LinkedIn Coding Interview Questions: The Consistent Bank

LinkedIn has a reputation among candidates for an unusually consistent question bank — the same problems and close variants recur across loops. That makes targeted preparation especially effective: if you drill LinkedIn's frequently-tagged list, you'll often recognize the exact problem or a one-step variant.

This guide covers five problems LinkedIn asks repeatedly, with worked Python, the full loop including the host-matching round, and how to prepare efficiently against a stable bank.

The full interview process

Stage	Format	Notes
Recruiter screen	30 min	Background, level, team interests
Technical phone screen	45 min, 1-2 problems	On a shared editor; clean code expected
Onsite coding (2)	45 min each	DS&A from LinkedIn's recurring set
System design (mid/senior)	45 min	Feed, connections, or messaging at scale
Host / behavioral round	45 min	Values, collaboration, "why LinkedIn"
Team match	Varies	Placement into a specific group

Problem 1: Maximum Subarray (Kadane's algorithm)

Question: Find the contiguous subarray with the largest sum and return that sum.

A LinkedIn staple. Kadane's algorithm: at each element, either extend the current subarray or start fresh from this element, tracking the best sum seen.

def maxSubArray(nums):
    best = curr = nums[0]
    for num in nums[1:]:
        curr = max(num, curr + num)
        best = max(best, curr)
    return best

Time O(n), space O(1). The one-line insight: curr = max(num, curr + num) — you only keep extending while the running sum helps. If asked to return the subarray itself, track start/end indices as curr resets.

Follow-up: "Return the actual subarray, not just the sum." Record the start index whenever you reset curr = num, and the end whenever you update best.

Problem 2: Search in Rotated Sorted Array (binary search)

Question: A sorted array is rotated at an unknown pivot. Find the index of a target value in O(log n), or -1.

Modified binary search. At each step one half is sorted; decide which half could contain the target and recurse into it.

def search(nums, target):
    lo, hi = 0, len(nums) - 1
    while lo <= hi:
        mid = (lo + hi) // 2
        if nums[mid] == target:
            return mid
        if nums[lo] <= nums[mid]:          # left half sorted
            if nums[lo] <= target < nums[mid]:
                hi = mid - 1
            else:
                lo = mid + 1
        else:                              # right half sorted
            if nums[mid] < target <= nums[hi]:
                lo = mid + 1
            else:
                hi = mid - 1
    return -1

Time O(log n), space O(1). The key is determining which half is sorted (compare nums[lo] and nums[mid]), then checking whether the target falls in that sorted half's range. Get the boundary comparisons exactly right.

Problem 3: Nested List Weight Sum (DFS)

Question: Given a nested list of integers, return the sum of each integer multiplied by its depth (depth 1 at the top level).

A LinkedIn signature. DFS the structure, carrying the current depth; add integers weighted by depth and recurse into sub-lists with depth+1.

def depthSum(nestedList):
    def dfs(items, depth):
        total = 0
        for item in items:
            if item.isInteger():
                total += item.getInteger() * depth
            else:
                total += dfs(item.getList(), depth + 1)
        return total
    return dfs(nestedList, 1)

Time O(total elements), space O(max depth) for the recursion stack. LinkedIn also asks the inverse-weight variant (deeper = lighter); for that, do a BFS to find max depth first, then weight by (maxDepth - depth + 1).

Problem 4: Lowest Common Ancestor of a BST

Question: Given a binary search tree and two nodes, return their lowest common ancestor.

Use the BST property: if both targets are smaller than the current node, go left; if both larger, go right; otherwise the current node is the split point — the LCA.

def lowestCommonAncestor(root, p, q):
    node = root
    while node:
        if p.val < node.val and q.val < node.val:
            node = node.left
        elif p.val > node.val and q.val > node.val:
            node = node.right
        else:
            return node

Time O(h) where h is tree height, space O(1) iteratively. The BST ordering makes this far simpler than the general-tree LCA — recognizing you can exploit the ordering is the signal LinkedIn grades.

Patterns LinkedIn asks most

Pattern	Frequency	Note
Arrays & Kadane-style DP	~20% of loops	Maximum subarray and variants
Binary search	~20%	Rotated arrays, search ranges
Trees / BST	~20%	LCA, traversals, BST validation
DFS on nested structures	~15%	Nested list weight sum
Hash map / two pointers	~15%	Shortest word distance, two sum
Heap	~10%	Merge k sorted lists

Common pitfalls specific to LinkedIn

• Not exploiting the consistent bank. LinkedIn reuses problems — if you skip its frequently-tagged list, you're leaving easy recognition on the table.
• Botching binary-search boundaries. The rotated-array problem fails on off-by-one comparisons. Practice it until the four branches are automatic.
• Missing the BST shortcut. Solving LCA with a general-tree algorithm works but signals you didn't notice the ordering. Use it.
• Ignoring the host round. LinkedIn's culture/values round is scored. Prepare specific stories and a real reason for LinkedIn.

A 4-week prep plan for a LinkedIn loop

1. Week 1: Drill LinkedIn's frequently-tagged problems on LeetCode (filter by company "LinkedIn," sort by frequency). Recognition is half the battle here.
2. Week 2: Trees and binary search to mastery — LCA variants, rotated arrays, BST validation.
3. Week 3: Mixed timed sets plus the system design cheat sheet for mid/senior loops.
4. Week 4: Host-round stories and a solo mock.

FAQ

Is LinkedIn's question bank really that consistent?

Yes - LinkedIn is known for reusing a fairly stable set of problems and close variants. Targeted prep against its frequently-tagged list is unusually effective compared with companies that rotate questions aggressively.

What problems does LinkedIn ask most?

Maximum subarray (Kadane's), search in rotated sorted array, nested list weight sum, lowest common ancestor in a BST, and shortest word distance are among the most reliably reported. Arrays, binary search, and trees dominate.

How many rounds is the LinkedIn interview?

Typically a phone screen, two onsite coding rounds, a system design round for mid and senior candidates, and a host/values round - followed by team matching.

What is the LinkedIn host round?

A culture-and-values interview focused on collaboration, growth, and why you want to work at LinkedIn. It's scored, so prepare specific stories rather than treating it as a formality.

Can CoPilot Interview help me prepare for LinkedIn?

Yes. It surfaces optimal solutions with Big-O so you can drill LinkedIn's recurring problems until recognition is instant. Always follow LinkedIn's stated rules during the live interview.