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Databricks Certified Data Engineer Professional : Databricks-Certified-Data-Engineer-Professional

Databricks-Certified-Data-Engineer-Professional

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

Exam Name: Databricks Certified Data Engineer Professional Exam

Updated: Aug 25, 2026

Q & A: 250 Questions and Answers

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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

SectionWeightObjectives
Developing Code for Data Processing using Python and SQL22%- Implement complex data processing logic
- Write efficient and maintainable code
- Use Databricks-specific libraries and APIs
Monitoring and Alerting10%- Track data lineage and metrics
- Set up alerts and notifications
- Monitor pipeline performance and health
Debugging and Deploying10%- Deploy using Asset Bundles, CLI, and APIs
- Troubleshoot and debug pipelines
- Implement CI/CD and DevOps practices
Data Ingestion & Acquisition7%- Use Auto Loader and structured streaming
- Ingest data from diverse sources
- Handle incremental and batch data loads
Data Sharing and Federation5%- Use Delta Sharing for secure data sharing
- Implement Lakehouse Federation
- Manage cross-platform data access
Data Modelling6%- Design Medallion Architecture
- Optimize table design and partitioning
- Implement dimensional and relational models
Cost & Performance Optimisation13%- Apply cost management best practices
- Improve query and pipeline performance
- Optimize compute and storage resources
Data Transformation, Cleansing, and Quality10%- Apply data cleansing and validation rules
- Enforce data quality standards
- Implement schema evolution and management
Data Governance7%- Enforce data policies and standards
- Manage data assets and metadata
- Use Unity Catalog for governance
Ensuring Data Security and Compliance10%- Implement access control and permissions
- Secure data at rest and in transit
- Ensure data privacy and compliance

Databricks Certified Data Engineer Professional Sample Questions:

1. The Databricks workspace administrator has configured interactive clusters for each of the data engineering groups. To control costs, clusters are set to terminate after 30 minutes of inactivity.
Each user should be able to execute workloads against their assigned clusters at any time of the day.
Assuming users have been added to a workspace but not granted any permissions, which of the following describes the minimal permissions a user would need to start and attach to an already configured cluster.

A) Workspace Admin privileges, cluster creation allowed. "Can Attach To" privileges on the required cluster
B) "Can Manage" privileges on the required cluster
C) "Can Restart" privileges on the required cluster
D) Cluster creation allowed. "Can Restart" privileges on the required cluster
E) Cluster creation allowed. "Can Attach To" privileges on the required cluster


2. A data engineer has a Delta table orders with deletion vectors enabled. The engineer executes the following command:
DELETE FROM orders WHERE status = 'cancelled';
What should be the behavior of deletion vectors when the command is executed?

A) Rows are marked as deleted both in metadata and in files.
B) Rows are marked as deleted in metadata, not in files.
C) Delta automatically removes all cancelled orders permanently.
D) Files are physically rewritten without the deleted rows.


3. A company has a task management system that tracks the most recent status of tasks. The system takes task events as input and processes events in near real-time using Lakeflow Declarative Pipelines. A new task event is ingested into the system when a task is created or the task status is changed. Lakeflow Declarative Pipelines provides a streaming table (tasks_status) for BI users to query.
The table represents the latest status of all tasks and includes 5 columns:
task_id (unique for each task)
task_name
task_owner
task_status
task_event_time
The table enables three properties: deletion vectors, row tracking, and change data feed (CDF).
A data engineer is asked to create a new Lakeflow Declarative Pipeline to enrich the tasks_status table in near real-time by adding one additional column representing task_owner's department, which can be looked up from a static dimension table (employee).
How should this enrichment be implemented?

A) Create a new Lakeflow Declarative Pipeline: use readStream() function with option readChangeFeed to read tasks_status table CDF; enrich with the employee table; create a new streaming table as the result table and use apply_changes() function to process the changes from the enriched CDF.
B) Create a new Lakeflow Declarative Pipeline: use the readStream() function with the option skipChangeCommits to read the tasks_status table; enrich with the employee table; store the result in a new streaming table.
C) Create a new Lakeflow Declarative Pipeline: use the read() function to read tasks_status table; enrich with employee table; store the result in a materialized view.
D) Create a new Lakeflow Declarative Pipeline: use the readStream() function to read tasks_status table; enrich with the employee table; store the result in a new streaming table.


4. The data engineer team has been tasked with configured connections to an external database that does not have a supported native connector with Databricks. The external database already has data security configured by group membership. These groups map directly to user group already created in Databricks that represent various teams within the company. A new login credential has been created for each group in the external database. The Databricks Utilities Secrets module will be used to make these credentials available to Databricks users. Assuming that all the credentials are configured correctly on the external database and group membership is properly configured on Databricks, which statement describes how teams can be granted the minimum necessary access to using these credentials?

A) "Read'' permissions should be set on a secret key mapped to those credentials that will be used by a given team.
B) "Manage" permission should be set on a secret scope containing only those credentials that will be used by a given team.
C) "Read" permissions should be set on a secret scope containing only those credentials that will be used by a given team.
D) No additional configuration is necessary as long as all users are configured as administrators in the workspace where secrets have been added.


5. A data engineer is troubleshooting a slow-running Delta Lake query on Databricks SQL involves complex joins and large datasets. They need to identify whether the root cause is related to poor data skipping, inefficient join strategies, or excessive data shuffling. Which approach should identify the specific bottlenecks using native Databricks tools?

A) Enable the EXPLAIN command to review the parsed logical plan and manually estimate shuffle sizes.
B) Check the query's execution time in the Jobs UI and correlate it with cluster resource utilization metrics.
C) Analyze the Top Operators panel in the Query Profile to identify high-cost operations like BroadcastNestedLoopJoin
D) Use the LIMIT clause to run a subset of the query and compare execution times with the full dataset.


Solutions:

Question # 1
Answer: C
Question # 2
Answer: B
Question # 3
Answer: A
Question # 4
Answer: C
Question # 5
Answer: C

Databricks-Certified-Data-Engineer-Professional Related Exams
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Databricks-Certified-Data-Engineer-Associate-JPN - Databricks Certified Data Engineer Associate Exam (Databricks-Certified-Data-Engineer-Associate日本語版)
Databricks-Certified-Data-Engineer-Associate - Databricks Certified Data Engineer Associate Exam
Databricks-Certified-Professional-Data-Engineer - Databricks Certified Professional Data Engineer Exam
Databricks-Certified-Professional-Data-Scientist - Databricks Certified Professional Data Scientist Exam
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