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

Databricks Certified Data Engineer Professional Sample Questions:

Question 1

The data engineer team is configuring environment for development testing, and production before beginning migration on a new data pipeline. The team requires extensive testing on both the code and data resulting from code execution, and the team want to develop and test against similar production data as possible.
A junior data engineer suggests that production data can be mounted to the development testing environments, allowing pre production code to execute against production data. Because all users have Admin privileges in the development environment, the junior data engineer has offered to configure permissions and mount this data for the team.
Which statement captures best practices for this situation?

A. In environments where interactive code will be executed, production data should only be accessible with read permissions; creating isolated databases for each environment further reduces risks.
B. All developer, testing and production code and data should exist in a single unified workspace; creating separate environments for testing and development further reduces risks.
C. Because access to production data will always be verified using passthrough credentials it is safe to mount data to any Databricks development environment.
D. Because delta Lake versions all data and supports time travel, it is not possible for user error or malicious actors to permanently delete production data, as such it is generally safe to mount production data anywhere.

Question 2

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 permanently 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("ALTER TABLE my_table SET TBLPROPERTIES
('delta.deletedFileRetentionDuration' = 'interval 15 days')")
C. spark.conf.set("spark.databricks.delta.deletedFileRetentionDuration", "15 days")
D. spark.sql("VACUUM my_table RETAIN 15 HOURS")

Question 3

How are the operational aspects of Lakeflow Declarative Pipelines different from Spark Structured Streaming?

A. Lakeflow Declarative Pipelines manage the orchestration of multi-stage pipelines automatically, while Structured Streaming requires external orchestration for complex dependencies.
B. Structured Streaming can process continuous data streams, while Lakeflow Declarative Pipelines cannot.
C. Lakeflow Declarative Pipelines automatically handle schema evolution, while Structured Streaming always requires manual schema management.
D. Lakeflow Declarative Pipelines can write to Delta Lake format, while Structured Streaming cannot.

Question 4

A data engineer wants to ingest a large collection of image files (JPEG and PNG) from cloud object storage into a Unity Catalog-managed table for analysis and visualization. Which two configurations and practices are recommended to incrementally ingest these images into the table? (Choose two.)

A. Use Auto Loader and set cloudFiles.format to "IMAGE".
B. Use Auto Loader and set cloudFiles.format to "TEXT".
C. Use Auto Loader and set cloudFiles.format to "BINARYFILE".
D. Move files to a volume and read with SQL editor.
E. Use the pathGlobFilter option to select only image files (e.g., "*.jpg,*.png").

Question 5

Which statement characterizes the general programming model used by Spark Structured Streaming?

A. Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.
B. Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded table.
C. Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.
D. Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.
E. Structured Streaming uses specialized hardware and I/O streams to achieve sub-second latency for data transfer.

Solutions: