Databricks Databricks-Certified-Professional-Data-Engineer Exam With Confidence Using Practice Dumps

Lakeflow Declarative Pipelines (DLT) supports data quality enforcement using @dlt.expect, @dlt.expect_or_drop, and @dlt.expect_all.

@dlt.expect applies a rule and records whether rows pass or fail the condition but does not drop failing rows . Instead, failing rows can be written to a quarantine table.

@dlt.expect_or_drop enforces that only rows passing the condition flow downstream, dropping bad records automatically.

In this case, the requirement is:

Good rows (reading < 120) go to the silver table .

Bad rows (reading > = 120) go to a quarantine table .

Bad rows should not be included in silver.

The correct implementation is Option A , where:

The silver table uses @dlt.expect to validate reading < 120. These rows flow normally.

The quarantine table applies an expectation for reading > = 120, ensuring bad records are captured separately.

Other options are incorrect:

Option B/D: These either use expect_or_drop incorrectly or apply wrong conditions, leading to dropped rows without quarantining properly.

Option C: Uses expect_or_drop for both tables, which would discard bad rows instead of persisting them into a quarantine table.

Thus, Option A meets the business requirement to split good and bad data streams while ensuring both are captured for auditing and processing.

Deletion vectors (DVs) in Delta Lake optimize delete operations by marking deleted rows logically in metadata rather than rewriting Parquet files. When a DELETE statement is executed, affected rows are tracked by DVs in the transaction log. The data remains in the underlying files but is filtered out during query reads. This improves performance for frequent deletes and updates since file rewrites are deferred. Physical data removal only occurs when a VACUUM command is later executed. The Databricks documentation confirms: “With deletion vectors, deleted rows are marked in metadata and skipped at read time, avoiding file rewrites.” Thus, rows are marked as deleted in metadata—not in files.