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Java + Angular Questions were asked by 2 interviewers
I applied via Naukri.com and was interviewed in May 2024. There were 2 interview rounds.
Basic aptitude questions
Fibonacci series , 1/2 hr all test
I was interviewed before Dec 2023.
I applied via Referral and was interviewed in Dec 2023. There were 3 interview rounds.
Kadane's Algorithm is used to find the maximum subarray sum in an array.
Iterate through the array and keep track of the maximum sum ending at each index.
If the sum at any index becomes negative, reset the sum to 0.
Return the maximum sum found.
Example: For array [-2, 1, -3, 4, -1, 2, 1, -5, 4], the maximum subarray sum is 6 (from index 3 to 6).
Reverse a linked list by changing the pointers direction
Iterate through the linked list and change the direction of pointers
Use three pointers - current, previous, and next to reverse the list
Update the head of the list to the last node after reversing
HashedIn by Deloitte interview questions for popular designations
High Level Design (HLD) of a lift includes system architecture, components, interfaces, and interactions.
Identify key components like motor, control panel, doors, and sensors
Define system architecture including how components interact
Specify interfaces for user input, floor selection, and emergency stop
Consider safety features like overload protection and emergency brakes
Get interview-ready with Top HashedIn by Deloitte Interview Questions
I applied via Referral and was interviewed in Mar 2024. There was 1 interview round.
I was interviewed in Jun 2024.
Medium level of codings.
Basic DS Algo, leetcode
Two coding problem was given
Spark Memory management involves configuring memory allocation, monitoring memory usage, and optimizing performance.
Set memory allocation parameters in Spark configuration (e.g. spark.executor.memory, spark.driver.memory)
Monitor memory usage using Spark UI or monitoring tools like Ganglia
Optimize performance by tuning memory allocation based on workload and cluster resources
Use techniques like caching and persistence t
Spark optimization involves tuning configurations, partitioning data, using appropriate transformations, and caching intermediate results.
Tune Spark configurations based on cluster resources and workload requirements
Partition data to distribute workload evenly across nodes
Use appropriate transformations like map, filter, and reduce to minimize data shuffling
Cache intermediate results to avoid recomputation
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