Databricks Databricks-Certified-Data-Engineer-Professional : Databricks Certified Data Engineer Professional Exam

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Databricks Certified Data Engineer Professional Databricks-Certified-Data-Engineer-Professional Prüfungsfragen mit Lösungen:

1. A data engineer is building a customer data pipeline in Lakeflow Spark Declarative Pipelines. The source is a cloud-based event stream with limited retention containing inserts, updates, and deletes for customer records. These changes are being applied using the AUTO CDC INTO syntax to maintain an SCD Type 1 table as the target table, customer_dim. How should the data engineer build a downstream job that streams from the customer_dim table to only act on updates and delete events, processing data incrementally?

A) Streaming from customer_dim table would only be possible in the case of SCD 2 retention.
B) Read change data feed from customer_dim table and apply filters to incrementally act on the change events.
C) When stored as SCD 1, the target of AUTO CDC INTO includes updates and deletes. Streaming from customer_dim can fail due to these operations. Instead, build another stream from the original source.
D) Use ignoreChanges flag while streaming from customer_dim to avoid breaking the pipeline during updates and deletes.

2. A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on task A.
If tasks A and B complete successfully but task C fails during a scheduled run, which statement describes the resulting state?

A) All logic expressed in the notebook associated with tasks A and B will have been successfully completed; some operations in task C may have completed successfully.
B) All logic expressed in the notebook associated with tasks A and B will have been successfully completed; any changes made in task C will be rolled back due to task failure.
C) All logic expressed in the notebook associated with task A will have been successfully completed; tasks B and C will not commit any changes because of stage failure.
D) Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task C failed, all commits will be rolled back automatically.
E) Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until ail tasks have successfully been completed.

3. A data engineer wants to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impressions led to monetizable clicks.
In the code below, Impressions is a streaming DataFrame with a watermark ("event_time", "10 minutes")

The data engineer notices the query slowing down significantly.
Which solution would improve the performance?

A) Joining on event time constraint: clickTime >= impressionTime AND clickTime <= impressionTime interval 1 hour
B) Joining on event time constraint: clickTime == impressionTime using a leftOuter join
C) Joining on event time constraint: clickTime >= impressionTime - interval 3 hours and removing watermarks
D) Joining on event time constraint: clickTime + 3 hours < impressionTime - 2 hours

4. A data engineer has created a transactions Delta table on Databricks that should be used by the analytics team. The analytics team wants to use the table with another tool that requires Apache Iceberg format. What should the data engineer do?

A) Convert the transactions Delta table to Iceberg and enable uniform so that the table can be read as a Delta table.
B) Enable uniform on the transactions table to 'iceberg' so that the table can be read as an Iceberg table.
C) Require the analytics team to use a tool that supports Delta table.
D) Create an Iceberg copy of the transactions Delta table which can be used by the analytics team.

5. The data governance team has instituted a requirement that the "user" table containing Personal Identifiable Information (PII) must have the appropriate masking on the SSN column. This means that anyone outside of the HRAdminGroup should see masked social security numbers as ***-**-
****.
The team created a masking function:

What does the data governance team need to do next to achieve this goal?

A) CREATE TABLE users
(name STRING, ssn STRING);
ALTER TABLE users ALTER COLUMN ssn SET MASK ssn_mask;
B) CREATE TABLE users
(name STRING);
ALTER TABLE users CREATE COLUMN ssn CREATE MASK ssn_mask;
C) CREATE TABLE users
(name STRING, int STRING);
ALTER TABLE users ALTER COLUMN ssn CREATE MASK if is_member('HRAdminGroup');
D) CREATE TABLE users
(name STRING, ssn INT MASKED ssn_mask);

Fragen und Antworten: