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

Databricks Certified Data Engineer Professional Sample Questions:

1. Why are Pandas UDFs often preferred over traditional PySpark UDFs in performance-critical applications involving large datasets?

A) They leverage Apache Arrow to enable vectorized operations between the JVM and Python runtimes, reducing serialization costs and improving computational efficiency.
B) They allow row-level execution of functions in Python with native Spark optimization, removing the need for columnar execution.
C) They eliminate the JVM-Python boundary by bypassing serialization entirely, thereby avoiding data conversion overhead.
D) They minimize memory usage by streaming each row individually through a lightweight Python wrapper, avoiding batch processing overhead.

2. The following table consists of items found in user carts within an e-commerce website.

The following MERGE statement is used to update this table using an updates view, with schema evolution enabled on this table.

How would the following update be handled?

A) The new nested field is added to the target schema, and files underlying existing records are updated to include NULL values for the new field.
B) The new restored field is added to the target schema, and dynamically read as NULL for existing unmatched records.
C) The update is moved to separate ''restored'' column because it is missing a column expected in the target schema.
D) The update throws an error because changes to existing columns in the target schema are not supported.

3. A data engineer is designing a pipeline in Databricks that processes records from a Kafka stream where late-arriving data is common. Which approach should the data engineer use?

A) Use an Auto CDC pipeline with batch tables to simplify late data handling.
B) Use a watermark to specify the allowed lateness to accommodate records that arrive after their expected window, ensuring correct aggregation and state management.
C) Implement a custom solution using Databricks Jobs to periodically reprocess all historical data.
D) Use batch processing and overwrite the entire output table each time to ensure late data is incorporated correctly.

4. A data engineer has a Delta table orders with deletion vectors enabled. The engineer executes the following command:
DELETE FROM orders WHERE status = 'cancelled';
What should be the behavior of deletion vectors when the command is executed?

A) Rows are marked as deleted both in metadata and in files.
B) Delta automatically removes all cancelled orders permanently.
C) Rows are marked as deleted in metadata, not in files.
D) Files are physically rewritten without the deleted rows.

5. A Databricks SQL dashboard has been configured to monitor the total number of records present in a collection of Delta Lake tables using the following query pattern:
SELECT COUNT (*) FROM table
Which of the following describes how results are generated each time the dashboard is updated?

A) The total count of rows is calculated by scanning all data files
B) The total count of rows will be returned from cached results unless REFRESH is run
C) The total count of records is calculated from the parquet file metadata
D) The total count of records is calculated from the Hive metastore
E) The total count of records is calculated from the Delta transaction logs

Solutions: