Data Engineering Associate with Databricks Practice Exam

When is Auto Loader typically used in data processing?

1. For bulk ingestion

2. For incremental ingestion

3. For data archiving

4. For real-time analytics

The correct answer is: For incremental ingestion

Auto Loader is specifically designed for incremental data ingestion. It is an optimization feature within Databricks that allows for the efficient and automatic loading of new data files as they arrive in cloud storage. This is highly beneficial for streaming datasets or for situations where data is continuously generated and stored. When using Auto Loader, users can set up continuous ingestion from files that are added to a specified directory, meaning that only new data since the last processing round is loaded, rather than re-processing existing data. This approach saves time and resources, as it focuses only on the incoming data changes, making it very suitable for operational workflows that require timely updates without the overhead of bulk loading entire datasets repeatedly. In contrast, bulk ingestion refers to loading large volumes of data all at once, which does not align with the incremental approach that Auto Loader takes. Data archiving involves the process of transferring data to storage for long-term retention and is not related to ingestion at all. Real-time analytics entails analyzing data as it comes in, which may utilize Auto Loader in practice but is not the primary function attributed to it. Hence, the primary use case of Auto Loader is incremental ingestion.