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I applied via LinkedIn and was interviewed in May 2024. There was 1 interview round.
Got a leetcode easy question
I appeared for an interview before Dec 2020.
Round duration - 90 minutes
Round difficulty - Hard
This was an online coding round where we were supposed to solve 2 questions under 90 minutes . Both the questions in my set were related to Graphs and were quite tricky and heavy to implement.
Given a directed graph with a specified number of vertices V
and edges E
, your task is to calculate the total number of distinct paths from a given source node S
to all ot...
Calculate the total number of distinct paths from a given source node to all other nodes in a directed graph.
Use dynamic programming to keep track of the number of paths from the source node to each node in the graph.
Consider using modular arithmetic to handle large numbers and prevent overflow.
Start by initializing the number of paths from the source node to itself as 1.
Iterate through the edges of the graph and updat...
You are provided with a number of courses 'N', some of which have prerequisites. There is a matrix named 'PREREQUISITES' of size 'M' x 2. This matrix indicates that fo...
Given courses with prerequisites, determine a valid order to complete all courses.
Use topological sorting to find a valid order of courses.
Create a graph with courses as nodes and prerequisites as edges.
Start with courses that have no prerequisites and remove them from the graph.
Continue this process until all courses are taken or there are no valid courses left.
If there is a cycle in the graph, it is impossible to com
Round duration - 60 Minutes
Round difficulty - Medium
This was a Data Structures and Algorithms round with some standard questions . I was expected to come up with an
efficient approach and code it as well .
You are provided with 'N' intervals, each containing two integers denoting the start time and end time of the interval.
Your task is to merge all overlapping intervals a...
Merge overlapping intervals and return sorted list of merged intervals.
Sort the intervals based on start times.
Iterate through intervals and merge overlapping intervals.
Return the merged intervals in sorted order.
Given a 2-dimensional binary matrix called Mat
of size N x M that consists solely of 0s and 1s, find the length of the longest path from a specified source cell to a destina...
Find the length of the longest path from a source cell to a destination cell in a binary matrix.
Use depth-first search (DFS) to explore all possible paths from source to destination.
Keep track of visited cells to avoid revisiting them.
Return the length of the longest path found, or -1 if no path exists.
Round duration - 50 Minutes
Round difficulty - Medium
This was also a DSA round where I was asked to code only one of the questions but I eventually ended up coding both
as I had some spare time and explained my approches very smoothly to the interviewer . This round went preety well .
Given an array of integers with 'N' elements, determine the length of the longest subsequence where each element is greater than the previous element. This...
Find the length of the longest strictly increasing subsequence in an array of integers.
Use dynamic programming to solve this problem efficiently.
Initialize an array to store the length of the longest increasing subsequence ending at each index.
Iterate through the array and update the length of the longest increasing subsequence for each element.
Return the maximum value in the array as the result.
Given a rotated sorted array ARR
of size 'N' and an integer 'K', determine the index at which 'K' is present in the array.
1. If 'K' is not present...
Given a rotated sorted array, find the index of a given integer 'K'.
Use binary search to find the pivot point where the array is rotated.
Then perform binary search on the appropriate half of the array to find 'K'.
Handle cases where 'K' is not present in the array by returning -1.
Round duration - 50 Minutes
Round difficulty - Medium
This was also a DSA round with 2 questions of Medium to Hard difficulty . I was expected to follow some clean code and OOPS principles to write the code in this round .
Given an array of integers ARR
and an integer K
, determine the rank of the element ARR[K]
.
The rank of any element in ARR
is defined as the number of elem...
Given an array and an index, find the number of elements smaller than the element at that index appearing before it in the array.
Iterate through the array up to index K and count the number of elements smaller than ARR[K].
Return the count as the rank of ARR[K].
Handle edge cases like empty array or invalid index K.
Design a data structure for a Least Recently Used (LRU) cache that supports the following operations:
1. get(key)
- Return the value of the key if it exists in the cache; otherw...
Design a Least Recently Used (LRU) cache data structure that supports get and put operations with capacity constraint.
Implement a doubly linked list to maintain the order of recently used keys.
Use a hashmap to store key-value pairs for quick access.
Update the order of keys in the linked list on get and put operations.
Evict the least recently used key when the cache reaches its capacity.
Tip 1 : Must do Previously asked Interview as well as Online Test Questions.
Tip 2 : Go through all the previous interview experiences from Codestudio and Leetcode.
Tip 3 : Do at-least 2 good projects and you must know every bit of them.
Tip 1 : Have at-least 2 good projects explained in short with all important points covered.
Tip 2 : Every skill must be mentioned.
Tip 3 : Focus on skills, projects and experiences more.
I appeared for an interview before Sep 2016.
Search an element in sorted rotated array.
Use binary search to find the pivot point where the array is rotated.
Divide the array into two subarrays and perform binary search on the appropriate subarray.
Handle the cases where the target element is at the pivot point or not present in the array.
Find the Kth largest element in an array.
Sort the array in descending order and return the element at index K-1.
Use a max heap to find the Kth largest element efficiently.
Implement a quickselect algorithm to find the Kth largest element in linear time.
Reverse a linked list in groups of n elements.
Divide the linked list into groups of n elements.
Reverse each group individually.
Connect the reversed groups to form the final linked list.
Handle cases where the number of elements is not a multiple of n.
Example: 1->2->3->4->5->6->7->8, n=3 -> 3->2->1->6->5->4->8->7
Find the median of two sorted arrays.
Merge the two arrays into one sorted array and find the median.
Use binary search to find the median in O(log(min(m, n))) time complexity.
Handle edge cases like empty arrays or arrays of different lengths.
Count number of paths from (0, 0) to (m, n) in a matrix while crossing through some indexes were not allowed.
Use dynamic programming to solve the problem
Create a 2D array to store the number of paths
Traverse the matrix and update the array based on the allowed paths
Return the value at the last index of the array
I appeared for an interview in Aug 2017.
Print the level order traversal of binary tree in spiral form
Perform level order traversal of the binary tree
Alternate the direction of traversal for each level
Use a stack to reverse the order of nodes in each level
Print the nodes in the order of traversal
Find the maximum element in each subarray of size k in a given array.
Iterate through the array from index 0 to n-k.
For each subarray of size k, find the maximum element.
Store the maximum elements in a separate array.
Return the array of maximum elements.
To find the Kth largest element in two sorted arrays, we can use the merge step of merge sort algorithm.
Merge the two arrays into a single sorted array using a modified merge sort algorithm.
Return the Kth element from the merged array.
Merge two sorted arrays into one sorted array with expected time complexity of (m+n).
Use a two-pointer approach to compare elements from both arrays and merge them into the first array.
Start comparing elements from the end of both arrays and place the larger element at the end of the first array.
Continue this process until all elements from the second array are merged into the first array.
The algorithm finds the position of the 3rd occurrence of 'B' in an n-ary tree from a given index in constant time complexity.
Traverse the n-ary tree using a depth-first search (DFS) algorithm
Keep track of the count of 'B' occurrences
When the count reaches 3, return the current position
If the end of the tree is reached before the 3rd 'B', return -1
Check if a given string is a composite of two words from a limited dictionary.
Create a hash set of all the words in the dictionary.
Iterate through all possible pairs of substrings in the given string.
Check if both substrings are present in the hash set.
If yes, return true. Else, return false.
Switch adjacent nodes in a single linked list.
Traverse the linked list and swap adjacent nodes.
Keep track of previous node to update its next pointer.
Handle edge cases for first two nodes and last node.
Example: 1->2->3->4 becomes 2->1->4->3.
Traverse only the left sub-tree of a binary search tree.
Start at the root node
If the left child exists, visit it and repeat the process
If the left child does not exist, return to the parent node
Continue until all nodes in the left sub-tree have been visited
Design an efficient data structure for two lifts in a building of n floors.
Use a priority queue to keep track of the floors each lift is heading to
Implement a scheduling algorithm to determine which lift to assign to a new request
Consider adding a weight limit to each lift to prevent overloading
Use a hash table to keep track of the current location of each lift
To find the maximum number that can be formed from the digits of an integer.
Convert the integer to a string
Sort the characters in descending order
Join the sorted characters to form the maximum number
Reverse all the words in a given string
Split the string into an array of words
Loop through the array and reverse each word
Join the reversed words back into a string
Explaining how to handle 'n' in a string during swapping process
Identify the positions of 'n' in the string
Exclude those positions from the swapping process
Use a temporary variable to swap the characters
Ensure the swapped characters are not 'n'
Return the modified string
We can use any sorting algorithm like quicksort, mergesort, heapsort, etc.
Choose the appropriate sorting algorithm based on the size of the file and the range of numbers
Implement the chosen algorithm in the programming language of choice
Read the numbers from the file into an array or list
Apply the sorting algorithm to the array or list
Write the sorted numbers back to the file
Word suggestions in Eclipse can be implemented using algorithms like Trie or N-gram models.
Use Trie data structure to store the dictionary of words
Implement auto-complete feature using Trie
Use N-gram models to suggest words based on context
Train the N-gram model on a large corpus of text data
Combine both approaches for better accuracy
Consider user's typing speed and frequency of words for better suggestions
To check if a number k lies in a sequence formed by adding previous 2 elements, start with a=0 and b=1 and iterate until k is found or exceeded.
Start with a=0 and b=1
Iterate through the sequence until k is found or exceeded
If k is found, return true. If exceeded, return false
Check if a Binary Tree is a Binary Search Tree (BST)
A BST has the property that all nodes in the left subtree of a node have values less than the node's value, and all nodes in the right subtree have values greater than the node's value
We can traverse the tree in-order and check if the resulting sequence is sorted
Alternatively, we can recursively check if each node satisfies the BST property
Keep track of kth largest number in a stream of numbers.
Use a min-heap of size k to keep track of kth largest number.
For each incoming number, compare it with the root of the heap.
If it is larger than the root, replace the root with the new number and heapify.
The root of the heap will always be the kth largest number.
Infix expression can be evaluated using the concept of operator precedence and associativity.
Convert the infix expression to postfix expression using stack data structure
Evaluate the postfix expression using stack data structure
Use operator precedence and associativity rules to determine the order of evaluation
Parentheses can be used to override the default order of evaluation
I applied via Referral
Design optimal data structures for LRU cache
Use a doubly linked list to keep track of recently used items
Use a hash table to store key-value pairs for quick access
When an item is accessed, move it to the front of the linked list
When the cache is full, remove the least recently used item from the back of the linked list and hash table
Convert a sorted array to balanced binary search tree
Find the middle element of the array and make it the root of the tree
Recursively construct the left subtree using the left half of the array
Recursively construct the right subtree using the right half of the array
Repeat until all elements are added to the tree
Reverse a singly linked list in groups of k inplace
Divide the linked list into groups of k nodes
Reverse each group of k nodes
Connect the reversed groups to form the final linked list
Optimal data structure for storing words and their meanings
Use a hash table with the word as the key and a list of meanings as the value
Each meaning can be stored as a string or an object with additional information
Consider using a trie data structure for efficient prefix search
Implement a search function that can handle partial matches and synonyms
A recursive routine to calculate a ^ n
The base case is when n is 0, in which case the result is 1
For any other value of n, the result is a multiplied by the result of a^(n-1)
The recursive function should call itself with a^(n-1) as the new input
Design optimal data structure for never-ending stream of numbers for insertion, deletion, searching, kth largest and kth smallest.
Use a balanced binary search tree like AVL or Red-Black tree for efficient insertion, deletion, and searching.
Maintain two heaps, one for kth largest and one for kth smallest.
For finding kth largest, use a min heap of size k and for kth smallest, use a max heap of size k.
Alternatively, use a...
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