Databricks Certified Data Engineer - Associate

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