The Databricks Certified Professional Data Engineer certification has a strong reputation for a reason — the Databricks-Certified-Professional-Data-Engineer exam tests applied skills, not memorized definitions. Candidates around the world use Actual4Exams practice questions to close knowledge gaps before test day.
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 |
| Exam Price: | USD 200 |
| Certificate Validity Period: | 2 years |
| Passing Score: | 70% |
| Exam Duration: | 120 minutes |
| Available Languages: | Portuguese (Brazil), English, Korean, Japanese |
| Related Certifications: | Databricks Certified Associate Data Engineer |
| Real Exam Qty: | 59 |
| Exam Format: | Multiple Choice |
| Recommended Training: | Databricks Data Engineer Professional Training |
| Exam Registration: | Databricks Official Certification Registration |
| 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 |
Databricks Databricks-Certified-Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Modelling | 6% | - Schema design and management - Delta Lake table design - Medallion Architecture implementation |
| Ensuring Data Security and Compliance | 10% | - Access control and permissions - Compliance standards implementation - Data encryption and masking |
| Data Sharing and Federation | 5% | - Cross-workspace and cross-cloud access - Unity Catalog data sharing |
| Debugging and Deploying | 10% | - Troubleshooting pipelines and errors - Deployment using bundles, CLI, and APIs - CI/CD and DevOps practices |
| Cost & Performance Optimisation | 13% | - Query optimization and caching - Storage optimization (partitioning, Z-order, indexing) - Cluster configuration and scaling |
| Data Governance | 7% | - Data lineage and metadata tracking - Policy enforcement - Unity Catalog management |
| Data Transformation, Cleansing, and Quality | 10% | - Standardization and normalization - Handling missing or inconsistent data - Data validation and quality checks |
| Data Ingestion & Acquisition | 7% | - Connecting to diverse data sources - Auto Loader and streaming ingestion - Schema inference and evolution |
| Developing Code for Data Processing using Python and SQL | 22% | - Batch and incremental processing logic - Integration with Databricks APIs and tools - Data transformation and aggregation |
| Monitoring and Alerting | 10% | - Pipeline observability and logging - Setting up alerts and notifications - Performance and health monitoring |
Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) — Questions Candidates Actually Ask
The Databricks-Certified-Professional-Data-Engineer exam, officially known as Databricks Certified Professional Data Engineer, is the Databricks test that leads to the Databricks Certified Professional Data Engineer certification at the Professional level. Passing it validates the skills employers expect from a certified professional. It is also associated with related credentials such as Databricks Certified Associate Data Engineer.
The Databricks-Certified-Professional-Data-Engineer exam contains 59 questions, and you have 120 minutes to complete them. Work out your per-question pace before test day, and flag slow items instead of stalling on them — time pressure, not knowledge, sinks many first attempts. Timed mock exams in the Actual4Exams test engines are the most reliable way to build that rhythm.
The passing score for the Databricks-Certified-Professional-Data-Engineer exam is 70%, and the official registration fee is USD 200. If you miss the mark, a retake means paying the full fee again, so book your seat only when you are ready. A practical benchmark: score consistently above the passing line on timed practice tests before scheduling the real exam.
No mandatory prerequisites; 1+ year hands-on experience and related training highly recommended
Entry requirements can change, so confirm the latest conditions on the official exam page: https://www.databricks.com/learn/certification/data-engineer-professional.
You can book the Databricks-Certified-Professional-Data-Engineer exam through the official registration channels below:
Exam delivery: Online proctored or onsite test center. Seats at popular test centers fill quickly, so schedule early once your preparation is on track.
Databricks recommends the following training options for Databricks Certified Professional Data Engineer candidates:
Pair any course with the 217 practice questions from Actual4Exams to measure how ready you really are before paying the exam fee.
Yes. A free PDF demo of the Databricks Certified Professional Data Engineer questions is available, so you can check the question style and answer quality before you pay. Every purchase also includes 365 days of free updates, and if the product expires you can renew the update service at a 50% discount from your member zone.
If you take the corresponding Databricks-Certified-Professional-Data-Engineer exam within 60 days of purchase and do not pass, you can apply for a full refund under the 100% Money Back Guarantee: submit a scan of your enrollment slip and your official Score Report (PDF) within 2 days of the exam date, and the claim is processed within 7 days. Attempts made within 3 days of purchase, downloads without an actual exam attempt, free materials, and expired orders are not eligible, and the candidate name must match the payer name. Prefer new material instead of a refund? You can exchange your purchase for two free products of equal value and keep the update service on your original product. As for delivery, the files are available for instant download and are also emailed to you within one minute of payment — if nothing arrives within 2 hours, contact customer service. There is no limit on how many computers you can install the product on.
The official Databricks Certified Professional Data Engineer outline is organized into 10 domains. The first three are:
- Developing Code for Data Processing using Python and SQL — 22% of the exam
- Data Sharing and Federation — 5% of the exam
- Ensuring Data Security and Compliance — 10% of the exam
See the complete exam topics section above for the full outline and the weighting of every domain.
Databricks Certified Professional Data Engineer Sample Questions:
Question 1
A nightly job ingests data into a Delta Lake table using the following code:
The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?
def new_records():
A. return spark.read.option( " readChangeFeed " , " true " ).table ( " bronze " )
B. 
C. return spark.readStream.table( " bronze " )
D. 
E. return spark.readStream.load( " bronze " )
Question 2
A platform engineer is creating catalogs and schemas for the development team to use.
The engineer has created an initial catalog, catalog_A, and initial schema, schema_A. The engineer has also granted USE CATALOG, USE SCHEMA, and CREATE TABLE to the development team so that the engineer can begin populating the schema with new tables.
Despite being owner of the catalog and schema, the engineer noticed that they do not have access to the underlying tables in Schema_A.
What explains the engineer ' s lack of access to the underlying tables?
A. Permissions explicitly given by the table creator are the only way the Platform Engineer could access the underlying tables in their schema.
B. The owner of the schema does not automatically have permission to tables within the schema, but can grant them to themselves at any point.
C. Users granted with USE CATALOG can modify the owner ' s permissions to downstream tables.
D. The platform engineer needs to execute a REFRESH statement as the table permissions did not automatically update for owners.
Question 3
Which Python variable contains a list of directories to be searched when trying to locate required modules?
A. ,sys.path
B. pylib.source
C. importlib.resource path
D. os-path
E. pypi.path
Question 4
An external object storage container has been mounted to the location /mnt/finance_eda_bucket .
The following logic was executed to create a database for the finance team:
After the database was successfully created and permissions configured, a member of the finance team runs the following code:
If all users on the finance team are members of the finance group, which statement describes how the tx_sales table will be created?
A. A logical table will persist the query plan to the Hive Metastore in the Databricks control plane.
B. A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane.
C. A managed table will be created in the DBFS root storage container.
D. An external table will be created in the storage container mounted to /mnt/finance eda bucket.
E. An managed table will be created in the storage container mounted to /mnt/finance eda bucket.
Question 5
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. 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)
C. class Pipeline:
def transform(self, df):
return df.withColumn( " value_upper " , upper(col( " value " )))
pipeline = Pipeline()
assertDataFrameEqual(pipeline.transform(test_input), expected)
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)
Solutions:
| Question 1 Answer: B | Question 2 Answer: B | Question 3 Answer: A | Question 4 Answer: E | Question 5 Answer: B |
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