Salesforce vs LinkedIn: Interview Question Comparison

When preparing for technical interviews at top tech companies, understanding the specific patterns and problem types each company favors can significantly increase your chances of success. Salesforce and LinkedIn are both major players, but their engineering interviews have distinct flavors. This comparison breaks down their question banks by volume, difficulty, and core topics to help you strategize your preparation.

Question Volume and Difficulty

The total number of questions and their difficulty distribution is the first point of comparison.

Salesforce has a catalog of 189 questions, categorized as Easy (27), Medium (113), and Hard (49). This breakdown reveals a strong emphasis on Medium-difficulty problems, which form nearly 60% of their question bank. The significant portion of Hard questions (about 26%) indicates that interviews can delve into complex algorithmic challenges, testing not just correctness but also optimal solutions under constraints.

LinkedIn has a slightly smaller set of 180 questions, with a distribution of Easy (26), Medium (117), and Hard (37). Similar to Salesforce, Medium problems dominate, making up about 65% of their questions. However, LinkedIn has a smaller proportion of Hard questions (roughly 20%) compared to Salesforce, suggesting their interviews might place a relatively stronger emphasis on solving Medium problems flawlessly and efficiently.

In summary, both require mastery of Medium problems, but you should be prepared for a slightly higher chance of encountering a rigorous Hard problem at Salesforce.

Topic Overlap

Analyzing the frequently tested topics shows where your study efforts will have the highest return for both companies.

There is substantial overlap in the most common topics. Array, String, and Hash Table problems are foundational for both Salesforce and LinkedIn. These often form the basis for questions involving two-pointer techniques, sliding windows, and frequency counting.

# Example: A common Hash Table problem (Two Sum)
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 []

The key divergence is in the next tier of topics. Salesforce prominently features Dynamic Programming (DP). This signals that you must be comfortable with classic DP patterns for optimization and counting problems, such as those involving sequences or grids.

LinkedIn, on the other hand, frequently tests Depth-First Search (DFS). This points to a focus on graph and tree traversal problems, which are common in scenarios involving hierarchical data, networks, or pathfinding—areas relevant to a social network's infrastructure.

# Example: DFS on a binary tree (LinkedIn-relevant)
def dfs(node):
    if not node:
        return
    # Process node here
    dfs(node.left)
    dfs(node.right)

Which to Prepare for First

Your preparation order should be guided by the shared foundation and your target companies.

Start with the common core: Array, String, and Hash Table problems. Achieving fluency with these will build a strong base applicable to both companies. Practice Medium-difficulty problems from these categories until you can identify patterns and implement solutions quickly.

If you are interviewing with both companies, prioritize this common core, then split your focus. Dedicate time to mastering Dynamic Programming paradigms (like knapsack, LCS, or house robber variants) for Salesforce. Concurrently, drill into graph representations, tree traversals (DFS/BFS), and recursive backtracking for LinkedIn.

If you must choose one to prepare for first, consider your strengths. If you are more comfortable with graph theory and recursive thinking, LinkedIn's focus on DFS might feel more natural to start with. If you excel at breaking down optimization problems into overlapping subproblems, begin with Salesforce's DP-heavy list. Ultimately, the shared emphasis on Medium problems means that rigorous practice on general data structures and algorithms will serve you well for either.

For focused practice, visit the Salesforce and LinkedIn question lists: Salesforce Interview Questions | LinkedIn Interview Questions