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

An hourly batch job is configured to ingest data files from a cloud object storage container where each batch represent all records produced by the source system in a given hour. The batch job to process these records into the Lakehouse is sufficiently delayed to ensure no late-arriving data is missed. The user_id field represents a unique key for the data, which has the following schema:

user_id BIGINT, username STRING, user_utc STRING, user_region STRING, last_login BIGINT, auto_pay BOOLEAN, last_updated BIGINT

New records are all ingested into a table named account_history which maintains a full record of all data in the same schema as the source. The next table in the system is named account_current and is implemented as a Type 1 table representing the most recent value for each unique user_id.

Assuming there are millions of user accounts and tens of thousands of records processed hourly, which implementation can be used to efficiently update the described account_current table as part of each hourly batch job?

The data engineering team has configured a job to process customer requests to be forgotten (have their data deleted). All user data that needs to be deleted is stored in Delta Lake tables using default table settings.

The team has decided to process all deletions from the previous week as a batch job at 1am each Sunday. The total duration of this job is less than one hour. Every Monday at 3am, a batch job executes a series of VACUUM commands on all Delta Lake tables throughout the organization.

The compliance officer has recently learned about Delta Lake's time travel functionality. They are concerned that this might allow continued access to deleted data.

Assuming all delete logic is correctly implemented, which statement correctly addresses this concern?

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.