LinkedIn vs Nutanix: Interview Question Comparison

When preparing for technical interviews, understanding the specific focus and expectations of each company is crucial for efficient study. LinkedIn and Nutanix, while both prominent tech employers, present distinct interview landscapes in terms of volume, difficulty, and topical emphasis. A strategic comparison helps candidates allocate their preparation time effectively.

Question Volume and Difficulty

The most striking difference is the sheer scale of the question banks. LinkedIn's list of 180 questions dwarfs Nutanix's 68. This volume suggests LinkedIn's interview process may draw from a broader set of problems or that its question pool has been more extensively documented by candidates.

Analyzing the difficulty distribution reveals further contrasts:

• LinkedIn (E26/M117/H37): Medium difficulty questions dominate, comprising about 65% of the total. This indicates a strong emphasis on problems that require applying core algorithms to moderately complex scenarios. The significant portion of Hard questions (≈21%) signals that senior or more challenging roles will test advanced problem-solving and optimization.
• Nutanix (E5/M46/H17): The distribution is similar in shape but scaled down. Medium questions are again the majority at roughly 68%. However, the absolute number of Hard questions is less than half of LinkedIn's, which could imply a slightly lower ceiling on the complexity required, or simply reflect the smaller overall pool.

This data suggests that for both companies, mastering medium-difficulty problems is the foundational priority. However, preparing for LinkedIn requires stamina across a larger set, while Nutanix preparation can be more focused.

Topic Overlap

The core technical areas tested are nearly identical, highlighting fundamental competencies valued across the industry. Both companies prioritize:

1. Array & String: Manipulation, searching, and slicing.
2. Hash Table: For efficient lookups and frequency counting.
3. Depth-First Search (DFS): For traversing trees and graphs.

This significant overlap is advantageous. Mastering these topics serves as a dual preparation. The implementation patterns for these data structures and algorithms are universal. For example, a sliding window problem on an array or a recursive DFS traversal for a tree path sum will be conceptually the same for both companies.

# Example: DFS to find all root-to-leaf paths (relevant to both)
def dfs_paths(root):
    def dfs(node, current_path, all_paths):
        if not node:
            return
        current_path.append(node.val)
        if not node.left and not node.right:
            all_paths.append(list(current_path))
        else:
            dfs(node.left, current_path, all_paths)
            dfs(node.right, current_path, all_paths)
        current_path.pop()  # backtrack
    all_paths = []
    dfs(root, [], all_paths)
    return all_paths

Which to Prepare for First

Given the high topic overlap, a synergistic preparation strategy is effective. Start with Nutanix's question list. Its smaller, focused set allows you to efficiently achieve coverage of the core topics that are also critical for LinkedIn. Solving Nutanix's ~46 medium-difficulty problems will build a solid foundation in array/string manipulation, hash table usage, and DFS applications.

Once comfortable with that core, expand to LinkedIn's larger question bank. This will expose you to a wider variety of problem framings and edge cases within the same key topics, while also introducing the additional Hard problems you need to tackle. This approach turns the volume difference from a burden into a structured learning path: core mastery first, then breadth and depth.

In essence, Nutanix's list is an excellent curated primer for the shared technical stack. LinkedIn's list then serves as the comprehensive training ground to build endurance and advanced skills. Prioritizing them in this order creates an efficient, staged preparation pipeline for both interview processes.

For targeted practice, explore the company-specific question lists: LinkedIn Interview Questions and Nutanix Interview Questions.