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

The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.

The following logic is used to process these records.

MERGE INTO customers

USING (

SELECT updates.customer_id as merge_ey, updates .*

FROM updates

UNION ALL

SELECT NULL as merge_key, updates .*

FROM updates JOIN customers

ON updates.customer_id = customers.customer_id

WHERE customers.current = true AND updates.address <> customers.address

) staged_updates

ON customers.customer_id = mergekey

WHEN MATCHED AND customers. current = true AND customers.address <> staged_updates.address THEN

UPDATE SET current = false, end_date = staged_updates.effective_date

WHEN NOT MATCHED THEN

INSERT (customer_id, address, current, effective_date, end_date)

VALUES (staged_updates.customer_id, staged_updates.address, true, staged_updates.effective_date, null)

Which statement describes this implementation?

The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.

A DLT pipeline includes the following streaming tables:

Raw_lot ingest raw device measurement data from a heart rate tracking device.

Bgm_stats incrementally computes user statistics based on BPM measurements from raw_lot.

How can the data engineer configure this pipeline to be able to retain manually deleted or updated records in the raw_iot table while recomputing the downstream table when a pipeline update is run?

What statement is true regarding the retention of job run history?