Concept Interview Questions

Jake M. - "Random Forest is a machine learning model used for classification problems or regression problems. It can handle binary classification as well as multi-class classification. It is a very efficient model and is great for a baseline or used in a service that needs extremely low latency depending on the size of the model. It's also a good option for wide datasets (dataset with many features) due to it's random subset of features. it is slightly less optimized for deep datasets on very large dataset"See full answer

Bhavya V. - "Overfitting is the condition where your model is giving an unexpectedly higher accuracy because of its training in a small database and not getting exposed to anu different type of database while testing"See full answer

Sue G. - "Race Condition i,e multiple threads modifying simultaneously can lead to data inconsistency Operations like putIfAbsent() or computeIfAbsent() are not atomoic i.e duplicate entries or missing updates when multiple threads perform operations Data Corruption : during resizing of a hashmap by a thread, if another thread is accessing the same data , buckets can get corrupted, leading to a loss of data"See full answer

Archna M. - "Caching is a strategy to have the frequently accessed data in the memory to reduce the latency. whenever a client request the data from server, it is first accessed into cache and if not available then it is getting fetched from the storage and stored into cache. As cache is having limited memory, so amount of data can be stored in cache is less. Data will be flushed out from cache based on a criteria which is termed as caching strategy. There could be different mechanisms under which a data can"See full answer