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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
Available Languages:	English
Exam Price:	USD 200
Related Certifications:	Databricks Certified Data Engineer Associate
Exam Duration:	120 minutes
Real Exam Qty:	60-70
Certificate Validity Period:	2 years
Passing Score:	70%
Exam Format:	Multiple choice, Scenario-based questions, Multiple select
Recommended Training:	Databricks Academy
Exam Registration:	Databricks Certification Portal
Sample Questions:	Databricks Databricks-Certified-Professional-Data-Engineer Sample Questions
Exam Way:	Online proctored exam
Pre Condition:	Recommended: Databricks Certified Data Engineer Associate or equivalent experience with Spark and Databricks platform.
Official Syllabus URL:	https://www.databricks.com/learn/certification

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Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) Exam is a certification exam designed to test the knowledge and skills of data engineers who use Databricks to build and manage data pipelines. Databricks is a cloud-based data processing and analytics platform that provides a unified workspace for data scientists, data engineers, and business analysts to collaborate and work with large-scale data. Databricks-Certified-Professional-Data-Engineer Exam is intended for data engineers who have experience in developing and maintaining data pipelines using Databricks and are looking to validate their skills and knowledge.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q120-Q125):

NEW QUESTION # 120
A data engineering team is migrating off its legacy Hadoop platform. As part of the process, they are evaluating storage formats for performance comparison. The legacy platform uses ORC and RCFile formats.
After converting a subset of data to Delta Lake , they noticed significantly better query performance. Upon investigation, they discovered that queries reading from Delta tables leveraged a Shuffle Hash Join , whereas queries on legacy formats used Sort Merge Joins . The queries reading Delta Lake data also scanned less data.
Which reason could be attributed to the difference in query performance?

A. Delta Lake enables data skipping and file pruning using a vectorized Parquet reader.
B. The queries against the ORC tables leveraged the dynamic data skipping optimization but not the dynamic file pruning optimization.
C. Shuffle Hash Joins are always more efficient than Sort Merge Joins.
D. The queries against the Delta Lake tables were able to leverage the dynamic file pruning optimization.

Answer: A

Explanation:
Delta Lake outperforms legacy Hadoop formats because it leverages Parquet-based storage , data skipping
, and file pruning . According to Databricks documentation, Delta Lake automatically stores detailed statistics (min/max values and file-level metadata) in the transaction log. During query planning, the engine uses these statistics to skip entire files that do not match query filters , a process called data skipping and file pruning . Additionally, Delta uses a vectorized Parquet reader , which reduces I/O and CPU overhead.
Together, these optimizations allow Delta to scan significantly less data and produce more efficient physical query plans (e.g., Shuffle Hash Join instead of Sort Merge Join). The performance gain is due to efficient data skipping, not the inherent superiority of join type.

NEW QUESTION # 121
An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id.
For auditing purposes, the data governance team wishes to maintain a full record of all values that have ever been valid in the source system. For analytical purposes, only the most recent value for each record needs to be recorded. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?

A. Use merge into to insert, update, or delete the most recent entry for each pk_id into a bronze table, then propagate all changes throughout the system.
B. Ingest all log information into a bronze table; use merge into to insert, update, or delete the most recent entry for each pk_id into a silver table to recreate the current table state.
C. Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.
D. Iterate through an ordered set of changes to the table, applying each in turn; rely on Delta Lake's versioning ability to create an audit log.
E. Create a separate history table for each pk_id resolve the current state of the table by running a union all filtering the history tables for the most recent state.

Answer: B

Explanation:
This is the correct answer because it meets the requirements of maintaining a full record of all values that have ever been valid in the source system and recreating the current table state with only the most recent value for each record. The code ingests all log information into a bronze table, which preserves the raw CDC data as it is. Then, it uses merge into to perform an upsert operation on a silver table, which means it will insert new records or update or delete existing records based on the change type and the pk_id columns. This way, the silver table will always reflect the current state of the source table, while the bronze table will keep the history of all changes. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Upsert into a table using merge" section.

NEW QUESTION # 122
A developer has successfully configured credential for Databricks Repos and cloned a remote Git repository.
Hey don not have privileges to make changes to the main branch, which is the only branch currently visible in their workspace.
Use Response to pull changes from the remote Git repository commit and push changes to a branch that appeared as a changes were pulled.

A. Use Repos to create a new branch commit all changes and push changes to the remote Git repertory.
B. Use repos to create a fork of the remote repository commit all changes and make a pull request on the source repository
C. Use Repos to merge all differences and make a pull request back to the remote repository.
D. Use repos to merge all difference and make a pull request back to the remote repository.

Answer: A

Explanation:
In Databricks Repos, when a user does not have privileges to make changes directly to the main branch of a cloned remote Git repository, the recommended approach is to create a new branch within the Databricks workspace. The developer can then make changes in this new branch, commit those changes, and push the new branch to the remote Git repository. This workflow allows for isolated development without affecting the main branch, enabling the developer to propose changes via a pull request from the new branch to the main branch in the remote repository. This method adheres to common Git collaboration workflows, fostering code review and collaboration while ensuring the integrity of the main branch.
References:
* Databricks documentation on using Repos with Git: https://docs.databricks.com/repos.html

NEW QUESTION # 123
Which of the following describes a benefit of a data lakehouse that is unavailable in a traditional data
warehouse?

A. A data lakehouse utilizes proprietary storage formats for data
B. A data lakehouse enables both batch and streaming analytics
C. A data lakehouse provides a relational system of data management
D. A data lakehouse captures snapshots of data for version control purposes
E. A data lakehouse couples storage and compute for complete control

Answer: B

NEW QUESTION # 124
Which approach demonstrates a modular and testable way to use DataFrame.transform for ETL code in PySpark?

A. def upper_transform(df):
return df.withColumn("value_upper", upper(col("value")))
actual = test_input.transform(upper_transform)
assertDataFrameEqual(actual, 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_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)
D. 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)

Answer: C

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 # 125
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