Spark Developer Interview Questions and Answers

Design architecture for end user clicks data to warehouse and datalake to ML model

• Create a pipeline to extract data from end user clicks

• Store data in both warehouse and datalake for redundancy and scalability

• Use ETL tools to transform and clean data for ML model

• Train ML model on transformed data

• Deploy ML model for predictions on new data