Databricks Certified Data Engineer - Associate
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A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE. Three datasets are defined against Delta Lake table sources using LIVE TABLE. The table is configured to run in Development mode using the Continuous Pipeline Mode. Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?
Real Exam
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KKeng
A
All datasets will be updated once and the pipeline will shut down
B
The compute resources will be terminated.
C
All datasets will be updated at set intervals until the pipeline is shut down
D
The compute resources will persist until the pipeline is shut down.
E
All datasets will be updated once and the pipeline will persist without any processing
F
The compute resources will persist but go unused.
Explanation:
In Delta Live Tables Development mode with Continuous Pipeline Mode:
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Development Mode Behavior: When running in development mode, Delta Live Tables reuses clusters to avoid restart overhead and disables pipeline retries for immediate error detection.
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Continuous Pipeline Mode: This mode is designed for streaming workloads where data is continuously processed. However, in development mode, the behavior differs from production mode.
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Key Difference: In development mode with continuous pipeline mode, the pipeline will:
- Process all available data once
- Then persist without further processing (unlike production mode which would continue processing)
- The compute resources remain available for additional testing and development
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Why not other options:
- A/G: The pipeline doesn't shut down completely - it persists for testing
- B: Compute resources are not terminated in development mode
- C/I: In development mode, datasets are not updated at set intervals continuously
- D: While compute persists, the pipeline doesn't shut down
- F/H/J: These don't accurately describe the complete behavior
This behavior allows developers to test their pipelines without incurring continuous compute costs while maintaining resources for iterative development.
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