Answer: Streaming workloads, Batch workloads

## Explanation Auto Loader is a Databricks feature designed for incremental data ingestion. It's compatible with both: **A. Streaming workloads** - Auto Loader can be used in streaming mode to continuously ingest new files as they arrive in cloud storage. **D. Batch workloads** - Auto Loader can also be used in batch mode to process files in discrete batches. ### Why these options are correct: 1. **Streaming workloads**: Auto Loader provides a scalable way to incrementally process new files from cloud storage using Structured Streaming. It can detect and process new files as they arrive. 2. **Batch workloads**: Auto Loader can be used in batch mode where you trigger it to process files that have arrived since the last run. ### Why other options are incorrect: - **B. Machine learning workloads**: While Auto Loader can ingest data that might be used for ML, it's not specifically designed for ML workloads. ML workloads typically use the data after it's been ingested. - **C. Serverless workloads**: Auto Loader itself is not a serverless workload type. It can run on serverless compute, but that's a deployment option, not a workload type that Auto Loader is specifically designed for. ### Key Points: - Auto Loader is optimized for incremental file processing from cloud storage - It supports both streaming and batch processing modes - It's particularly useful for ETL pipelines that need to process new files as they arrive - It tracks processed files to avoid reprocessing, making it efficient for incremental data loading

Author: Keng Suppaseth