You have an Azure Databricks workspace that is enabled for Unity Catalog.

The correct answer is C — @dlt.expect_or_fail.

Lakeflow Spark Declarative Pipelines (SDP) offers three expectation decorators, each with a different violation response:

@dlt.expect — logs the violation as a metric but writes all records, including bad ones, to the table. Suitable for monitoring only.

@dlt.expect_or_drop — drops violating records and continues the pipeline. The table receives only clean rows, but the pipeline update commits successfully.

@dlt.expect_or_fail — fails the entire pipeline update when a violation is detected. The table update is never committed. This is the correct choice when data integrity is non-negotiable: ' Prevent an update to Table1 from being committed when data that violates rule1 is detected. '

@dlt.expect_all_or_drop takes a dictionary of rules and drops violating rows but still commits — it doesn ' t halt the pipeline.