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.