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Databricks Certified Data Engineer Professional Exam Questions and Answers

Question 1

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.

Options:

The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.

The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.

The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.

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Question 2

Which is a key benefit of an end-to-end test?

Options:

It closely simulates real world usage of your application.

It pinpoint errors in the building blocks of your application.

It provides testing coverage for all code paths and branches.

It makes it easier to automate your test suite

Question 3

Which statement describes the correct use of pyspark.sql.functions.broadcast?

Options:

It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.

It marks a column as small enough to store in memory on all executors, allowing a broadcast join.

It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.

It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.

It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.

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