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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
Real Exam Qty:	59
Exam Price:	USD 200
Certificate Validity Period:	2 years
Related Certifications:	Databricks Certified Associate Data Engineer
Exam Duration:	120 minutes
Exam Format:	Multiple Choice
Available Languages:	Japanese, English, Korean, Portuguese (Brazil)
Passing Score:	70%
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 is a leading cloud-based data engineering and analytics platform that enables organizations to process, store, and analyze large volumes of data. The platform offers a comprehensive suite of tools and services that help data engineers and data scientists to collaborate and streamline their workflows. To validate the skills and expertise of data engineers using the Databricks platform, Databricks offers the Databricks-Certified-Professional-Data-Engineer (Databricks Certified Professional Data Engineer) certification.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q161-Q166):

NEW QUESTION # 161
Which approach demonstrates a modular and testable way to use DataFrame.transform for ETL code in PySpark?

A. class Pipeline:
def transform(self, df):
return df.withColumn( " value_upper " , upper(col( " value " )))
pipeline = Pipeline()
assertDataFrameEqual(pipeline.transform(test_input), expected)
B. def upper_transform(df):
return df.withColumn( " value_upper " , upper(col( " value " )))
actual = test_input.transform(upper_transform)
assertDataFrameEqual(actual, expected)
C. 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)
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:
Databricks and Apache Spark recommend building modular and reusable ETL transformations by leveraging the DataFrame.transform() API. This method allows you to chain multiple transformation functions in a clean and testable way.
* Option A : Encapsulating the logic in a class (Pipeline) works, but it reduces modularity and flexibility.
It does not show the true intended use of DataFrame.transform() which is chaining functional transformations.
* Option B : This is the correct approach. It defines small, reusable functions (upper_value, filter_positive) that each take a DataFrame and return a transformed DataFrame. By chaining them with df.transform(func), you can compose ETL pipelines in a clear and declarative manner. This enables unit testing of individual functions and makes the ETL pipeline modular, testable, and production- ready.
* Option C : This shows a single transformation wrapped in a function and tested, but it lacks pipeline composition - it is not demonstrating modular chaining across multiple transformations.
* Option D : This simply defines a transformation function with hardcoded logic. It does not leverage DataFrame.transform() nor demonstrate modularity through composition.
Therefore, Option B is the best demonstration of how to use DataFrame.transform() in PySpark ETL pipelines.
Databricks documentation explicitly highlights that DataFrame.transform() allows developers to "chain together reusable functions in a readable and modular way, improving testability and maintainability of ETL code." This makes B the correct and officially supported pattern.

NEW QUESTION # 162
A healthcare analytics team is implementing a dimensional model in Delta Lake for patient care analysis.
They have a date dimension table and are evaluating design options to ensure it supports a wide range of time- based analyses.
Which design approach for the date dimension will support efficient time-based querying and aggregation?

A. Store the date as a string in the format YYYY-MM-DD for readability.
B. Store only the date value and calculate all time attributes dynamically in queries.
C. Create separate dimension tables for different calendar systems (fiscal, academic, etc.).
D. Pre-calculate attributes like fiscal_period, quarter, month_name, day_of_week, and holiday.

Answer: D

Explanation:
In dimensional modeling, Databricks recommends denormalized, attribute-rich dimension tables for performance and usability. A date dimension should include all commonly used derived time attributes such as fiscal period, quarter, month, weekday, and holiday flags. Precomputing these attributes ensures consistent business logic, eliminates repeated calculations during query time, and enables efficient filtering and aggregation. The documentation for Delta Lake and Lakehouse design explicitly advises precomputing these attributes for analytical workloads that depend heavily on time-based slicing. Options A and C degrade performance and consistency, while maintaining multiple calendar-specific dimension tables (B) complicates the model unnecessarily.

NEW QUESTION # 163
An analytics team wants to run a short-term experiment in Databricks SQL on the customer transactions Delta table (about 20 billion records) created by the data engineering team. Which strategy should the data engineering team use to ensure minimal downtime and no impact on the ongoing ETL processes?

A. Deep clone the table for the analytics team.
B. Shallow clone the table for the analytics team.
C. Create a new table for the analytics team using a CTAS statement.
D. Give the analytics team direct access to the production table.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
* Exact extract: "A shallow clone creates a copy of the metadata that references the source data files; it is fast and inexpensive."
* Exact extract: "A deep clone copies the data."
References: Delta Lake cloning (shallow vs deep).

NEW QUESTION # 164
A data engineering team is configuring access controls in Databricks Unity Catalog. They grant the SELECT privilege on the sales catalog to the analyst_group, expecting that members of this group will automatically have SELECT access to all current and future schemas, tables, and views within the catalog.
What describes the privilege inheritance behavior in Unity Catalog?

A. Privileges granted at the schema level override any catalog-level privileges and prevent access unless explicitly revoked.
B. Privileges in Unity Catalog do not cascade; SELECT must be explicitly granted on each schema and table, even if granted at the catalog level.
C. Granting SELECT at the catalog level applies to existing schemas and tables but not to those created in the future.
D. Granting SELECT on a catalog automatically applies SELECT to all current and future schemas, tables, and views within that catalog.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
In Unity Catalog, privileges are non-cascading-meaning that granting a privilege (like SELECT) on a catalog does not automatically grant the same privilege on contained objects (schemas, tables, or views). Each object type has its own independent access control hierarchy.
According to the Databricks access control documentation: "Privileges do not automatically cascade from catalog to schema or table levels." Administrators must explicitly grant privileges on each level if users need access across objects. This design ensures tighter governance and least-privilege enforcement. Therefore, option B correctly describes Unity Catalog's privilege model, while A and D incorrectly imply automatic inheritance.

NEW QUESTION # 165
What is a method of installing a Python package scoped at the notebook level to all nodes in the currently active cluster?

A. Run source env/bin/activate in a notebook setup script
B. Install libraries from PyPi using the cluster UI
C. Use &Pip install in a notebook cell
D. Use &sh install in a notebook cell

Answer: B

Explanation:
Installing a Python package scoped at the notebook level to all nodes in the currently active cluster in Databricks can be achieved by using the Libraries tab in the cluster UI. This interface allows you to install libraries across all nodes in the cluster. While the %pip command in a notebook cell would only affect the driver node, using the cluster UI ensures that the package is installed on all nodes.
Reference:
Databricks Documentation on Libraries: Libraries

NEW QUESTION # 166
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