Purchase Databricks : Certified-Data-Engineer-Professional Exam Materials and then pass exam easily
Updated: Oct 11, 2026
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Databricks Certified-Data-Engineer-Professional Exam Overview:
| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Data Engineer Professional |
| Exam Number: | Certified Data Engineer Professional |
| Passing Score: | Not publicly specified in the current official exam guide |
| Related Certifications: | Databricks Certified Data Engineer Associate |
| Real Exam Qty: | 59 scored questions |
| Exam Format: | Multiple-choice |
| Exam Duration: | 120 minutes |
| Exam Price: | USD 200, plus applicable taxes as required by local law |
| Available Languages: | English |
| Certificate Validity Period: | 2 years |
| Sample Questions: | Databricks Certified-Data-Engineer-Professional Sample Questions |
| Exam Way: | Online proctored or test center proctored |
| Pre Condition: | No mandatory prerequisite. Databricks recommends related course attendance and approximately one year of hands-on experience performing the Data Engineering tasks covered by the exam. |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/data-engineer-professional |
Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Data Governance | - Unity Catalog Permissions
|
| Topic 2: Debugging and Deploying | - Deploying CI/CD
|
| Topic 3: Ensuring Data Security and Compliance | - Data Security
|
| Topic 4: Data Modelling | - Scalable Data Models
|
| Topic 5: Data Ingestion & Acquisition | - Design and implement data ingestion pipelines
|
| Topic 6: Developing Code for Data Processing using Python and SQL | - Building and Testing ETL Pipelines
|
| Topic 7: Data Sharing and Federation | - Lakehouse Federation
|
| Topic 8: Cost & Performance Optimisation | - Delta Optimization
|
| Topic 9: Monitoring and Alerting | - Alerting
|
| Topic 10: Data Transformation, Cleansing, and Quality | - Data Quality
|
Databricks Certified Data Engineer Professional Sample Questions:
A data engineer has created a transactions Delta table on Databricks that should be used by the analytics team. The analytics team wants to use the table with another tool that requires Apache Iceberg format. What should the data engineer do?
- A. Enable uniform on the transactions table to 'iceberg' so that the table can be read as an Iceberg table.
- B. Require the analytics team to use a tool that supports Delta table.
- C. Convert the transactions Delta table to Iceberg and enable uniform so that the table can be read as a Delta table.
- D. Create an Iceberg copy of the transactions Delta table which can be used by the analytics team.
Correct Answer: C 🗳️
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A data engineering team is implementing an append-only data pipeline using Delta Lake, and wants to ensure that data is never modified or deleted once written. Which Delta Lake feature should the data engineer enable to prevent modifications to existing data?
- A. Delta Time Travel
- B. Delta VACUUM
- C. Delta OPTIMIZE
- D. Delta APPEND_ONLY
Correct Answer: D 🗳️
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Which statement regarding stream-static joins and static Delta tables is correct?
- A. The checkpoint directory will be used to track updates to the static Delta table.
- B. Stream-static joins cannot use static Delta tables because of consistency issues.
- C. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch.
- D. The checkpoint directory will be used to track state information for the unique keys present in the join.
- E. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.
Correct Answer: C 🗳️
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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.readStream.table("bronze")
- B.
- C. return spark.readStream.load("bronze")
- D.
- E. return spark.read.option("readChangeFeed", "true").table ("bronze")
Correct Answer: B 🗳️
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The data governance team has instituted a requirement that the "user" table containing Personal Identifiable Information (PII) must have the appropriate masking on the SSN column. This means that anyone outside of the HRAdminGroup should see masked social security numbers as ***-**-
****.
The team created a masking function:
What does the data governance team need to do next to achieve this goal?
- A. CREATE TABLE users
(name STRING, ssn INT MASKED ssn_mask); - B. CREATE TABLE users
(name STRING, int STRING);
ALTER TABLE users ALTER COLUMN ssn CREATE MASK if is_member('HRAdminGroup'); - C. CREATE TABLE users
(name STRING, ssn STRING);
ALTER TABLE users ALTER COLUMN ssn SET MASK ssn_mask; - D. CREATE TABLE users
(name STRING);
ALTER TABLE users CREATE COLUMN ssn CREATE MASK ssn_mask;
Correct Answer: C 🗳️
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