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Databricks Certified Data Engineer Professional Sample Questions:

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 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.
D) Because access to production data will always be verified using passthrough credentials it is safe to mount data to any Databricks development environment.

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.sql("VACUUM my_table RETAIN 15 HOURS")
D) spark.conf.set("spark.databricks.delta.deletedFileRetentionDuration", "15 days")

3. What statement is true regarding the retention of job run history?

A) It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3
B) It is retained for 60 days, after which logs are archived
C) It is retained until you export or delete job run logs
D) It is retained for 90 days or until the run-id is re-used through custom run configuration
E) It is retained for 60 days, during which you can export notebook run results to HTML

4. A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.
assert(myIntegrate(lambda x: x*x, 0, 3) [0] == 9)
Which kind of the test does the above line exemplify?

A) Integration
B) functional
C) Unit
D) Manual
E) End-to-end

5. A streaming video analytics team ingests billions of events daily into a Unity Catalog-managed Delta table video_events. Analysts run ad-hoc point-lookup queries on columns like user_id, campaign_id, and region. The team manually runs OPTIMIZE video_events ZORDER BY (user_id, campaign_id, region), but still sees poor performance on recent data and dislikes the operational overhead. The team wants a hands-off way to keep hot columns co-located as query patterns evolve. Which Delta capability should the team leverage on video_events?

A) Enable auto-compaction (optimizeWrite and autoCompact).
B) Utilize Liquid Clustering (CLUSTER BY AUTO) and Predictive Optimization.
C) Enable Delta caching.
D) Schedule OPTIMIZE/ZORDER to run after each job to improve recent file performance.

Solutions: