Data Engineering Associate with Databricks Practice Exam

How is end-to-end fault tolerance achieved in structured streaming?

1. Through data replication

2. Using checkpointing and write ahead logs

3. By maintaining complete datasets

4. Through constant monitoring of data sources

The correct answer is: Using checkpointing and write ahead logs

End-to-end fault tolerance in structured streaming is achieved using checkpointing and write-ahead logs because these mechanisms ensure that the system can recover from failures and continue processing data without loss. Checkpointing involves saving the intermediate state of a structured streaming application at defined intervals, which allows the application to restart from the last successful checkpoint if a failure occurs. This minimizes data loss and ensures that the job can resume processing from a precise state, maintaining the integrity of the data flow. Write-ahead logs serve a critical role in this process by recording incoming data before it is processed. If the application encounters a failure before it has a chance to process the data, the logs ensure that the data can be replayed when the application is restarted. This guarantees that no data is lost during the streaming process and that all messages are accounted for. Both mechanisms work in conjunction to provide robust fault tolerance, enabling the system to handle unexpected interruptions without the risk of data inconsistency or loss. This allows structured streaming to be resilient and reliable, making it suitable for real-time data processing tasks. While data replication, maintaining complete datasets, and constant monitoring of data sources are important components of data engineering, they do not specifically address end-to-end fault tolerance in the same manner as