Data Engineering Associate with Databricks Practice Exam

What is the Auto Loader used for in Databricks?

1. Batch loading of historical data

2. Incremental and continuous data loading

3. Static file loading only

4. Schema validation of data

The correct answer is: Incremental and continuous data loading

The Auto Loader in Databricks is specifically designed for incremental and continuous data loading from cloud storage into a Delta Lake table. It allows users to efficiently ingest streaming data as well as batch data by automatically detecting new files and automatically handling any subsequent data changes. Auto Loader simplifies the process of loading data into the data pipeline, making it possible to easily process and analyze real-time data streams without manual intervention. Moreover, it is optimized for performance with scalable ingestion, handling various file formats such as JSON and CSV. This means users can focus more on the analysis and less on the complexities of the loading process. Auto Loader also supports schema inference and evolution, allowing for flexibility in handling changes in the data structure over time, which is particularly valuable for maintaining the integrity and usability of evolving data sets. The other choices, such as batch loading of historical data, static file loading, and schema validation, either describe functionalities that are not primarily associated with Auto Loader or are features found in other components of the Databricks ecosystem.