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I applied via Company Website and was interviewed in Jul 2024. There were 2 interview rounds.
Javascript logical questions
I applied via Campus Placement and was interviewed before Feb 2021. There were 3 interview rounds.
Aptitude round consists Logical reasoning, General Aptitude, Grammar related questions etc. All are moderate level questions.
OOPs is a programming paradigm that uses objects to represent real-world entities. Java is an OOPs language.
OOPs stands for Object-Oriented Programming System
Java is a class-based OOPs language
Encapsulation, Inheritance, Polymorphism, and Abstraction are the four pillars of OOPs
Objects have state and behavior
Java supports interfaces, which allow for multiple inheritance
Example: A car can be represented as an object wit...
Java solves machine dependency by using bytecode and virtual machine.
Java code is compiled into bytecode which is platform-independent
The bytecode is executed by the Java Virtual Machine (JVM) which is platform-specific
JVM translates bytecode into machine code for the specific platform
This allows Java code to run on any platform with a JVM installed
Example: A Java program compiled on Windows can run on Linux or Mac as
I applied via Campus Placement and was interviewed before May 2021. There were 2 interview rounds.
Numerical ability and logical reasoning followed by some coding mcqs
Printing 1 to 100 without for loop
Use recursion to print numbers from 1 to 99
Print 100 outside the recursion
Use a base case to stop recursion at 100
I applied via Campus Placement and was interviewed before Jun 2021. There were 2 interview rounds.
Simple aptitude test
I appeared for an interview before Sep 2020.
Round duration - 60 minutes
Round difficulty - Easy
Round duration - 50 minutes
Round difficulty - Easy
Round duration - 60 minutes
Round difficulty - Easy
At the beginning of this round, the interviewer asked me about the data structures I knew. Linked lists, trees, graphs, arrays etc. was my answer. He asked me how well I knew Dynamic Programming. I said I wasn’t strong in that and he said that he would ask me a question on dynamic programming for sure.
Round duration - 40 minutes
Round difficulty - Easy
The interviewer asked me if I was comfortable with the interview process so far and how the previous interviews were. I said it was good and he gave me the first problem to solve.
Round duration - 60 minutes
Round difficulty - Easy
The interviewer asked me some Computer Science fundamentals in this round as well as some behavioural questions.
Implement a Trie data structure with insert and search functions.
Create a TrieNode class with children and isEndOfWord attributes.
Implement insert function to add words by iterating through characters.
Implement search function to check if a word exists by traversing the Trie.
Example: Insert 'apple', 'banana', 'orange' and search for 'apple' and 'grape'.
Do lot of hard work and practice of Data Structures and Algorithms based questions. I personally recommend you Coding Ninjas and Geeks For Geeks for interview preparation.
Application resume tips for other job seekersMake your resume short and try to make it of one page only and do mention all your skills which you are confident of in your resume.
Final outcome of the interviewSelectedI applied via Campus Placement and was interviewed before Aug 2021. There were 2 interview rounds.
It was a basic aptitude test.
I applied via Newspaper Ad and was interviewed before Jun 2021. There were 3 interview rounds.
I applied via Naukri.com and was interviewed before Jun 2021. There were 2 interview rounds.
English is the main focussed part
I applied via Company Website and was interviewed before Apr 2021. There were 2 interview rounds.
It's totally based on Basic understanding of concepts such as Linear arrangement, circular arrangement، blood relation, direction sense, ratio proportion, time, speed, distance
I appeared for an interview in Aug 2017.
Merge Sort is a divide and conquer algorithm that sorts an array by dividing it into two halves, sorting them separately, and then merging the sorted halves.
Divide the array into two halves
Recursively sort the two halves
Merge the sorted halves
Find pairs of integers in a BST whose sum is equal to a given number.
Traverse the BST and store the values in a hash set.
For each node, check if (X - node.value) exists in the hash set.
If yes, add the pair (node.value, X - node.value) to the result.
Continue traversal until all nodes are processed.
Merge overlapping time intervals into mutually exclusive intervals.
Sort the intervals based on their start time.
Iterate through the intervals and merge overlapping intervals.
Output the mutually exclusive intervals.
Example: [(1,3), (2,6), (8,10), (15,18)] -> [(1,6), (8,10), (15,18)]
Different types of hashing and alternative for Linear Chaining
Different types of hashing include division, multiplication, and universal hashing
Alternative for Linear Chaining is Open Addressing
Open Addressing includes Linear Probing, Quadratic Probing, and Double Hashing
An AVL tree is a self-balancing binary search tree where the heights of the left and right subtrees differ by at most one.
AVL tree is a binary search tree with additional balance factor for each node.
The balance factor is the difference between the heights of the left and right subtrees.
Insertion and deletion operations in AVL tree maintain the balance factor to ensure the tree remains balanced.
Rotations are performed ...
Find the minimum number of squares whose sum equals to a given number n.
Use dynamic programming to solve the problem efficiently.
Start with finding the square root of n and check if it is a perfect square.
If not, then try to find the minimum number of squares required for the remaining number.
Repeat the process until the remaining number becomes 0.
Return the minimum number of squares required for the given number n.
Insertion sort for a singly linked list.
Traverse the list and compare each node with the previous nodes
If the current node is smaller, swap it with the previous node
Repeat until the end of the list is reached
Time complexity is O(n^2)
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