Preparing for a software engineering interview at Google can be a challenging yet rewarding journey. Google is renowned for its rigorous technical interviews, which heavily emphasize data structures, algorithms, and problem-solving skills. Candidates are expected to demonstrate not only their coding proficiency but also their ability to think critically, communicate their thought process, and arrive at optimal solutions. Mastering common algorithmic patterns and typical interview questions, particularly those found on platforms like Leetcode, is paramount for success. This guide provides an in-depth look at 30 frequently asked Google Leetcode interview questions, offering insights into their underlying concepts and effective solution strategies.
What Are Google Leetcode Interview Questions?
Google Leetcode interview questions are a specific subset of algorithmic problems often found on the Leetcode platform that are frequently utilized by Google in their technical interviews. These questions cover a wide range of computer science fundamentals, focusing on evaluating a candidate's grasp of core data structures like arrays, linked lists, trees, graphs, and hash maps, as well as algorithmic paradigms such as dynamic programming, recursion, binary search, and sorting. The problems often require more than just a brute-force approach; interviewers look for optimized solutions in terms of time and space complexity. They are designed to simulate real-world problem-solving scenarios, assessing how a candidate approaches a complex problem from understanding to implementation and optimization.
Why Do Interviewers Ask Google Leetcode Interview Questions?
Interviewers at Google ask Leetcode-style questions for several key reasons. Firstly, these questions provide a standardized way to assess a candidate's foundational computer science knowledge and their ability to apply theoretical concepts to practical problems. They reveal how a candidate designs efficient algorithms, manages edge cases, and writes clean, maintainable code. Secondly, the problem-solving process itself is highly valuable: interviewers observe how candidates break down complex problems, articulate their thought process, handle constraints, and optimize solutions. This communication aspect is crucial, as engineers at Google frequently collaborate on intricate systems. Finally, these questions help gauge a candidate's resilience, creativity, and their ability to perform under pressure, mirroring the demands of a fast-paced development environment.
Two Sum
Add Two Numbers
Median of Two Sorted Arrays
Longest Substring Without Repeating Characters
Reverse Linked List
Merge Intervals
Three Sum
Binary Tree Inorder Traversal
Validate Binary Search Tree
Number of Islands
Clone Graph
Word Ladder
Serialize and Deserialize Binary Tree
Pow(x, n)
LRU Cache
Top K Frequent Elements
Kth Largest Element in an Array
Course Schedule
Subsets
Product of Array Except Self
Search in Rotated Sorted Array
Find Minimum in Rotated Sorted Array
Longest Palindromic Substring
Maximum Subarray
Coin Change
Word Search
Meeting Rooms / Intervals
Merge K Sorted Lists
Reverse Words in a String
Basic Calculator
Preview List
1. Two Sum
Why you might get asked this:
This fundamental problem tests your understanding of hash maps for efficient lookups, illustrating your ability to reduce time complexity from O(n^2) to O(n) for searching pairs.
How to answer:
Use a hash map to store numbers and their indices as you iterate. For each number, calculate its "complement" (target - current_num) and check if the complement exists in the hash map.
Example answer:
Given nums = [2, 7, 11, 15], target 9. Iterate: 2. Complement is 7. Store (2, 0). Next, 7. Complement is 2. 2 is in map at index 0. Return [0, 1].
2. Add Two Numbers
Why you might get asked this:
Evaluates your proficiency with linked lists, handling carry-overs, and constructing new lists, mimicking arithmetic operations on non-standard data types.
How to answer:
Iterate through both linked lists simultaneously, summing corresponding digits along with any carry from the previous sum. Create a new linked list to store the results.
Example answer:
l1 = [2,4,3], l2 = [5,6,4] (representing 342 + 465). Sum 2+5=7, no carry. Sum 4+6=0 with 1 carry. Sum 3+4+1=8. Resulting list: [7,0,8].
3. Median of Two Sorted Arrays
Why you might get asked this:
A challenging problem that assesses advanced algorithmic skills, specifically binary search and divide & conquer techniques on sorted arrays to achieve O(log(min(m,n))) complexity.
How to answer:
Use binary search on the smaller array to find a partition point that correctly splits both arrays into two halves, such that all elements in the left halves are less than or equal to elements in the right halves.
Example answer:
To find the median, you need to find the k-th smallest element. By performing binary search on the partition points of the two arrays, you can effectively eliminate half of the search space in each step, converging to the correct median.
4. Longest Substring Without Repeating Characters
Why you might get asked this:
Tests your understanding of the sliding window technique and hash sets/maps to efficiently track character occurrences and manage window boundaries.
How to answer:
Use a sliding window. Expand the window's right boundary, adding characters to a set. If a duplicate is found, shrink the window's left boundary until the duplicate is removed from the set, then update max length.
Example answer:
String "abcabcbb". Window [a], [a,b], [a,b,c]. Next a is repeat. Shrink: [b,c,a]. Max length 3. Window [b,c,a,b]. Repeat b. Shrink: [c,a,b]. Track max length.
5. Reverse Linked List
Why you might get asked this:
A fundamental linked list manipulation problem, assessing your ability to manage pointers and understand iterative or recursive approaches to list reversal.
How to answer:
Iteratively, keep track of prev, curr, and next pointers. For each node, set curr.next to prev, then update prev to curr and curr to next.
Example answer:
1 -> 2 -> 3 -> 4 -> 5. Start with prev=null, curr=1. First step: 1.next=null, prev=1, curr=2. Second: 2.next=1, prev=2, curr=3. Continue until curr is null. Result: 5 -> 4 -> 3 -> 2 -> 1.
6. Merge Intervals
Why you might get asked this:
Tests your ability to handle interval-based problems, requiring sorting and a single-pass merge logic, common in scheduling or resource allocation scenarios.
How to answer:
First, sort the intervals based on their start times. Then, iterate through the sorted intervals, merging overlapping ones by extending the end time of the current merged interval.
Example answer:
Intervals [[1,3],[2,6],[8,10],[15,18]]. Sort: already sorted. Merge [1,3] and [2,6] to [1,6]. [1,6] does not overlap with [8,10]. [8,10] does not overlap [15,18]. Result: [[1,6],[8,10],[15,18]].
7. Three Sum
Why you might get asked this:
A classic problem combining sorting with the two-pointer technique to find unique triplets that sum to zero, testing optimization for unique combinations and duplicates.
How to answer:
Sort the array. Iterate through each element nums[i]. For each nums[i], use two pointers (left and right) on the remaining array to find nums[left] + nums[right] = -nums[i]. Handle duplicates by skipping.
Example answer:
nums = [-1,0,1,2,-1,-4]. Sort: [-4,-1,-1,0,1,2].
For i=0, num[i]=-4: left=1, right=5. Target 4. No pair.
For i=1, num[i]=-1: left=2, right=5. Target 1.[-1,0,1] works. Skip duplicates for num[i].[-1,2,-1] works. The result will be [[-1,-1,2],[-1,0,1]].
8. Binary Tree Inorder Traversal
Why you might get asked this:
A fundamental tree traversal problem, testing your understanding of DFS (Depth-First Search) and its recursive or iterative implementation using a stack.
How to answer:
Recursively, traverse the left subtree, visit the root, then traverse the right subtree. Iteratively, use a stack to push nodes, pop when left is null, process, then move to the right.
Example answer:
For a tree where root=1, root.left=null, root.right=2, 2.left=3:
Recursive: inorder(null), print 1, inorder(2). For 2: inorder(3), print 2, inorder(null). For 3: inorder(null), print 3, inorder(null). Output: 1,3,2.
9. Validate Binary Search Tree
Why you might get asked this:
Checks your understanding of BST properties and how to apply DFS with appropriate boundary conditions (min/max values) to verify the tree's structure.
How to answer:
Use a recursive helper function that takes node, minval, and maxval as arguments. For each node, check if node.val is within (minval, maxval). Update boundaries for recursive calls (e.g., node.left gets (min_val, node.val)).
Example answer:
To validate, check if leftchild.val < root.val < rightchild.val. More accurately, leftsubtreemax < root.val < rightsubtreemin. The recursive approach ensures this by passing down valid range bounds.
10. Number of Islands
Why you might get asked this:
A classic graph/grid traversal problem, assessing your mastery of BFS or DFS to explore connected components and mark visited nodes.
How to answer:
Iterate through the grid. If you find a '1' (land), increment the island count, then use DFS or BFS to traverse all connected '1's, marking them as '0' (water) to avoid recounting.
Example answer:
Grid: [[1,1,0,0],[1,0,0,0],[0,0,1,0]].
Find grid[0][0]=1. Count=1. DFS converts [0][0] and [1][0] to 0.
Next grid[2][2]=1. Count=2. DFS converts [2][2] to 0. Result is 2 islands.
11. Clone Graph
Why you might get asked this:
Tests your graph traversal (BFS/DFS) skills and how to handle node creation and mapping original nodes to their cloned counterparts to prevent infinite loops.
How to answer:
Use BFS or DFS to traverse the graph. Maintain a hash map to store (originalnode, clonednode) pairs. When visiting an original node, create its clone if not already in the map, then process its neighbors recursively.
Example answer:
Graph 1-2, 1-4, 2-3, 3-4. Start BFS from 1. Create clone 1'. Map 1->1'. Add 1 to queue.
Pop 1. For neighbor 2: create 2', map 2->2', add edge 1'-2'. Add 2 to queue.
For neighbor 4: create 4', map 4->4', add edge 1'-4'. Add 4 to queue. Continue similarly.
12. Word Ladder
Why you might get asked this:
A BFS problem disguised as a string problem, testing shortest path finding on an implicit graph where words are nodes and one-letter difference implies an edge.
How to answer:
Use BFS to find the shortest path. Nodes are words, edges exist if words differ by one letter. Start BFS from beginWord, explore neighbors, and mark visited to avoid cycles.
Example answer:
beginWord="hit", endWord="cog", wordList=["hot","dot","dog","lot","log","cog"].
Queue: (hit,1). Dequeue hit. Neighbors hot. Enqueue (hot,2).
Dequeue hot. Neighbors dot, lot. Enqueue (dot,3), (lot,3).
Dequeue dot. Neighbors dog. Enqueue (dog,4).
Dequeue dog. Neighbors cog. Enqueue (cog,5).
Dequeue cog. cog == endWord. Return 5.
13. Serialize and Deserialize Binary Tree
Why you might get asked this:
Assesses advanced tree manipulation, particularly how to represent a tree as a string and reconstruct it, covering pre-order traversal and handling nulls.
How to answer:
Serialize using pre-order traversal, converting the tree into a string. Use a special marker (e.g., "#") for null nodes. Deserialize by parsing the string, reconstructing nodes recursively, and handling markers to create null children.
Example answer:
Tree [1,2,3,null,null,4,5].
Serialize: "1,2,#,#,3,4,#,#,5,#,#".
Deserialize: Read "1", create root. Read "2", create left child. Read "#", left child of 2 is null. Read "#", right child of 2 is null. Read "3", right child of 1 is 3. And so on.
14. Pow(x, n)
Why you might get asked this:
Tests your understanding of binary exponentiation (or exponentiation by squaring), an efficient algorithm to compute powers in O(log n) time, avoiding iterative multiplication.
How to answer:
Handle n=0 (result is 1) and negative n. Use recursion: pow(x, n/2) * pow(x, n/2). If n is odd, multiply by x. For negative n, calculate 1 / pow(x, -n).
Example answer:
pow(2, 10): pow(2,5) pow(2,5). pow(2,5): pow(2,2)pow(2,2)*2.pow(2,2): pow(2,1)pow(2,1). pow(2,1): pow(2,0)pow(2,0)*2.
Base pow(2,0)=1. Then compute back up.
15. LRU Cache
Why you might get asked this:
A classic design problem combining hash maps (for O(1) lookups) and doubly linked lists (for O(1) recency updates), requiring careful management of both data structures.
How to answer:
Implement with a hash map mapping keys to nodes in a doubly linked list. The linked list maintains item order by recency, with the head being most recent and tail being least recent. Update nodes to head on access, evict from tail on capacity overflow.
Example answer:
Capacity 2. put(1,1). List: 1. Map: {1:node1}.put(2,2). List: 1<->2. Map: {1:node1, 2:node2}.get(1). 1 moved to head. List: 2<->1. Map: {...}.put(3,3). Capacity reached. Evict 2 (tail). List: 1<->3. Map: {1:node1, 3:node3}.
16. Top K Frequent Elements
Why you might get asked this:
Tests frequency counting (hash map) and selection of top elements, often involving heaps (min-heap of size K) or bucket sort for optimal performance.
How to answer:
First, count frequencies of all elements using a hash map. Then, use a min-heap to store (frequency, element) pairs. Maintain the heap size K. If heap size exceeds K, pop the smallest frequency element.
Example answer:
Count frequencies:
{1:3, 2:2, 3:1}.Iterate
(val, freq):
Push
(1,3)to heap. Heap[(1,3)].Push
(2,2)to heap. Heap[(2,2),(1,3)]. (Min-heap ensures root is smallest freq)Push
(3,1)to heap. Size3 > k=2. Pop(3,1). Heap[(2,2),(1,3)].
nums=[1,1,1,2,2,3], k=2. Frequencies: {1:3, 2:2, 3:1}.
Min-heap (size 2): push (1,3), push (2,2). Heap: [(2,2), (1,3)].
Push (3,1). (3,1) smaller than (2,2), so pop (2,2). Heap: [(3,1), (1,3)]. Wait, this is wrong. Min-heap always keeps the smallest frequency at top.
The elements should be (value, frequency).
Min-heap (size k): (1,3), (2,2). The heap keeps elements with the smallest frequency at the root.
Push (3,1). (3,1) has smaller frequency than (2,2) so it would be at the top. But if k=2, we want the top 2 most frequent. This requires a max-heap of all elements or a min-heap of size k.
For min-heap of size k:
Result: [1, 2] (order may vary).
17. Kth Largest Element in an Array
Why you might get asked this:
Tests your understanding of selection algorithms, primarily QuickSelect (average O(n)) or heap-based solutions (O(n log K)), both essential for finding order statistics.
How to answer:
Option 1: Use a min-heap of size k. Iterate through nums, push each element. If heap size exceeds k, pop the smallest. The heap's root after iteration is the Kth largest.
Option 2 (QuickSelect): Partition the array around a pivot. If the pivot is at index k, return it. Otherwise, recurse on the appropriate subarray.
Example answer:
Push 3. Heap
[3].Push 2. Heap
[2,3].Push 1. Heap full,
1smaller than root2. Pop2. Push1. Heap[1,3]. (Incorrect logic for min-heap of size k, min-heap keeps smallest at top)Push 3. Heap
[3].Push 2. Heap
[2,3](top is 2).Push 1. Heap
[1,2,3](top is 1). Heap size > k. Pop 1. Heap[2,3].Push 5. Heap
[2,3,5](top is 2). Heap size > k. Pop 2. Heap[3,5].Push 6. Heap
[3,5,6](top is 3). Heap size > k. Pop 3. Heap[5,6].Push 4. Heap
[4,5,6](top is 4). Heap size > k. Pop 4. Heap[5,6].
nums=[3,2,1,5,6,4], k=2.
Min-heap (size 2):
Correct min-heap for Kth largest:nums=[3,2,1,5,6,4], k=2.
Final heap top is 5. Result 5.
18. Course Schedule
Why you might get asked this:
A graph problem requiring topological sort to determine if a valid course order exists (i.e., no cycles). Tests graph representation and cycle detection (DFS or BFS with in-degrees).
How to answer:
Represent courses and prerequisites as a directed graph. Use either DFS to detect cycles (if DFS visits a node already in current recursion stack, there's a cycle) or Kahn's algorithm (BFS with in-degrees, if queue empties before all nodes processed, there's a cycle).
Example answer:
numCourses=2, prerequisites=[[1,0]] (to take 1, must take 0). Graph: 0 -> 1.
In-degrees: 0:0, 1:1. Queue: [0].
Pop 0. Process 1's in-degree to 0. 1's in-degree is now 0. Add 1 to queue.
Pop 1. Queue empty. All processed. True.prerequisites=[[1,0],[0,1]] (cycle). Graph: 0 <-> 1.
In-degrees: 0:1, 1:1. Queue: []. Nothing to start with. So false.
19. Subsets
Why you might get asked this:
A classic backtracking problem that explores all possible combinations of elements, illustrating recursive decision-making (include/exclude) or bit manipulation.
How to answer:
Use a recursive backtracking function. At each step, you have two choices for the current element: include it in the current subset and recurse, or exclude it and recurse. The base case is when all elements are considered.
Example answer:
Include
1:[1]. Recurse.Exclude
1:[]. Recurse (for2,3).
nums=[1,2,3].
Start [].
a. Include 2: [1,2]. Recurse.
i. Include 3: [1,2,3]. Add to result. Backtrack.
ii. Exclude 3: [1,2]. Add to result. Backtrack.
b. Exclude 2: [1]. Recurse.
i. Include 3: [1,3]. Add to result. Backtrack.
ii. Exclude 3: [1]. Add to result. Backtrack.
(similar process as above). Result: [[],[1],[2],[3],[1,2],[1,3],[2,3],[1,2,3]].
20. Product of Array Except Self
Why you might get asked this:
Tests array manipulation and optimizing space/time complexity by avoiding division and utilizing prefix/suffix products.
How to answer:
Create two arrays: prefixproducts and suffixproducts. prefixproducts[i] stores product of nums[0...i-1]. suffixproducts[i] stores product of nums[i+1...n-1]. answer[i] is prefixproducts[i] * suffixproducts[i]. Can optimize to O(1) extra space.
Example answer:
nums=[1,2,3,4].prefix: [1, 1, 2, 6] (init first to 1, then prev * num).suffix: [24, 12, 4, 1] (init last to 1, then prev * num).
Result: [124, 112, 24, 61] which is [24,12,8,6].
21. Search in Rotated Sorted Array
Why you might get asked this:
A challenging binary search variation that requires adapting the standard algorithm to handle a pivot point where the array is rotated, assessing edge cases and logic.
How to answer:
Use binary search. In each step, determine which half of the array is sorted. If nums[mid] is in the sorted half, check if target is within that sorted range. If not, search the other half.
Example answer:
nums=[4,5,6,7,0,1,2], target 0.
Mid 7. Left half [4,5,6,7] is sorted. 0 is not in it. Search right half [0,1,2].
Mid 1. Left half [0] is sorted. 0 is in it. Search left half.
Mid 0. Found 0 at index 4. Return 4.
22. Find Minimum in Rotated Sorted Array
Why you might get asked this:
Another binary search variant on a rotated array, requiring precise logic to narrow down the search space to the pivot point, which is the minimum element.
How to answer:
Use binary search. The minimum element is the only element smaller than its predecessor. If nums[mid] > nums[right], the minimum is in the right half. Otherwise, it's in the left half or mid itself.
Example answer:
nums=[4,5,6,7,0,1,2].low=0, high=6, mid=3 (7). 7 > 2. Min is in right part ([0,1,2]). low=4.low=4, high=6, mid=5 (1). 1 < 2. Min could be 1 or to its left. high=5.low=4, high=5, mid=4 (0). 0 < 1. Min could be 0. high=4.low=4, high=4. low==high. Return nums[low] (which is 0).
23. Longest Palindromic Substring
Why you might get asked this:
A dynamic programming or "expand around center" problem. Tests substring manipulation and optimization for palindrome detection.
How to answer:
Option 1 (DP): Build a boolean table dp[i][j] indicating if s[i...j] is a palindrome. dp[i][j] is true if s[i] == s[j] and dp[i+1][j-1] is true (or length <= 2).
Option 2 (Expand around center): Iterate through each character as a potential center of a palindrome (both odd and even length). Expand outwards to find the longest palindrome.
Example answer:
String "babad".
Expand around 'a' (index 1): (b)a(b) is bab. Length 3.
Expand around 'a' (index 3): (b)a(d) is a. Length 1.
Expand between 'b' and 'a' (indices 0,1): ba no.
Expand between 'a' and 'b' (indices 1,2): ab no.
Longest found is bab.
24. Maximum Subarray
Why you might get asked this:
A classic dynamic programming problem solved efficiently by Kadane's algorithm, demonstrating optimization for finding a contiguous subarray with the largest sum.
How to answer:
Use Kadane's algorithm. Maintain two variables: currentmax (maximum sum ending at the current position) and globalmax (overall maximum sum found so far). Update currentmax as max(num, currentmax + num). Update globalmax as max(globalmax, current_max).
Example answer:
nums=[-2,1,-3,4,-1,2,1,-5,4].globalmax = -inf, currentmax = 0.num=-2: currmax = max(-2, 0-2) = -2. globmax = max(-inf, -2) = -2.num=1: currmax = max(1, -2+1) = 1. globmax = max(-2, 1) = 1.num=-3: currmax = max(-3, 1-3) = -2. globmax = max(1, -2) = 1.num=4: currmax = max(4, -2+4) = 4. globmax = max(1, 4) = 4.
... eventually, global_max becomes 6 for [4,-1,2,1].
25. Coin Change
Why you might get asked this:
A dynamic programming problem (often unbounded knapsack variant), requiring building up solutions for smaller amounts to solve larger ones, testing memoization or tabulation.
How to answer:
Use dynamic programming. Create a dp array where dp[i] is the minimum number of coins to make amount i. Initialize dp[0]=0 and others to infinity. Iterate through amounts from 1 to amount, and for each amount, iterate through coins, updating dp[i] = min(dp[i], dp[i - coin] + 1).
Example answer:
coins=[1,2,5], amount=11.dp array of size 12, all infinity, dp[0]=0.dp[1] = dp[1-1]+1 = 1.dp[2] = min(dp[2-1]+1, dp[2-2]+1) = min(1+1, 0+1) = 1.dp[3] = min(dp[3-1]+1, dp[3-2]+1) = min(1+1, 1+1) = 2.dp[4] = min(dp[4-1]+1, dp[4-2]+1) = min(2+1, 1+1) = 2.dp[5] = min(dp[5-1]+1, dp[5-2]+1, dp[5-5]+1) = min(2+1, 2+1, 0+1) = 1.
... dp[11] will be 3 (5+5+1).
26. Word Search
Why you might get asked this:
A grid-based DFS/backtracking problem. It assesses your ability to navigate a 2D array, track visited states, and use recursion for exploring paths.
How to answer:
Iterate through the grid. If a cell matches the first character of the word, start a DFS from that cell. The DFS function attempts to find the remaining characters by exploring adjacent cells (up, down, left, right), marking visited cells, and backtracking if a path fails.
Example answer:
board=[["A","B","C","E"],["S","F","C","S"],["A","D","E","E"]], word="ABCCED".
Start at board[0][0]='A'. DFS (0,0) for "ABCCED".
Move to (0,1)='B'. DFS (0,1) for "BCCED".
Move to (0,2)='C'. DFS (0,2) for "CCED".
Move to (1,2)='C'. DFS (1,2) for "CED".
Move to (2,2)='E'. DFS (2,2) for "ED".
Move to (2,3)='D'. DFS (2,3) for "D". Found. Return true.
27. Meeting Rooms / Intervals
Why you might get asked this:
Similar to merge intervals, this type assesses greedy approaches with sorting to determine if a person can attend all meetings or find the minimum number of rooms required.
How to answer:
To check if a person can attend all meetings: Sort intervals by start time. Iterate and check if currentmeeting.start < previousmeeting.end. If so, they overlap, and the person cannot attend all.
Example answer:
intervals = [[0,30],[5,10],[15,20]].
Sort: Already sorted.
Meeting 1: [0,30].
Meeting 2: [5,10]. 5 < 30. Overlap! Return false.
28. Merge K Sorted Lists
Why you might get asked this:
A complex linked list problem often solved efficiently using a min-heap or a divide and conquer strategy, demonstrating your ability to combine multiple sorted sequences.
How to answer:
Option 1 (Min-Heap): Put the head of each list into a min-heap. Repeatedly extract the smallest node from the heap, append it to the result list, and if its list has a next node, add that to the heap.
Option 2 (Divide & Conquer): Recursively merge pairs of lists until only one sorted list remains.
Example answer:
lists = [[1,4,5],[1,3,4],[2,6]].
Min-heap: (1 from list1), (1 from list2), (2 from list3).
Extract 1 (list1). Add 4 (list1) to heap. Result 1. Heap (1 from list2), (2 from list3), (4 from list1).
Extract 1 (list2). Add 3 (list2) to heap. Result 1,1. Heap (2 from list3), (3 from list2), (4 from list1).
Extract 2 (list3). Add 6 (list3) to heap. Result 1,1,2. Heap (3 from list2), (4 from list1), (6 from list3).
Continue until heap empty. Final result 1,1,2,3,4,5,6.
29. Reverse Words in a String
Why you might get asked this:
Tests string manipulation, handling multiple spaces, leading/trailing spaces, and efficiently reversing elements (words) within a string.
How to answer:
Trim leading/trailing spaces. Split the string into words using space as a delimiter. Filter out empty strings if multiple spaces exist. Reverse the order of the words. Join them back with single spaces.
Example answer:
Trim:
"the sky is blue".Split:
["the", "sky", "is", "blue"].Reverse:
["blue", "is", "sky", "the"].Join:
"blue is sky the".
s = " the sky is blue ".
30. Basic Calculator
Why you might get asked this:
A challenging stack-based problem for parsing arithmetic expressions with parentheses and operator precedence. Assesses your ability to manage state and complex parsing rules.
How to answer:
Use a stack to handle parentheses and sign changes. Iterate through the string. If a digit, build the number. If an operator, apply it with the current sign. If (, push current sign and result to stack, reset. If ), pop and apply stored values.
Example answer:
s = "(1+(4+5+2)-3)+(6+8)".
Process 1.(: push current result (0) and sign (1). Reset result=0, sign=1.
Process 4,5,2. result=11.-: sign=-1.3: result=11-3=8.): Pop previous sign (1) and result (0). Current result = 0 + 1 * 8 = 8.(: push current result (8) and sign (1). Reset result=0, sign=1.
Process 6,8. result=14.): Pop previous sign (1) and result (8). Current result = 8 + 1 * 14 = 22.
Final 22.
Other Tips to Prepare for a Google Leetcode Interview
Successful preparation for a Google Leetcode interview extends beyond just solving problems. It requires a holistic approach that builds your technical skills and boosts your confidence. Firstly, consistently practice. Focus on understanding the core data structures and algorithms, rather than just memorizing solutions. As engineering leader John Johnson once said, "The only way to do great work is to love what you do," and for software engineers, that often means loving the challenge of problem-solving. Practice Google-tagged problems on Leetcode, prioritizing arrays, trees, graphs, and dynamic programming, which constitute a significant portion of Google's coding questions.
Secondly, simulate interview conditions. Use tools like Verve AI Interview Copilot (https://vervecopilot.com) to practice explaining your thought process, walking through edge cases, and optimizing solutions verbally. Verve AI Interview Copilot can provide real-time feedback on your communication and solution clarity, essential skills in a Google interview. It’s not just about coding; it's about articulating your approach under pressure. "Luck is what happens when preparation meets opportunity," a quote attributed to Seneca, perfectly captures the essence of interview readiness.
Thirdly, refine your debugging and testing skills. After coding a solution, mentally walk through it with various test cases, including edge cases (empty inputs, single elements, max/min values). The Verve AI Interview Copilot can also help you identify common pitfalls in your logic or approach. Finally, ask clarifying questions during your practice. This habit will serve you well in actual interviews, demonstrating your thoughtful engagement with the problem. Leverage resources like Verve AI Interview Copilot to gain comprehensive insights into your performance and improve iteratively.
Frequently Asked Questions
Q1: How difficult are Google Leetcode questions?
A1: Most are medium to hard difficulty, often increasing in complexity through interview stages, requiring strong command of data structures and algorithms.
Q2: What topics are most common for Google coding interviews?
A2: Arrays & Strings, Graphs & Trees, Dynamic Programming, and Recursion & Backtracking are frequently tested.
Q3: Is it better to solve iteratively or recursively?
A3: Often depends on the problem; understand both. For trees, recursion is natural, while iterative might be preferred for memory/stack limits.
Q4: Should I memorize solutions?
A4: No, focus on understanding the underlying patterns and algorithms. Interviewers want to see your problem-solving process, not just memorized code.
Q5: How important is communication during the coding interview?
A5: Extremely important. Articulating your thought process, discussing trade-offs, and explaining your solution is as crucial as writing correct code.
Q6: How long does a typical Google coding interview last?
A6: Usually around 45-60 minutes, including problem understanding, coding, testing, and Q&A.
