Navigating the landscape of technical interviews can be daunting, especially when targeting top companies like LinkedIn. These interviews often feature a blend of behavioral questions, system design challenges, and most prominently, coding problems designed to assess your problem-solving abilities and foundational computer science knowledge. Understanding the types of questions frequently asked can significantly boost your confidence and performance, transforming a stressful experience into a structured pathway to success. This guide will help you prepare for common technical questions encountered during LinkedIn interview processes, providing insights into their purpose and effective answering strategies.
What Are LinkedIn Interview Questions?
LinkedIn interview questions, particularly for software engineering roles, are primarily algorithm and data structure problems. These are often similar to challenges found on platforms like LeetCode. They range in difficulty from easy to hard, covering core concepts such as arrays, linked lists, trees, graphs, sorting, searching, dynamic programming, and hash tables. The questions are not just about finding the correct answer but also about demonstrating your thought process, ability to articulate solutions, handle edge cases, and analyze time and space complexity. Recruiters and hiring managers at LinkedIn seek candidates who can think critically under pressure and implement efficient, scalable solutions.
Why Do Interviewers Ask LinkedIn Interview Questions?
Interviewers ask these types of coding challenges for several key reasons. Firstly, they gauge your fundamental understanding of computer science principles and your proficiency in a programming language. Secondly, these questions reveal your problem-solving methodology: how you approach an unfamiliar problem, break it down, identify constraints, and devise an optimal solution. Thirdly, they assess your ability to write clean, correct, and efficient code, which is crucial for collaborative development environments. Finally, technical questions help evaluate your communication skills as you explain your approach and reasoning. A strong performance signals that you possess the necessary analytical rigor and technical skills to contribute effectively to LinkedIn's engineering teams.
Preview List
Two Sum
Merge Two Sorted Lists
Merge k Sorted Lists
Search in Rotated Sorted Array
Permutations
Pow(x, n)
Maximum Subarray
Merge Intervals
Symmetric Tree
Binary Tree Level Order Traversal
Maximum Depth of Binary Tree
Word Ladder
Max Points on a Line
Evaluate Reverse Polish Notation
Maximum Product Subarray
Reverse Linked List
Reorder List
LRU Cache
Subarray Sum Equals K
Longest Increasing Subsequence
Minimum Window Substring
Maximum Gap
Two Sum II - Input Array Is Sorted
Binary Search
Diameter of Binary Tree
Search a 2D Matrix
Clone Graph
First Missing Positive
Insert Interval
Validate Binary Search Tree
1. Two Sum
Why you might get asked this:
This classic problem tests your understanding of hash tables (dictionaries) for efficient lookups and basic array traversal, often a good warm-up for more complex problems.
How to answer:
Use a hash map to store numbers and their indices as you iterate. For each number, check if the complement (target - current_num) is already in the map.
Example answer:
I'd iterate through the array, storing each number and its index in a hash map. For each number n, I'd check if target - n exists in the map. If it does, I've found the pair and return their indices. If not, I add n and its index to the map.
2. Merge Two Sorted Lists
Why you might get asked this:
Evaluates your ability to manipulate linked lists, handle pointers, and merge sorted data structures, a fundamental skill in data processing.
How to answer:
Create a dummy node to simplify list construction. Use a pointer to build the new list by iteratively comparing nodes from both input lists and appending the smaller one.
Example answer:
I'd create a dummy head node for the merged list. Then, I'd use a current pointer, initially pointing to the dummy. I'd iterate while both input lists have nodes, comparing their current values. The node with the smaller value is appended to current.next, and its list pointer advances. Finally, any remaining nodes from either list are appended.
3. Merge k Sorted Lists
Why you might get asked this:
Tests your knowledge of priority queues (min-heaps) and advanced divide-and-conquer strategies for merging multiple sorted data streams.
How to answer:
Use a min-heap to keep track of the smallest current element from all k lists. Repeatedly extract the minimum, add its next element to the heap, and build the merged list.
Example answer:
I would use a min-priority queue (min-heap). Initially, insert the first node from each of the k lists into the heap. Then, repeatedly extract the minimum node from the heap, append it to the result list, and if that node has a next, add next to the heap.
4. Search in Rotated Sorted Array
Why you might get asked this:
A variation of binary search that assesses your ability to adapt standard algorithms to non-standard constraints and find the rotation point.
How to answer:
Apply a modified binary search. Determine which half of the array is sorted. Based on the target's value, decide whether to search in the sorted half or the unsorted half.
Example answer:
I would use a modified binary search. First, find the middle element. Determine if the left or right half is sorted. Then, check if the target falls within the range of the sorted half. Adjust search boundaries (low, high) accordingly, iteratively narrowing down the search space until the target is found or boundaries cross.
5. Permutations
Why you might get asked this:
Commonly tests recursion and backtracking techniques for generating all possible arrangements of elements, important for combinatorial problems.
How to answer:
Employ recursion with backtracking. Maintain a temporary list for current permutation and a boolean array or set to track used elements.
Example answer:
I'd use a recursive backtracking approach. A helper function would build permutations. For each position, iterate through available numbers. If a number hasn't been used, add it to the current permutation, mark it used, and recurse. After the recursive call, remove it and mark it unused (backtrack) to explore other paths.
6. Pow(x, n)
Why you might get asked this:
Checks your understanding of recursive algorithms, handling edge cases (negative exponents, zero), and optimization using exponentiation by squaring.
How to answer:
Implement using recursion and the property that x^n = (x^(n/2))^2. Handle negative exponents by inverting the result for x^(-n).
Example answer:
I would implement this using a recursive approach leveraging exponentiation by squaring. If n is 0, return 1. If n is negative, calculate x^(-n) and then return 1/result. For positive n, compute half = myPow(x, n/2). If n is even, return half half. If n is odd, return x half * half.
7. Maximum Subarray
Why you might get asked this:
A classic dynamic programming problem (Kadane's algorithm) that assesses your ability to optimize solutions for contiguous ranges.
How to answer:
Use Kadane's algorithm: iterate through the array, keeping track of the currentmaxsum ending at the current position and the overallmaxsum found so far.
Example answer:
I'd use Kadane's algorithm. Initialize maxsofar to negative infinity and currentmax to 0. Iterate through the array, adding each number to currentmax. If currentmax becomes negative, reset it to 0. Update maxsofar if currentmax is greater. This ensures we always have the largest sum ending at the current point.
8. Merge Intervals
Why you might get asked this:
Tests your ability to sort and iterate through intervals, identifying and merging overlaps, common in scheduling or range management problems.
How to answer:
Sort intervals by their start times. Iterate, merging an interval with the previous one if they overlap, updating the end time.
Example answer:
First, sort the intervals based on their start times. Then, iterate through the sorted intervals. If the current interval overlaps with the last merged interval (i.e., its start is less than or equal to the last merged interval's end), merge them by updating the last merged interval's end to the maximum of both ends. Otherwise, add the current interval as a new merged interval.
9. Symmetric Tree
Why you might get asked this:
Checks your understanding of tree traversals (often recursion) and how to compare two subtrees for structural and value symmetry.
How to answer:
Use a recursive helper function to compare the left child of one node with the right child of another, and vice-versa, for symmetry.
Example answer:
I'd implement a helper function that takes two nodes, say p and q. This function would return true if both p and q are null, or if p and q are non-null AND p.val == q.val AND isSymmetric(p.left, q.right) AND isSymmetric(p.right, q.left). The main function would call this helper with root.left and root.right.
10. Binary Tree Level Order Traversal
Why you might get asked this:
A fundamental tree traversal method (Breadth-First Search) that demonstrates your use of queues and ability to process nodes level by level.
How to answer:
Use a queue. Add the root. In a loop, process all nodes at the current level, add their children to the queue, and then move to the next level.
Example answer:
I would use a queue for Breadth-First Search (BFS). Initialize the queue with the root node. While the queue is not empty, process all nodes at the current level. For each node, add its value to a temporary list for the current level, then enqueue its left and right children if they exist. After processing all nodes at a level, add the temporary list to the overall result.
11. Maximum Depth of Binary Tree
Why you might get asked this:
A simple recursive tree problem that tests your grasp of recursion and base cases for tree structures.
How to answer:
Recursively calculate the depth of the left and right subtrees, and return 1 plus the maximum of the two depths. Base case is a null node (depth 0).
Example answer:
I'd use a recursive approach. If the root is null, the depth is 0. Otherwise, recursively calculate the maximum depth of the left subtree and the right subtree. The overall maximum depth is 1 plus the larger of the two subtree depths.
12. Word Ladder
Why you might get asked this:
A classic Breadth-First Search (BFS) problem on graphs, testing shortest path finding where nodes are words and edges are single-character differences.
How to answer:
Model as a graph problem. Use BFS to find the shortest path from beginWord to endWord, considering valid transformations as edges.
Example answer:
I would build an adjacency list representing words that differ by one character. Then, use BFS starting from beginWord. A queue stores (word, level). Mark visited words. When endWord is reached, its level is the shortest path length. If the queue becomes empty and endWord isn't found, no path exists.
13. Max Points on a Line
Why you might get asked this:
Geometric problem testing your ability to handle floating-point precision, calculate slopes, and use hash maps for counting collinear points.
How to answer:
Iterate through pairs of points. For each pair, calculate the slope. Use a hash map to store slopes and their counts for a fixed first point. Handle vertical lines and duplicate points separately.
Example answer:
For each point i, iterate through all subsequent points j. Calculate the slope between i and j (handling vertical lines and duplicate points). Store slopes in a hash map, incrementing counts. The maximum count for point i combined with duplicates, plus 1 (for point i itself), gives the max points for lines passing through i. Track the overall maximum.
14. Evaluate Reverse Polish Notation
Why you might get asked this:
Tests your use of stacks for parsing and evaluating expressions, a common problem in compiler design and basic arithmetic.
How to answer:
Use a stack. Iterate through the tokens. If a token is a number, push it onto the stack. If it's an operator, pop the top two numbers, perform the operation, and push the result back.
Example answer:
I would use a stack. Iterate through the given RPN tokens. If a token is an operand (number), push it onto the stack. If it's an operator (+, -, *, /), pop the top two operands from the stack, perform the operation, and then push the result back onto the stack. After processing all tokens, the final result will be the only item left on the stack.
15. Maximum Product Subarray
Why you might get asked this:
A dynamic programming problem that's tricky due to negative numbers. Tests careful state management to track both maximum and minimum products.
How to answer:
Iterate through the array, maintaining currentmaxproduct and currentminproduct ending at the current position. Update these by considering the current number itself, or currentnum * currentmax, or currentnum * currentmin.
Example answer:
I would track both the maxsofar and minsofar product ending at the current position. When iterating, for each number n, calculate potential new maxprod and minprod by multiplying n with maxsofar and minsofar. The new currentmax will be the max of n, n * maxprodprev, n * minprodprev. Similarly for currentmin. Update overall max.
16. Reverse Linked List
Why you might get asked this:
A fundamental linked list manipulation problem. Tests your understanding of pointer reassignments.
How to answer:
Iteratively: use three pointers (previous, current, next). In each step, reverse the current node's next pointer to previous.
Example answer:
I'd use three pointers: prev (initially None), curr (initially head), and nextnode. In a loop while curr is not None, store curr.next in nextnode, then set curr.next = prev. Update prev = curr and curr = next_node. Finally, prev will point to the new head of the reversed list.
17. Reorder List
Why you might get asked this:
Combines several linked list techniques: finding middle, reversing, and merging, to achieve a specific reordering pattern.
How to answer:
Find the middle of the list. Split into two halves. Reverse the second half. Merge the two halves by interleaving nodes.
Example answer:
First, find the middle of the linked list using fast and slow pointers. Second, split the list into two halves at the middle. Third, reverse the second half of the list. Finally, merge the two lists by taking one node from the first half, then one from the reversed second half, and so on, until both lists are exhausted.
18. LRU Cache
Why you might get asked this:
Tests your ability to design a data structure combining a hash map (for O(1) lookups) and a doubly linked list (for O(1) recency updates), crucial for efficient caching.
How to answer:
Implement using a hash map to store key-node pairs and a doubly linked list to maintain access order. Custom nodes will store key and value.
Example answer:
I would implement this using a HashMap to store key: Node mappings for O(1) access and a doubly linked list to maintain the order of usage (most recently used at the tail, least recently used at the head). get operations move the accessed node to the tail. put operations add a new node to the tail or move an existing one; if capacity is exceeded, the head node is removed.
19. Subarray Sum Equals K
Why you might get asked this:
A good problem for prefix sums and hash maps. Tests how to efficiently count subarrays with a specific sum.
How to answer:
Use a hash map to store prefix sums and their frequencies. Iterate, calculate current prefix sum, and check if current_sum - k exists in the map.
Example answer:
I would use a hash map to store the frequency of prefix sums. Initialize sum = 0 and count = 0. Put (0, 1) into the map (for empty prefix sum). Iterate through the array, add current number to sum. Check if sum - k exists in the map; if so, add its frequency to count. Finally, increment the frequency of sum in the map.
20. Longest Increasing Subsequence
Why you might get asked this:
A classic dynamic programming problem. Tests your understanding of memoization or tabulation for optimal substructure. Can also be solved with N log N using binary search.
How to answer:
Use dynamic programming: dp[i] stores the length of LIS ending at nums[i]. Iterate i from 0 to n-1, for each i, iterate j from 0 to i-1.
Example answer:
I would use dynamic programming. Create a dp array where dp[i] is the length of the longest increasing subsequence ending with nums[i]. Initialize all dp[i] to 1. Then, for each nums[i], iterate through previous elements nums[j] (where j < i). If nums[i] > nums[j], update dp[i] = max(dp[i], dp[j] + 1). The maximum value in the dp array is the result.
21. Minimum Window Substring
Why you might get asked this:
A sliding window problem that checks your ability to manage two pointers and character frequencies (hash maps) for substring search.
How to answer:
Use a sliding window with two pointers (left, right) and hash maps to track character counts in the window and required counts for the target string.
Example answer:
I'd use a sliding window approach with two pointers, left and right. Maintain a frequency map for characters needed from string T and another for characters in the current window of string S. Expand the window with right. When all characters from T are covered, contract the window with left, trying to minimize its size, while still meeting the criteria. Track the minimum valid window found.
22. Maximum Gap
Why you might get asked this:
Often requires bucket sort or counting sort principles to achieve linear time complexity, testing knowledge beyond comparison sorts.
How to answer:
Sort the array. Then, iterate through the sorted array to find the maximum difference between adjacent elements. (For O(N), use bucket sort if range permits).
Example answer:
A straightforward approach is to sort the array and then iterate through the sorted elements, calculating the difference between adjacent elements. The maximum of these differences is the answer. For an optimal O(N) solution, a bucket sort strategy could be used where elements are distributed into buckets based on their values.
23. Two Sum II - Input Array Is Sorted
Why you might get asked this:
A variant of Two Sum that leverages the sorted input for an optimized two-pointer solution instead of a hash map.
How to answer:
Use two pointers, one at the beginning and one at the end. Adjust them inward based on whether their sum is less than, greater than, or equal to the target.
Example answer:
Since the array is sorted, I would use a two-pointer approach. Initialize a left pointer at the beginning and a right pointer at the end of the array. While left < right, calculate their sum. If it equals the target, return their indices. If the sum is less than the target, increment left. If greater, decrement right.
24. Binary Search
Why you might get asked this:
A fundamental algorithm. Tests your understanding of divide and conquer, boundary conditions (low, high, mid), and iteration/recursion.
How to answer:
Repeatedly divide the search interval in half. Compare the target value with the middle element. If they match, return index. Adjust interval (left or right half) based on comparison.
Example answer:
I would implement an iterative binary search. Initialize left to 0 and right to n-1. While left <= right, calculate mid. If nums[mid] equals the target, return mid. If nums[mid] < target, set left = mid + 1. Else, set right = mid - 1. If the loop finishes, the target isn't found.
25. Diameter of Binary Tree
Why you might get asked this:
Tests recursive tree traversal and tracking maximum path lengths, which involve paths that might not pass through the root.
How to answer:
Use a recursive function that returns the height of the current node. Simultaneously, track the maximum diameter found so far by considering the sum of left and right subtree heights at each node.
Example answer:
I'd use a recursive helper function that calculates the height of each subtree while also updating a global maxdiameter variable. At each node, the maxdiameter is updated with leftheight + rightheight. The function then returns 1 + max(leftheight, rightheight) for its own height calculation.
26. Search a 2D Matrix
Why you might get asked this:
Combines binary search logic, often treating the 2D matrix as a flattened 1D sorted array, or optimizing by starting from a corner.
How to answer:
Treat the matrix as a single sorted array and apply binary search, converting 1D index to 2D coordinates. Alternatively, start from top-right or bottom-left corner and move based on target.
Example answer:
I would perform a modified binary search. Consider the matrix as a flattened sorted array. Calculate mid index, then convert it to 2D coordinates (row, col). Compare matrix[row][col] with the target. Adjust the low or high pointers based on the comparison, narrowing down the search space until the target is found or bounds cross.
27. Clone Graph
Why you might get asked this:
A fundamental graph traversal problem that tests your understanding of BFS/DFS and handling visited nodes to prevent infinite loops and correctly copy edges.
How to answer:
Use BFS or DFS. Maintain a hash map to store mappings from original nodes to their cloned counterparts to prevent re-cloning and handle cycles.
Example answer:
I would use BFS with a hash map. Add the starting node to a queue and its clone to the map. While the queue is not empty, dequeue a node. For each neighbor of this node, if it's not in the map, create its clone, add to map and queue. Then, establish the edge between the current node's clone and its neighbor's clone.
28. First Missing Positive
Why you might get asked this:
A challenging array manipulation problem often requiring in-place modification or specific data structure use to achieve O(N) time and O(1) space.
How to answer:
Utilize the input array itself as a hash map. Place each number x at index x-1 if 1 <= x <= n. Then, iterate to find the first index i where nums[i] != i+1.
Example answer:
I would iterate through the array, attempting to place each positive number nums[i] at its correct index (nums[i]-1). If nums[i] is in the range [1, n] and nums[i] is not already at its correct place, swap it. After this "placement" phase, iterate from 0 to n-1; the first index i where nums[i] is not i+1 is the first missing positive. If all are in place, n+1 is missing.
29. Insert Interval
Why you might get asked this:
Tests your ability to manage and merge intervals, similar to "Merge Intervals" but with a single new interval.
How to answer:
Iterate through existing intervals. Add intervals that come before the new one. Merge overlapping intervals with the new one. Add remaining intervals.
Example answer:
I would iterate through the existing intervals. First, add all intervals that end before newInterval starts to the result list. Then, merge any intervals that overlap with newInterval by updating newInterval's start to the minimum and end to the maximum. Finally, add the (potentially merged) newInterval to the result, and then add any remaining intervals from the original list.
30. Validate Binary Search Tree
Why you might get asked this:
A crucial tree property. Tests recursive traversal and the passing of valid range boundaries to ensure BST properties are upheld.
How to answer:
Use recursion, passing minval and maxval to children. Left child must be < parent and > minval. Right child must be > parent and < maxval.
Example answer:
I'd use a recursive helper function that takes a node, a minval, and a maxval. For a given node, check if its value is within (minval, maxval). Then, recursively call for the left child with (minval, node.val) and for the right child with (node.val, maxval). Initialize the initial call with (-infinity, +infinity).
Other Tips to Prepare for a LinkedIn Interview Questions
Preparing for LinkedIn interview questions requires a holistic approach that goes beyond just memorizing solutions. "The only way to do great work is to love what you do," and for many engineers, that includes the thrill of solving complex problems. Start by solidifying your understanding of core data structures and algorithms. Practice regularly on platforms like LeetCode, focusing on understanding the underlying patterns rather than just passing test cases. Actively engaging with a diverse range of problems will build your problem-solving muscle.
Crucially, practice explaining your thought process aloud. Interviewers are evaluating your communication as much as your code. Consider using tools like Verve AI Interview Copilot to simulate real interview scenarios and get instant feedback on your answers. Verve AI Interview Copilot can help you refine your explanations, identify areas for improvement, and boost your confidence. Don't shy away from asking clarifying questions during the interview—it shows critical thinking. Remember, "Success is not final, failure is not fatal: it is the courage to continue that counts." Leverage resources like Verve AI Interview Copilot (https://vervecopilot.com) to rehearse and perfect your delivery, ensuring you articulate your solutions clearly and concisely. Review common behavioral questions too, as LinkedIn interviews often blend technical and fit assessments. Mastering both technical depth and clear communication will set you apart.
Frequently Asked Questions
Q1: How much time should I dedicate to preparing for LinkedIn interview questions?
A1: Dedicate 4-12 weeks, depending on your experience. Consistent daily practice, even 1-2 hours, is more effective than cramming.
Q2: Should I focus on specific programming languages for LinkedIn interviews?
A2: Most LinkedIn roles allow popular languages like Python, Java, or C++. Choose one you're most comfortable with and proficient in.
Q3: Are system design questions common in LinkedIn interviews?
A3: Yes, for mid-level to senior roles, system design is a critical component, testing your ability to design scalable distributed systems.
Q4: How important is understanding time and space complexity?
A4: Extremely important. You'll be expected to analyze and optimize your solutions, justifying your choices with complexity analysis.
Q5: What if I get stuck on a problem during the interview?
A5: Communicate your thought process. Talk through your ideas, even partial ones, and ask clarifying questions. This shows problem-solving approach.
Q6: Does LinkedIn ask behavioral questions alongside technical ones?
A6: Absolutely. Behavioral questions are used to assess your teamwork, leadership, and how you handle challenges and failures.
