Google vs LinkedIn: Interview Question Comparison

When preparing for technical interviews at top tech companies, understanding the specific focus and patterns of each company's question bank can dramatically increase your efficiency. Google and LinkedIn, while both requiring strong algorithmic skills, show distinct differences in their approach to technical screening, as evidenced by their LeetCode question lists. This comparison breaks down the key metrics—volume, difficulty, and topic emphasis—to help you tailor your preparation strategy.

Question Volume and Difficulty

The most striking difference is the sheer scale of the question pools. Google's list, with 2,217 questions, is over 12 times larger than LinkedIn's 180 questions. This volume reflects Google's vast historical interview data and the broader range of problems candidates might encounter.

The difficulty distribution reveals another critical strategic point:

• Google (E588/M1153/H476): The distribution is relatively balanced but leans medium-heavy. Medium questions constitute the majority (52%), with a significant portion of Easy (27%) and Hard (21%) questions. This suggests that while foundational mastery is tested, the interview is designed to push candidates into complex problem-solving.
• LinkedIn (E26/M117/H37): The distribution is heavily skewed towards Medium difficulty. A full 65% of questions are Medium, with Hard questions making up 21% and Easy only 14%. This indicates LinkedIn's interview process is intensely focused on core algorithmic concepts applied in non-trivial scenarios, with less emphasis on simple warm-up problems.

Topic Overlap

Both companies heavily test fundamental data structures. Array, String, and Hash Table problems are central to both question banks, underscoring their universal importance.

The key divergence is in the fourth most frequent topic:

• Google emphasizes Dynamic Programming (DP). The presence of 476 Hard questions, many of which are DP, signals that Google frequently assesses a candidate's ability to handle optimization problems, recursive thinking, and state management. Mastery of DP patterns (knapsack, LCS, palindromic subsequences, etc.) is crucial.
• LinkedIn emphasizes Depth-First Search (DFS). This points towards a stronger focus on graph and tree traversal problems, including recursive backtracking, pathfinding, and connected components. This aligns with LinkedIn's network-based product domain.

Example: A Common Overlap (Two Sum Problem)

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: A Diverging Focus (Google DP vs. LinkedIn DFS)

# Google-style DP: Climbing Stairs
def climbStairs(n):
    if n <= 2:
        return n
    dp = [0] * (n + 1)
    dp[1], dp[2] = 1, 2
    for i in range(3, n + 1):
        dp[i] = dp[i-1] + dp[i-2]
    return dp[n]

# LinkedIn-style DFS: Number of Islands
def numIslands(grid):
    def dfs(r, c):
        if (r < 0 or c < 0 or r >= rows or c >= cols or
            grid[r][c] == '0'):
            return
        grid[r][c] = '0'
        dfs(r+1, c)
        dfs(r-1, c)
        dfs(r, c+1)
        dfs(r, c-1)

    count = 0
    rows, cols = len(grid), len(grid[0])
    for r in range(rows):
        for c in range(cols):
            if grid[r][c] == '1':
                dfs(r, c)
                count += 1
    return count

Which to Prepare for First

Start with LinkedIn. Its smaller, more concentrated question bank allows for efficient, targeted preparation. You can achieve broad coverage of their likely question types—especially Arrays, Strings, Hash Tables, and DFS/Graph problems—in less time. This builds a strong foundation in core algorithms.

Then, move to Google. Use the foundational skills from LinkedIn prep to tackle Google's vast list. Focus on mastering pattern recognition across their large set of Medium problems, and dedicate significant time to Dynamic Programming and other advanced topics (like System Design, which often accompanies their interviews). The volume requires a longer, more sustained study period.

Ultimately, preparing for LinkedIn first creates a efficient on-ramp to the more comprehensive challenge of a Google interview.

For specific question lists and patterns, visit the company pages: Google Interview Questions and LinkedIn Interview Questions.