Gwynnie Bee Software Developer Interview Questions and Answers

There were three coding questions.

Q1. Could you provide a thorough explanation of the project?
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Q2. OOps Concepts and question from the technical subjects
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Q3. Why Spring Boot
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Q4. JWT Authentication
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Q5. Docker and Kubernetes
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OA test 3 Dsa questions 2 medium 1 hard you have to solve 1.5 questions in 120 minutes

Q1. DSA Round Astreoid collision leetcode
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Q2. Dp question based on exclusion and inclusion
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DSA 2 question
1 -> Find Lca and traverse a tree path available on GFG
2 -> LinkedList pallindrome check (you have to solve that in 0(1) space complexity)

Q1. LRU Cache - how to tackle
Ans. 
LRU Cache is a data structure that maintains a list of items in order of most recently used to least recently used.
• Implement using a doubly linked list and a hashmap for efficient operations

• When an item is accessed, move it to the front of the list

• When the cache is full, remove the least recently used item from the end of the list
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Q2. LRU Cache implementation based on a real time system. How would the main work which will be called etc etc
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Q1. It was lld question to design a cab booking system.
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Q2. Tell me about yourself
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2 coding questions and a few MCQs

Q1. Asked to explain and optimise my solution from coding test
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Q1. Behavioral round. Asked about resume.
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Q1. General HR round and trying to find if you're a right fit for the company
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DSA + JAVA + Spring boot

LLD (bookmyshow) + DSA

What is your name write it in binary format using Ensemble language and how to optimize super computer

Q1. Sort an array in O(nlogn)
Ans. 
Use merge sort algorithm to sort the array in O(nlogn) time complexity.
• Implement merge sort algorithm which divides the array into two halves, recursively sorts the halves, and then merges the sorted halves.

• Time complexity of merge sort is O(nlogn) where n is the number of elements in the array.

• Example: ['apple', 'banana', 'cherry', 'date'] can be sorted using merge sort in O(nlogn) time complexity.
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Q2. Find element in sorted Array
Ans. 
Use binary search to find element in sorted array efficiently.
• Start with middle element and compare with target

• If target is less, search left half, if greater search right half

• Repeat until element is found or search space is empty
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