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Die Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) Prüfung ist ein Zertifizierungsprogramm für Personen, die ihre Expertise im Aufbau, der Bereitstellung und der Wartung von Datenengineering-Lösungen mithilfe von Databricks demonstrieren möchten. Die Prüfung richtet sich an Dateningenieure, Datenarchitekten und andere Datenprofis, die mit großen Datenverarbeitungssystemen arbeiten und ihre Fähigkeiten und Kenntnisse in diesem Bereich validieren möchten.

Um die Datenbank für Datenbleal-Zertifizierungsprofessional-Data-Engineer-Prüfung zu machen, müssen die Kandidaten zunächst die zertifizierten Datenbäume für Datenbanken und Datenbanken zertifizierte Associate Data Engineer-Prüfungen abschließen. Diese Prüfungen bieten eine Grundlage von Wissen und Fähigkeiten, die für die bestehende Prüfung der professionellen Ebene erforderlich sind. Die Kandidaten müssen auch Erfahrung mit Datenbanken haben und mit den verschiedenen Funktionen und Funktionen vertraut sein.

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Die Prüfung ist für Dateningenieure mit Erfahrung in der Gestaltung und Umsetzung von Datenlösungen mit Databricks gedacht. Kandidaten für diese Zertifizierung sollten ein gutes Verständnis von Datenengineering-Konzepten, Datenverarbeitungs-Frameworks und Programmiersprachen wie Python und SQL haben. Sie sollten auch mit Cloud-Plattformen wie AWS, Azure und Google Cloud Platform vertraut sein.

Databricks Certified Professional Data Engineer Exam Databricks-Certified-Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q209-Q214):

209. Frage
A user wants to use DLT expectations to validate that a derived table report contains all records from the source, included in the table validation_copy.
The user attempts and fails to accomplish this by adding an expectation to the report table definition.
Which approach would allow using DLT expectations to validate all expected records are present in this table?

A. Define a function that performs a left outer join on validation_copy and report and report, and check against the result in a DLT expectation for the report table
B. Define a temporary table that perform a left outer join on validation_copy and report, and define an expectation that no report key values are null
C. Define a SQL UDF that performs a left outer join on two tables, and check if this returns null values for report key values in a DLT expectation for the report table.
D. Define a view that performs a left outer join on validation_copy and report, and reference this view in DLT expectations for the report table

Antwort: D

Begründung:
To validate that all records from the source are included in the derived table, creating a view that performs a left outer join between the validation_copy table and the report table is effective. The view can highlight any discrepancies, such as null values in the report table's key columns, indicating missing records. This view can then be referenced in DLT (Delta Live Tables) expectations for the report table to ensure data integrity. This approach allows for a comprehensive comparison between the source and the derived table.
References:
* Databricks Documentation on Delta Live Tables and Expectations: Delta Live Tables Expectations

210. Frage
A CHECK constraint has been successfully added to the Delta table named activity_details using the following logic:

A batch job is attempting to insert new records to the table, including a record where latitude = 45.50 and longitude = 212.67.
Which statement describes the outcome of this batch insert?

A. The write will include all records in the target table; any violations will be indicated in the boolean column named valid_coordinates.
B. The write will fail completely because of the constraint violation and no records will be inserted into the target table.
C. The write will insert all records except those that violate the table constraints; the violating records will be recorded to a quarantine table.
D. The write will fail when the violating record is reached; any records previously processed will be recorded to the target table.
E. The write will insert all records except those that violate the table constraints; the violating records will be reported in a warning log.

Antwort: B

Begründung:
The CHECK constraint is used to ensure that the data inserted into the table meets the specified conditions. In this case, the CHECK constraint is used to ensure that the latitude and longitude values are within the specified range. If the data does not meet the specified conditions, the write operation will fail completely and no records will be inserted into the target table. This is because Delta Lake supports ACID transactions, which means that either all the data is written or none of it is written. Therefore, the batch insert will fail when it encounters a record that violates the constraint, and the target table will not be updated. Reference:
Constraints: https://docs.delta.io/latest/delta-constraints.html
ACID Transactions: https://docs.delta.io/latest/delta-intro.html#acid-transactions

211. Frage
The data architect has decided that once data has been ingested from external sources into the Databricks Lakehouse, table access controls will be leveraged to manage permissions for all production tables and views.
The following logic was executed to grant privileges for interactive queries on a production database to the core engineering group.
GRANT USAGE ON DATABASE prod TO eng;
GRANT SELECT ON DATABASE prod TO eng;
Assuming these are the only privileges that have been granted to the eng group and that these users are not workspace administrators, which statement describes their privileges?

A. Group members are able to create, query, and modify all tables and views in the prod database, but cannot define custom functions.
B. Group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database.
C. Group members have full permissions on the prod database and can also assign permissions to other users or groups.
D. Group members are able to query and modify all tables and views in the prod database, but cannot create new tables or views.
E. Group members are able to list all tables in the prod database but are not able to see the results of any queries on those tables.

Antwort: B

Begründung:
The GRANT USAGE ON DATABASE prod TO eng command grants the eng group the permission to use the prod database, which means they can list and access the tables and views in the database. The GRANT SELECT ON DATABASE prod TO eng command grants the eng group the permission to select data from the tables and views in the prod database, which means they can query the data using SQL or DataFrame API.
However, these commands do not grant the eng group any other permissions, such as creating, modifying, or deleting tables and views, or defining custom functions. Therefore, the eng group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database. References:
* Grant privileges on a database: https://docs.databricks.com/en/security/auth-authz/table-acls/grant- privileges-database.html
* Privileges you can grant on Hive metastore objects: https://docs.databricks.com/en/security/auth-authz
/table-acls/privileges.html

212. Frage
A data engineer is tasked with ensuring that a Delta table in Databricks continuously retains deleted files for
15 days instead of the default 7 days, in order to comply with the organization's data retention policy. Which code snippet correctly sets this retention period for deleted files?

A. from delta.tables import *
deltaTable = DeltaTable.forPath(spark, " /mnt/data/my_table " )
deltaTable.deletedFileRetentionDuration = " interval 15 days "
B. spark.sql( " VACUUM my_table RETAIN 15 HOURS " )
C. spark.sql( " " "
ALTER TABLE my_table
SET TBLPROPERTIES ( ' delta.deletedFileRetentionDuration ' = ' interval 15 days ' )
" " " )
D. spark.conf.set( " spark.databricks.delta.deletedFileRetentionDuration " , " 15 days " )

Antwort: C

Begründung:
Databricks documents delta.deletedFileRetentionDuration as a Delta table property and shows that Delta table properties are modified with SET TBLPROPERTIES . The documented value format is an interval expression such as ' interval 7 days ' , so ' interval 15 days ' is the correct way to set the retention window on the table itself. ( Databricks Documentation ) Option C controls a specific VACUUM run and is shown in hours here, not as a persistent table-level retention setting. Option D sets a Spark configuration rather than the table property required by the question.
Option B is not the documented API for setting this Delta retention property. ( Databricks Documentation )
======

213. Frage
A data engineer needs to install the PyYAML Python package within an air-gapped Databricks environment. The workspace has no direct internet access to PyPI. The engineer has downloaded the .whl file locally and wants it available automatically on all new clusters.
Which approach should the data engineer use?

A. Add the .whl file to Databricks Git Repos and assume automatic installation.
B. Upload the PyYAML .whl file to a Unity Catalog Volume, ensure it's allow-listed, and create a cluster-scoped init script that installs it from that path.
C. Set up a private PyPI repository and install via pip index URL.
D. Upload the PyYAML .whl file to the user home directory and create a cluster-scoped init script to install it.

Antwort: B

Begründung:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
For secure, air-gapped Databricks deployments, the recommended practice is to host dependency files such as .whl packages in Unity Catalog Volumes - a managed storage layer governed by Unity Catalog.
Once stored in a volume, these files can be safely referenced from cluster-scoped init scripts, which automatically execute installation commands (e.g., pip install /Volumes/catalog/schema/path/PyYAML.whl) during cluster startup.
This ensures consistent environment setup across clusters and compliance with data governance rules.
User directories (A) lack enterprise security controls; private repositories (C) are not viable in air-gapped setups; and Git repos (D) do not trigger package installation. Therefore, B is the correct and officially approved method.

214. Frage
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