Lenskart Application Developer Interview Questions and Answers

2 coding question and few multiple choice

Q1. Few coding questions and sql and oops questions
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Q1. Question of PG and NS number
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A total of 2 question where asked -
The 1st question was a easy one involving Arrays and 2-Pointers
The 2nd question was medium-hard problem involving Matrix, Recursion and Back-Tracking.

Q1. Questions where about previous projects and experiences. It was more of a behavioural round than a technical round.
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Q2. How did you handle the errors?
Ans. 
I handle errors by identifying the root cause, troubleshooting, and implementing a solution to prevent future occurrences.
• Identify the root cause of the error

• Troubleshoot the issue thoroughly

• Implement a solution to prevent future occurrences

• Document the error and solution for reference
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Q1. Question were abouts studies, job experiences and willingness to join the organization.
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I don't want to reveal them

Q1. Two coding questions we have shared our screen and write
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Q2. Question based on linked list
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Coding test was easy mainly based on array and string both the questions were doable. If you have practiced in past you d good to go.

Q1. Anagram string question
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Q2. Move all 0 s to end
Ans. 
Move all 0s to the end of an array of strings
• Iterate through the array and move all 0s to the end

• Use two pointers to keep track of the current position and the position to swap with

• Example: Input: ['apple', '0', 'banana', '0', 'cherry'], Output: ['apple', 'banana', 'cherry', '0', '0']
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Q1. Yet to be completed
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It consists of Code based on DSA-concepts

Q1. It was a problem-solving round using DSA
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Q1. Technical and Behavioral questions
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Q1. 2 questions both are from array and string
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1hr test 2 DSA problems

Q1. 2DSA problems 1 from DP and 1 from heaps
Ans. 
Dynamic programming and heap problems are common in technical interviews for Applications Engineer roles.
• Dynamic programming (DP) involves breaking down a problem into smaller subproblems and solving each subproblem only once. Example: finding the longest increasing subsequence in an array.

• Heaps are data structures that satisfy the heap property. Example: finding the kth largest element in an array using a heap.
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