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

Section	Objectives
Data Modeling and Transformation	- Performance optimization techniques - Dimensional modeling concepts - Spark SQL transformations
Production Pipelines and Orchestration	- Databricks Workflows - Job scheduling and monitoring - Error handling and recovery strategies
Delta Lake and Data Management	- Delta Lake transactions and ACID properties - Schema evolution and enforcement - Time travel and versioning
Data Ingestion and Processing	- ETL pipeline design patterns - Batch and streaming ingestion with Auto Loader - Structured Streaming fundamentals
Databricks Lakehouse Platform Architecture	- Data governance concepts (Unity Catalog basics) - Medallion architecture (Bronze, Silver, Gold) - Workspace and cluster architecture

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Databricks Certified Data Engineer Professional Exam Databricks-Certified-Data-Engineer-Professional Prüfungsfragen mit Lösungen (Q77-Q82):

77. Frage
Incorporating unit tests into a PySpark application requires upfront attention to the design of your jobs, or a potentially significant refactoring of existing code.
Which statement describes a main benefit that offset this additional effort?

A. Validates a complete use case of your application
B. Ensures that all steps interact correctly to achieve the desired end result
C. Yields faster deployment and execution times
D. Improves the quality of your data
E. Troubleshooting is easier since all steps are isolated and tested individually

Antwort: E

Begründung:
Unit tests are small, isolated tests that are used to check specific parts of the code, such as functions or classes.

78. Frage
Which statement describes a key benefit of an end-to-end test?

A. It closely simulates real world usage of your application.
B. It provides testing coverage for all code paths and branches.
C. It pinpoint errors in the building blocks of your application.
D. It makes it easier to automate your test suite

Antwort: A

Begründung:
End-to-end testing is a methodology used to test whether the flow of an application, from start to finish, behaves as expected. The key benefit of an end-to-end test is that it closely simulates real- world, user behavior, ensuring that the system as a whole operates correctly.
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79. Frage
The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.

Assuming that user_id is a unique identifying key and that all users have requested deletion have been removed from the user_lookup table, which statement describes whether successfully executing the above logic guarantees that the records to be deleted from the user_aggregates table are no longer accessible and why?

A. No; files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.
B. Yes; Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.
C. Yes; the change data feed uses foreign keys to ensure delete consistency throughout the Lakehouse.
D. No; the change data feed only tracks inserts and updates not deleted records.
E. No; the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command

Antwort: A

Begründung:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from Explanation:
The DELETE operation in Delta Lake is ACID compliant, which means that once the operation is successful, the records are logically removed from the table. However, the underlying files that contained these records may still exist and be accessible via time travel to older versions of the table. To ensure that these records are physically removed and compliance with GDPR is maintained, a VACUUM command should be used to clean up these data files after a certain retention period. The VACUUM command will remove the files from the storage layer, and after this, the records will no longer be accessible.

80. Frage
Which statement regarding stream-static joins and static Delta tables is correct?

A. The checkpoint directory will be used to track updates to the static Delta table.
B. Stream-static joins cannot use static Delta tables because of consistency issues.
C. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.
D. The checkpoint directory will be used to track state information for the unique keys present in the join.
E. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch.

Antwort: E

Begründung:
This is the correct answer because stream-static joins are supported by Structured Streaming when one of the tables is a static Delta table. A static Delta table is a Delta table that is not updated by any concurrent writes, such as appends or merges, during the execution of a streaming query. In this case, each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch, which means it will reflect any changes made to the static Delta table before the start of each microbatch.

81. Frage
A company has a task management system that tracks the most recent status of tasks. The system takes task events as input and processes events in near real-time using Lakeflow Declarative Pipelines. A new task event is ingested into the system when a task is created or the task status is changed. Lakeflow Declarative Pipelines provides a streaming table (tasks_status) for BI users to query.
The table represents the latest status of all tasks and includes 5 columns:
task_id (unique for each task)
task_name
task_owner
task_status
task_event_time
The table enables three properties: deletion vectors, row tracking, and change data feed (CDF).
A data engineer is asked to create a new Lakeflow Declarative Pipeline to enrich the tasks_status table in near real-time by adding one additional column representing task_owner's department, which can be looked up from a static dimension table (employee).
How should this enrichment be implemented?

A. Create a new Lakeflow Declarative Pipeline: use the read() function to read tasks_status table; enrich with employee table; store the result in a materialized view.
B. Create a new Lakeflow Declarative Pipeline: use the readStream() function with the option skipChangeCommits to read the tasks_status table; enrich with the employee table; store the result in a new streaming table.
C. Create a new Lakeflow Declarative Pipeline: use the readStream() function to read tasks_status table; enrich with the employee table; store the result in a new streaming table.
D. Create a new Lakeflow Declarative Pipeline: use readStream() function with option readChangeFeed to read tasks_status table CDF; enrich with the employee table; create a new streaming table as the result table and use apply_changes() function to process the changes from the enriched CDF.

Antwort: D

Begründung:
Change Data Feed (CDF) allows downstream consumers to read incremental changes (inserts, updates, deletes) from a Delta table. The documentation explains that when streaming from a Delta table with CDF enabled, developers can use readStream().option("readChangeFeed","true") to capture incremental events. For maintaining a derived table with enrichment logic, the recommended practice is to use apply_changes(), which applies CDC semantics (insert/update/delete) correctly to the target streaming table. By joining with the static employee dimension, enriched rows are generated before being merged into the new streaming target. This ensures correctness, scalability, and minimal latency. Batch reads or skipping commits do not maintain correctness for CDC pipelines.

82. Frage
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