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

Section	Weight	Objectives
Topic 1: Cost & Performance Optimisation	13%	- Storage optimization (partitioning, Z-order, indexing) - Cluster configuration and scaling - Query optimization and caching
Topic 2: Data Ingestion & Acquisition	7%	- Auto Loader and streaming ingestion - Schema inference and evolution - Connecting to diverse data sources
Topic 3: Data Transformation, Cleansing, and Quality	10%	- Handling missing or inconsistent data - Standardization and normalization - Data validation and quality checks
Topic 4: Data Modelling	6%	- Medallion Architecture implementation - Schema design and management - Delta Lake table design
Topic 5: Monitoring and Alerting	10%	- Performance and health monitoring - Pipeline observability and logging - Setting up alerts and notifications
Topic 6: Ensuring Data Security and Compliance	10%	- Access control and permissions - Compliance standards implementation - Data encryption and masking
Topic 7: Debugging and Deploying	10%	- Troubleshooting pipelines and errors - Deployment using bundles, CLI, and APIs - CI/CD and DevOps practices
Topic 8: Developing Code for Data Processing using Python and SQL	22%	- Integration with Databricks APIs and tools - Batch and incremental processing logic - Data transformation and aggregation
Topic 9: Data Sharing and Federation	5%	- Unity Catalog data sharing - Cross-workspace and cross-cloud access
Topic 10: Data Governance	7%	- Policy enforcement - Data lineage and metadata tracking - Unity Catalog management

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Databricks Certified Professional Data Engineer Exam Databricks-Certified-Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q191-Q196):

191. Frage
A workspace admin has created a new catalog called finance_data and wants to delegate permission management to a finance team lead without giving them full admin rights.
Which privilege should be granted to the finance team lead?

A. Make the finance team lead a metastore admin.
B. MANAGE privilege on the finance_data catalog.
C. ALL PRIVILEGES on the finance_data catalog.
D. GRANT OPTION privilege on the finance_data catalog.

Antwort: B

Begründung:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
The MANAGE privilege in Unity Catalog provides the ability to grant and revoke privileges on the specified object (in this case, a catalog) without giving full administrative access or ownership.
This is the Databricks-recommended approach for delegating governance responsibilities while preserving the principle of least privilege.
By contrast, the ALL PRIVILEGES option grants excessive access (including read and write permissions), and metastore admin status provides global control over all catalogs-far exceeding the requirement. The MANAGE privilege enables the finance team lead to control access to objects within finance_data responsibly while limiting overall administrative exposure.

192. Frage
The data architect has mandated that all tables in the Lakehouse should be configured as external (also known as "unmanaged") Delta Lake tables.
Which approach will ensure that this requirement is met?

A. When the workspace is being configured, make sure that external cloud object storage has been mounted.
B. When data is saved to a table, make sure that a full file path is specified alongside the Delta format.
C. When a database is being created, make sure that the LOCATION keyword is used.
D. When tables are created, make sure that the EXTERNAL keyword is used in the CREATE TABLE statement.
E. When configuring an external data warehouse for all table storage, leverage Databricks for all ELT.

Antwort: D

Begründung:
To create an external or unmanaged Delta Lake table, you need to use the EXTERNAL keyword in the CREATE TABLE statement. This indicates that the table is not managed by the catalog and the data files are not deleted when the table is dropped. You also need to provide a LOCATION clause to specify the path where the data files are stored. For example:
CREATE EXTERNAL TABLE events ( date DATE, eventId STRING, eventType STRING, data STRING) USING DELTA LOCATION '/mnt/delta/events'; This creates an external Delta Lake table named events that references the data files in the '/mnt/delta/events' path. If you drop this table, the data files will remain intact and you can recreate the table with the same statement.
References:
* https://docs.databricks.com/delta/delta-batch.html#create-a-table
* https://docs.databricks.com/delta/delta-batch.html#drop-a-table

193. Frage
A data engineer needs to implement column masking for a sensitive column in a Unity Catalog-managed table. The masking logic must dynamically check if users belong to specific groups defined in a separate table (group_access) that maps groups to allowed departments.
Which approach should the engineer use to efficiently enforce this requirement?

A. Use a row filter to restrict access based on the user's group.
B. Create a UDF that hardcodes allowed groups and apply it as a column mask.
C. Create a view without selecting the sensitive column.
D. Apply a column mask that references the group_access mapping table in its UDF.

Antwort: D

Begründung:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Databricks Unity Catalog supports dynamic column masking, where masking logic can be implemented using SQL functions or UDFs that reference external mapping tables or metadata for context-aware access control.
By referencing the group_access table inside the masking function, the mask dynamically evaluates whether a requesting user belongs to an authorized group. If permitted, the original column value is returned; otherwise, a masked value (such as NULL or asterisks) is shown.
This method enables fine-grained, data-driven masking policies while maintaining a single authoritative access mapping source.
Hardcoding values (A) reduces flexibility, and row filters (D) apply to entire rows rather than specific columns. Therefore, C correctly aligns with Databricks best practices for dynamic masking.

194. Frage
A new data engineer has started at a company. The data engineer has recently been added to the company's
Databricks workspace as new.engineer@company.com. The data engineer needs to be able to query the table
sales in the database retail. The new data engineer already has been granted USAGE on the database retail.
Which of the following commands can be used to grant the appropriate permissions to the new data engineer?

A. GRANT SELECT ON TABLE new.engineer@company.com TO sales;
B. GRANT SELECT ON TABLE sales TO new.engineer@company.com;
C. GRANT USAGE ON TABLE new.engineer@company.com TO sales;
D. GRANT USAGE ON TABLE sales TO new.engineer@company.com;
E. GRANT CREATE ON TABLE sales TO new.engineer@company.com;

Antwort: B

195. Frage
if you run the command VACUUM transactions retain 0 hours? What is the outcome of this command?

A. Command will fail if you have an active transaction running
B. Command will fail, you cannot run the command with retentionDurationcheck enabled
C. Command will be successful, but historical data will be removed
D. Command runs successful and compacts all of the data in the table
E. Command will be successful, but no data is removed

Antwort: B

Begründung:
Explanation
The answer is,
Command will fail, you cannot run the command with retentionDurationcheck enabled.
1.VACUUM [ [db_name.]table_name | path] [RETAIN num HOURS] [DRY RUN]
*Recursively vacuum directories associated with the Delta table and remove data files that are no longer in the latest state of the transaction log for the table and are older than a retention threshold. Default is 7 Days.
*The reason this check is enabled is because, DELTA is trying to prevent unintentional deletion of history, and also one important thing to point out is with 0 hours of retention there is a possibility of data loss(see below kb) Documentation in VACUUM https://docs.delta.io/latest/delta-utility.html
https://kb.databricks.com/delta/data-missing-vacuum-parallel-write.html

196. Frage
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