Databricks Certified Data Engineer - Associate
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Which type of workloads are compatible with Auto Loader?
Real Exam
Community
KKeng
0
A
Streaming workloads
B
Machine learning workloads
C
Serverless workloads
D
Batch workloads
Explanation:
Auto Loader is designed to support both streaming and batch workloads for incremental data ingestion. It can continuously ingest new files as they arrive (streaming) and also process existing files in batches. This dual capability makes Auto Loader versatile for various data ingestion scenarios.
Key Points:
- Streaming workloads (A): Auto Loader can continuously monitor cloud storage locations for new files and process them as they arrive, making it suitable for streaming use cases.
- Batch workloads (D): Auto Loader can also process existing files in batch mode, making it compatible with traditional batch processing workflows.
- Machine learning workloads (B): While Auto Loader can ingest data that might be used for ML, it's not specifically designed for ML workloads.
- Serverless workloads (C): Auto Loader can run on serverless compute, but this refers to the compute infrastructure rather than the workload type.
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