Lakeflow Declarative Pipelines (LDP), formerly Delta Live Tables (DLT), supports enforcing data quality using expectations . Expectations can either:
* Track violations (expect) # records that do not meet conditions are flagged but still included in the pipeline.
* Drop violations (expect_or_drop) # records that do not meet conditions are excluded from downstream tables.
* Fail pipeline on violations (expect_or_fail) # records that fail conditions stop the pipeline.
In this scenario, the requirement explicitly states that invalid records (where customer_id is null or amount #
0) must be dropped . According to the official documentation, the correct method is .expect_or_drop( " expectation_name " , " SQL_predicate " ) applied on the streaming input.
* Option A is correct: It uses .expect_or_drop directly within the transformation chain for both rules, ensuring records that fail are removed before writing to the silver table.
* Option B incorrectly uses @dlt.expect decorators, which only track violations but do not drop invalid rows.
* Option C uses .expect, which also only flags rows, not drop them.
* Option D uses @dlt.expect_or_drop decorator syntax, which is not supported in Python API; expect_or_drop must be applied as a method on the DataFrame, not as a decorator.
Therefore, the correct solution is Option A , which ensures compliance by enforcing data quality and dropping invalid rows programmatically during ingestion.
Reference: Databricks Lakeflow Declarative Pipelines Documentation - Expectations (expect, expect_or_drop, expect_or_fail)