Array Questions at LinkedIn: What to Expect

Array questions dominate LinkedIn’s technical interview landscape, making up roughly 40% of their problem catalog. This emphasis isn’t arbitrary. LinkedIn’s core products—the news feed, connection graphs, job listings, and messaging systems—rely heavily on efficient data sequencing, real-time updates, and in-memory processing of ordered data. Whether it’s ranking feed updates, managing user connections, or processing batches of profile data, the fundamental operations of searching, sorting, filtering, and traversing sequences are performed on array-like structures daily. Mastering arrays is therefore not just about solving algorithm puzzles; it’s about demonstrating the foundational skill needed to work on LinkedIn’s primary data pipelines and features.

What to Expect — Types of Problems

LinkedIn’s array problems tend to focus on practical applications over abstract complexity. You can generally expect a mix of these categories:

• Two Pointers & Sliding Window: Extremely common for problems involving sorted data, subarrays, or removing duplicates. Think merging sorted lists, finding a contiguous subarray meeting a condition (e.g., maximum sum, target average), or the classic "two sum" in a sorted array.
• Sorting & Searching: Questions often test your ability to leverage sorting to simplify a problem or implement efficient search variants. Examples include finding the intersection of arrays, meeting scheduler problems, or finding a missing element.
• In-Place Array Manipulation: A key pattern for optimizing space to O(1). Problems may ask you to move zeros, remove elements, or rearrange items (like the Dutch National Flag problem) without allocating extra space.
• Prefix Sum or Running Aggregates: Useful for questions about subarray sums or averages where you need to answer many queries efficiently.
• Simulation & Matrix Traversal: Since a 2D array (matrix) is just an array of arrays, you’ll encounter problems involving spiral order, rotation, or searching in a 2D sorted matrix.

How to Prepare — Study Tips with One Code Example

Focus on pattern recognition, not memorization. For each problem type above, learn the core technique and practice its variations. Always discuss time and space complexity first. A highly effective strategy is to master the Two Pointers pattern, as it’s versatile and frequently appears.

Consider the classic problem: Given a sorted array, remove duplicates in-place such that each element appears at most twice, and return the new length.

The efficient approach uses a slow pointer to build the "valid" array and a fast pointer to scan through the original. This is O(n) time and O(1) space.

def removeDuplicates(nums):
    if len(nums) <= 2:
        return len(nums)
    slow = 2  # Start from the third position
    for fast in range(2, len(nums)):
        if nums[fast] != nums[slow - 2]:  # Allow at most two duplicates
            nums[slow] = nums[fast]
            slow += 1
    return slow

Recommended Practice Order

1. Start with fundamental operations: binary search, two-sum, and basic two-pointer problems.
2. Move to in-place manipulation: removing elements, moving zeros, and the Dutch National Flag problem.
3. Tackle sliding window problems for subarrays, both fixed and variable length.
4. Practice prefix sum and basic matrix traversal.
5. Finally, combine patterns with medium-difficulty problems that simulate real data tasks, like merging intervals or inserting/deleting in a sorted array.

This progression builds the layered understanding needed to tackle LinkedIn’s array-focused interviews efficiently.

Practice Array at LinkedIn