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Databricks Databricks-Certified-Professional-Data-Engineer Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Professional Data Engineer Exam
Exam Number:	Databricks-Certified-Professional-Data-Engineer
Certificate Validity Period:	2 years
Exam Price:	USD 200
Related Certifications:	Databricks Certified Associate Data Engineer
Available Languages:	Japanese, Korean, Portuguese (Brazil), English
Passing Score:	70%
Exam Format:	Multiple Choice
Exam Duration:	120 minutes
Real Exam Qty:	59
Recommended Training:	Databricks Data Engineer Professional Training
Exam Registration:	Databricks Official Certification Registration
Sample Questions:	Databricks Databricks-Certified-Professional-Data-Engineer Sample Questions
Exam Way:	Online proctored or onsite test center
Pre Condition:	No mandatory prerequisites; 1+ year hands-on experience and related training highly recommended
Official Syllabus URL:	https://www.databricks.com/learn/certification/data-engineer-professional

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Databricks Certified Professional Data Engineer certification is an excellent choice for individuals who are looking to specialize in data engineering and want to demonstrate their expertise in Databricks technologies. It is also a valuable credential for companies that use Databricks and want to ensure that their employees have the necessary skills to manage and analyze large amounts of data effectively.

Databricks Certified Professional Data Engineer certification is a valuable credential for data engineers who want to demonstrate their skills and proficiency in using Databricks for data engineering tasks. Databricks Certified Professional Data Engineer Exam certification can help data engineers to advance their careers and increase their earning potential. It can also help organizations to identify and hire skilled data engineers who can design and implement data solutions using Databricks.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q36-Q41):

NEW QUESTION # 36
Which approach demonstrates a modular and testable way to use DataFrame transform for ETL code in PySpark?

A. def transform_data(input_df):
# transformation logic here
return output_df
test_input = spark.createDataFrame([(1, " a " )], [ " id " , " value " ]) assertDataFrameEqual(transform_data(test_input), expected)
B. class Pipeline:
def transform(self, df):
return df.withColumn( " value_upper " , upper(col( " value " )))
pipeline = Pipeline()
assertDataFrameEqual(pipeline.transform(test_input), expected)
C. def upper_transform(df):
return df.withColumn( " value_upper " , upper(col( " value " )))
actual = test_input.transform(upper_transform)
assertDataFrameEqual(actual, expected)
D. def upper_value(df):
return df.withColumn( " value_upper " , upper(col( " value " )))
def filter_positive(df):
return df.filter(df[ " id " ] > 0)
pipeline_df = df.transform(upper_value).transform(filter_positive)

Answer: D

Explanation:
Apache Spark documents DataFrame.transform(func, *args, **kwargs) as concise syntax for chaining custom transformations, where the function takes a DataFrame and returns a DataFrame. The official example explicitly shows chained transforms, which makes option B the most modular and idiomatic ETL design among the choices. ( Apache Spark ) Option D shows a valid single transform and test, but it does not demonstrate the modular pipeline composition aspect as clearly as B. Option A does not actually use DataFrame.transform , and option C wraps logic in a class method but does not demonstrate the documented chaining pattern that transform is designed for. ( Apache Spark )
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NEW QUESTION # 37
A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?

A. Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly- parallel writes.
B. Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.
C. All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.
D. Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.

Answer: C

Explanation:
In Databricks and Delta Lake, transactions are indeed ACID-compliant, but this compliance is limited to single table transactions. Delta Lake does not inherently enforce foreign key constraints, which are a staple in relational database systems for maintaining referential integrity between tables. This means that when migrating workloads from a relational database system to Databricks Lakehouse, engineers need to reconsider how to maintain data integrity and relationships that were previously enforced by foreign key constraints.
Unlike traditional relational databases where foreign key constraints help in maintaining the consistency across tables, in Databricks Lakehouse, the data engineer has to manage data consistency and integrity at the application level or through careful design of ETL processes.
:
Databricks Documentation on Delta Lake: Delta Lake Guide
Databricks Documentation on ACID Transactions in Delta Lake: ACID Transactions in Delta Lake

NEW QUESTION # 38
How do you access or use tables in the unity catalog?

A. catalog_name.database_name.schema_name.table_name
B. schema_name.table_name
C. catalog_name.schema_name.table_name
D. schema_name.catalog_name.table_name
E. catalog_name.table_name

Answer: C

Explanation:
Explanation
The answer is catalog_name.schema_name.table_name
Graphical user interface, diagram Description automatically generated

Note: Database and Schema are analogous they are interchangeably used in the Unity catalog.
FYI, A catalog is registered under a metastore, by default every workspace has a default metastore called hive_metastore, with a unity catalog you have the ability to create meatstores and share that across multiple workspaces.

Diagram Description automatically generated

NEW QUESTION # 39
Which statement describes integration testing?

A. Validates interactions between subsystems of your application
B. Requires manual intervention
C. Requires an automated testing framework
D. Validates an application use case
E. Validates behavior of individual elements of your application

Answer: A

Explanation:
Explanation
This is the correct answer because it describes integration testing. Integration testing is a type of testing that validates interactions between subsystems of your application, such as modules, components, or services.
Integration testing ensures that the subsystems work together as expected and produce the correct outputs or results. Integration testing can be done at different levels of granularity, such as component integration testing, system integration testing, or end-to-end testing. Integration testing can help detect errors or bugs that may not be found by unit testing, which only validates behavior of individual elements of your application. Verified References: [Databricks Certified Data Engineer Professional], under "Testing" section; Databricks Documentation, under "Integration testing" section.

NEW QUESTION # 40
In order to use Unity catalog features, which of the following steps needs to be taken on man-aged/external tables in the Databricks workspace?

A. Upgrade workspace to Unity catalog
B. Migrate/upgrade objects in workspace managed/external tables/view to unity catalog
C. Enable unity catalog feature in workspace settings
D. Copy data from workspace to unity catalog
E. Upgrade to DBR version 15.0

Answer: B

Explanation:
Explanation
Upgrade tables and views to Unity Catalog - Azure Databricks | Microsoft Docs Managed table: Upgrade a managed to Unity Catalog External table: Upgrade an external table to Unity Catalog

NEW QUESTION # 41
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