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I applied via Recruitment Consultant and was interviewed in Mar 2021. There were 3 interview rounds.
Finding number with most frequency in 1M size array of strings.
Use a hash table to store the frequency of each number.
Iterate through the array and update the frequency in the hash table.
Find the number with the highest frequency in the hash table.
To find length of linked list in single iteration, use two pointers and traverse the list until the end.
Use two pointers, one moving one node at a time and the other moving two nodes at a time.
Traverse the list until the end is reached.
The number of iterations taken by the faster pointer to reach the end is the length of the linked list.
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I applied via Approached by Company and was interviewed in Aug 2017. There were 5 interview rounds.
posted on 26 May 2021
I applied via Recruitment Consultant and was interviewed in Nov 2020. There were 3 interview rounds.
posted on 11 Oct 2024
I applied via LinkedIn and was interviewed before Oct 2023. There was 1 interview round.
I applied via Company Website and was interviewed in Jan 2023. There were 5 interview rounds.
I applied via LinkedIn and was interviewed before Jan 2022. There were 4 interview rounds.
Microservices are modular and scalable, while monolithic is simpler but less flexible.
Microservices allow for independent deployment and scaling of individual components
Monolithic architecture is easier to develop and test, but can become unwieldy as the application grows
Microservices can be more fault-tolerant and resilient to failures
Monolithic architecture can be more efficient for small applications with limited fu...
Microservices are small, independent services that communicate with each other, while monolithic is a single, self-contained application.
Microservices are loosely coupled and can be developed and deployed independently.
Monolithic applications are tightly coupled and require a full deployment for any changes.
Microservices allow for better scalability and fault tolerance.
Monolithic applications can be simpler to develop ...
Zero one knapsack problem is a classic optimization problem in computer science.
It involves selecting a subset of items with maximum value, subject to a weight constraint.
It is a NP-hard problem and can be solved using dynamic programming.
Examples include packing a backpack with limited weight capacity or selecting stocks for a portfolio with limited investment budget.
Indexing a dictionary for faster search
Use hash tables for constant time lookup
Consider using a trie for prefix search
Sort the keys for binary search
Use a balanced tree for range queries
I applied via Naukri.com and was interviewed in Nov 2021. There were 4 interview rounds.
I applied via Naukri.com and was interviewed before Apr 2023. There were 3 interview rounds.
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