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

Databricks Certified Data Engineer Professional Sample Questions:

Question 1

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

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

Question 2

What is the first line of a Databricks Python notebook when viewed in a text editor?

A. # Databricks notebook source
B. # MAGIC %python
C. %python
D. // Databricks notebook source
E. -- Databricks notebook source

Question 3

A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?

A. The Parquet file footers are scanned for min and max statistics for the latitude column
B. All records are cached to attached storage and then the filter is applied
C. All records are cached to an operational database and then the filter is applied
D. The Delta log is scanned for min and max statistics for the latitude column
E. The Hive metastore is scanned for min and max statistics for the latitude column

Question 4

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

Question 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: