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DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE · Question #37

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…

The correct answer is E. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources. In Delta Live Tables, the Pipeline Mode determines how the cluster behaves after processing. Continuous Pipeline Mode keeps the pipeline perpetually running, processing new data as it arrives rather than shutting down after a single pass. In this mode, streaming tables…

Submitted by paula_co· Apr 18, 2026ELT with Spark SQL and Python

Question

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?

Options

  • AAll datasets will be updated once and the pipeline will shut down. The compute resources will be
  • BAll datasets will be updated at set intervals until the pipeline is shut down. The compute resources
  • CAll datasets will be updated once and the pipeline will persist without any processing. The compute
  • DAll datasets will be updated once and the pipeline will shut down. The compute resources will
  • EAll datasets will be updated at set intervals until the pipeline is shut down. The compute resources

How the community answered

(19 responses)
  • B
    5% (1)
  • D
    11% (2)
  • E
    84% (16)

Explanation

In Delta Live Tables, the Pipeline Mode determines how the cluster behaves after processing. Continuous Pipeline Mode keeps the pipeline perpetually running, processing new data as it arrives rather than shutting down after a single pass. In this mode, streaming tables continuously consume new records and non-streaming LIVE TABLEs are refreshed on a recurring schedule. Combined with Development mode (which keeps the cluster alive between runs to speed up iteration), the compute resources remain active and datasets are updated repeatedly at set intervals until the pipeline is manually stopped. This contrasts with Triggered mode, where the pipeline runs once and the cluster terminates.

Topics

#Delta Live Tables#Continuous Pipeline Mode#Streaming Data Processing#ELT Pipelines

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