focus requires mastery of breaking down complex problems into overlapping subproblems (e.g., knapsack, longest common subsequence). The Math category includes problems related to number theory, combinatorics, and numerical computations, which are prevalent in general problem-solving and client project scenarios.

# Example: DP - Fibonacci (common Infosys pattern)
def fib(n, memo={}):
    if n in memo:
        return memo[n]
    if n <= 2:
        return 1
    memo[n] = fib(n-1, memo) + fib(n-2, memo)
    return memo[n]

Which to Prepare for First

Your preparation priority should align with your target role and current skill level.

If you are aiming for a software engineering role at a product-based company like LinkedIn, prioritize their profile. Start with a strong foundation in arrays, strings, and hash tables, then drill deeply into graph and tree traversal algorithms (DFS, BFS). Practice a high volume of Medium-difficulty problems to build the stamina and pattern recognition needed for their interviews.

If you are preparing for Infosys or similar IT services firms, begin with their list. Solidify your fundamentals in arrays, strings, and mathematical problem-solving. Then, dedicate significant time to understanding Dynamic Programming patterns, as this is a frequent differentiator. While the difficulty curve may be gentler, a methodical approach to problem decomposition is crucial.

For a generalized strategy, start with the shared fundamentals: arrays and strings. These are universal. Then, incorporate hash table practice (beneficial for both) before branching into your target company's specialties—DFS for LinkedIn or DP/Math for Infosys. This approach ensures you build a versatile core competency.

For more detailed question lists and patterns, visit the LinkedIn and Infosys company pages: LinkedIn Interview Questions | Infosys Interview Questions