LinkedIn vs JPMorgan: Interview Question Comparison

When preparing for technical interviews, understanding the specific focus and expectations of each company is crucial for efficient study. LinkedIn and JPMorgan represent two distinct industry domains—tech-first social media and financial services—which is clearly reflected in their technical interview question profiles. This comparison analyzes their question volume, difficulty distribution, and core topics to help you prioritize your preparation.

Question Volume and Difficulty

The total volume of questions and their difficulty spread are the most immediate differentiators.

LinkedIn maintains a significantly larger public question bank of 180 questions. The difficulty distribution is heavily weighted toward medium problems (M117, ~65%), with a substantial number of hard (H37, ~21%) and a smaller set of easy (E26, ~14%) questions. This profile is typical for a top-tier tech company, emphasizing complex problem-solving and algorithmic optimization. You must be comfortable with medium-level challenges as the baseline.

JPMorgan has a more focused set of 78 questions. The distribution skews heavily toward easy and medium difficulties (E25, ~32% and M45, ~58%), with very few hard problems (H8, ~10%). This reflects the interview's focus on assessing fundamental programming competency, data structure knowledge, and clean code, rather than solving highly complex algorithmic puzzles under extreme time pressure.

In short, LinkedIn's interview will feel like a standard, rigorous software engineering coding round, while JPMorgan's will test core proficiency with generally more approachable problems.

Topic Overlap

Both companies emphasize foundational data structures, but the depth of required knowledge diverges.

The core overlapping topics are Array, String, and Hash Table. For these, the problem-solving approach is similar across both companies. You should master techniques like two-pointers, sliding window, and prefix sums for arrays/strings, and know hash maps for efficient lookups and frequency counting.

# Example: Two-pointer for a sorted array (common for both)
def two_sum_sorted(numbers, target):
    left, right = 0, len(numbers) - 1
    while left < right:
        current_sum = numbers[left] + numbers[right]
        if current_sum == target:
            return [left + 1, right + 1]
        elif current_sum < target:
            left += 1
        else:
            right -= 1
    return []

The key difference is in the additional topics. LinkedIn explicitly lists Depth-First Search, indicating a requirement for proficiency in tree and graph traversal, recursion, and backtracking—common in advanced algorithm interviews. JPMorgan lists Sorting as a primary topic, underscoring the importance of understanding fundamental algorithms, time/space complexity, and implementing or leveraging sorts to solve problems.

Which to Prepare for First

Your preparation order should be guided by your target industry and the foundational nature of the content.

Prepare for JPMorgan first if: You are new to technical interviews or are targeting finance/quant roles. The question set is smaller and the difficulty is lower, allowing you to solidify your grasp on absolute fundamentals (arrays, strings, hash tables, sorting) without being overwhelmed. Success here builds confidence. The skills you gain are 100% transferable to the base-level questions at any tech company, including LinkedIn.

Prepare for LinkedIn first if: Your primary goal is a software engineering role at a tech giant. The broad and challenging question bank will force you to level up your algorithmic thinking comprehensively. Mastering LinkedIn's profile means you will easily cover all of JPMorgan's core topics and difficulties. The reverse is not true; being ready for JPMorgan does not prepare you for LinkedIn's DFS problems or its volume of medium-hard challenges.

A strategic hybrid approach is to use JPMorgan's topics as your foundational study list. Once you can reliably solve easy and medium problems on arrays, strings, hash tables, and sorting, you will have the base to tackle LinkedIn's question bank. Then, layer on dedicated study for Depth-First Search and other advanced graph/tree algorithms to meet LinkedIn's full scope.

For focused practice, visit the company pages: LinkedIn Interview Questions and JPMorgan Interview Questions.