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Databricks Certified Data Engineer Professional Exam Questions and Answers

Question 1

All records from an Apache Kafka producer are being ingested into a single Delta Lake table with the following schema:

key BINARY, value BINARY, topic STRING, partition LONG, offset LONG, timestamp LONG

There are 5 unique topics being ingested. Only the "registration" topic contains Personal Identifiable Information (PII). The company wishes to restrict access to PII. The company also wishes to only retain records containing PII in this table for 14 days after initial ingestion. However, for non-PII information, it would like to retain these records indefinitely.

Which of the following solutions meets the requirements?

Options:

A.

All data should be deleted biweekly; Delta Lake's time travel functionality should be leveraged to maintain a history of non-PII information.

B.

Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory.

C.

Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken.

D.

Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level.

E.

Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.

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Question 2

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:

A.

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

B.

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.

C.

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

Question 3

A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late-arriving data.

Streaming DataFrame df has the following schema:

"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"

Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

Options:

A.

withWatermark("event_time", "10 minutes")

B.

awaitArrival("event_time", "10 minutes")

C.

await("event_time + ‘10 minutes'")

D.

slidingWindow("event_time", "10 minutes")

E.

delayWrite("event_time", "10 minutes")