Module Lead Software Engineer
Module Lead Software Engineer Interview Questions and Answers

Asked in Impetus Technologies

Q. Mutithreading nd hhow to use future completable future
Multithreading allows concurrent execution of multiple threads. Future and CompletableFuture are used for asynchronous programming.
Multithreading enables parallel processing of tasks
Future is used to represent a result that may not be available yet
CompletableFuture is a subclass of Future that provides more flexibility and functionality
CompletableFuture can be used to chain multiple asynchronous tasks
CompletableFuture can also be used to handle exceptions and timeouts

Asked in Impetus Technologies

Q. Write a Spark program to count the occurrences of each word in a dataframe and sort the words in descending order based on their occurrence count.
Spark wordcount and sort in descending order with their number occurrence in dataframe
Read text file into dataframe using spark.read.text()
Split each line into words using split() function
Use groupBy() and count() functions to count the occurrence of each word
Sort the resulting dataframe in descending order using orderBy() function
Show the resulting dataframe using show() function

Asked in Impetus Technologies

Q. How do you find the intersection point of two arrays?
Find the intersection point of two arrays of strings.
Convert arrays to sets to find common elements
Iterate through one array and check if element exists in the other array
Use built-in intersection method in Python
Sort arrays and use two pointers to find common elements

Asked in Impetus Technologies

Q. How do you implement a stack using two queues?
To create a stack from two queues, we need to use one queue for storing elements and the other for temporary storage.
Push elements into the first queue
When popping, move all elements except the last one to the second queue
Pop the last element from the first queue and return it
Swap the names of the two queues to make the second queue the first one

Asked in Impetus Technologies

Q. How do you optimize Spark performance?
Performance optimization of Spark involves tuning various parameters and optimizing code.
Tune memory allocation and garbage collection settings
Optimize data serialization and compression
Use efficient data structures and algorithms
Partition data appropriately
Use caching and persistence wisely
Avoid shuffling data unnecessarily
Monitor and analyze performance using Spark UI and other tools
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