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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
Exam Price:USD 200 plus applicable taxes
Exam Duration:120 minutes
Related Certifications:Databricks Certified Data Engineer Associate
Real Exam Qty:59 scored multiple-choice questions
Exam Format:Test center proctored, Online proctored, Multiple-choice questions
Available Languages:English
Certificate Validity Period:2 years
Recommended Training:Databricks Academy
Advanced Data Engineering with Databricks
Exam Registration:Databricks Certified Data Engineer Professional Certification
Sample Questions:Databricks Certified-Data-Engineer-Professional Sample Questions
Exam Way:Online proctored or test center proctored
Pre Condition:No prerequisite is required. Related course attendance and one year of hands-on experience in data engineering tasks covered by the exam are highly recommended.
Official Syllabus URL:https://www.databricks.com/sites/default/files/2025-11/databricks-certified-data-engineer-professional-exam-guide-november-30-2025.pdf

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

SectionObjectives
Topic 1: Data Sharing and Federation- Share and federate data
  • 1. Demonstrate secure Delta Sharing between Databricks deployments using Databricks-to-Databricks sharing or with external platforms using the open sharing protocol
    • 2. Use Delta Sharing to share live data from the Lakehouse with any computing platform
      • 3. Configure Lakehouse Federation with appropriate governance across supported source systems
        Topic 2: Monitoring and Alerting- Alerting
        • 1. Use the Workflows UI and Jobs API to configure notifications for job status and performance issues
          • 2. Use SQL Alerts to monitor data quality
            - Monitoring
            • 1. Use Query Profile and Spark UI to monitor workloads
              • 2. Use Databricks REST APIs and Databricks CLI to monitor jobs and pipelines
                • 3. Use Lakeflow Declarative Pipelines event logs to monitor pipelines
                  • 4. Use system tables for observability of resource utilization, cost, auditing, and workloads
                    Topic 3: Data Ingestion & Acquisition- Design and implement data ingestion pipelines
                    • 1. Create an append-only data pipeline capable of handling both batch and streaming data using Delta
                      • 2. Ingest formats including Delta Lake, Parquet, ORC, AVRO, JSON, CSV, XML, text, and binary data from sources such as message buses and cloud storage
                        Topic 4: Ensuring Data Security and Compliance- Applying Data Security Mechanisms
                        • 1. Apply anonymization and pseudonymization methods including hashing, tokenization, suppression, and generalization
                          • 2. Use ACLs to secure workspace objects and enforce the principle of least privilege
                            • 3. Use row filters and column masks to protect sensitive table data
                              - Ensuring Compliance
                              • 1. Implement compliant batch and streaming pipelines that detect and mask PII
                                • 2. Develop data purging solutions that comply with data retention policies
                                  Topic 5: Data Transformation, Cleansing, and Quality- Transform and validate data
                                  • 1. Write efficient Spark SQL and PySpark code for advanced transformations including window functions, joins, and aggregations
                                    • 2. Develop a quarantining process for bad data with Lakeflow Declarative Pipelines or Auto Loader in classic jobs
                                      Topic 6: Data Governance- Govern enterprise data
                                      • 1. Create and add descriptions and metadata to enterprise data to improve discoverability
                                        • 2. Demonstrate understanding of the Unity Catalog permission inheritance model
                                          Topic 7: Data Modeling- Design and optimize data models
                                          • 1. Design and implement scalable data models using Delta Lake to manage large datasets
                                            • 2. Design dimensional models for analytical workloads with efficient querying and aggregation
                                              • 3. Simplify data layout decisions and optimize query performance using liquid clustering
                                                • 4. Identify the benefits of liquid clustering over partitioning and Z-Ordering
                                                  Topic 8: Debugging and Deploying- Deploying CI/CD
                                                  • 1. Build and deploy Databricks resources using Databricks Asset Bundles
                                                    • 2. Configure and integrate Git-based CI/CD workflows using Databricks Git folders for notebook and code deployment
                                                      - Debugging and Troubleshooting
                                                      • 1. Use Lakeflow Declarative Pipelines event logs and Spark UI to debug Lakeflow Declarative Pipelines and Spark pipelines
                                                        • 2. Identify diagnostic information using Spark UI, cluster logs, system tables, and query profiles to troubleshoot errors
                                                          • 3. Analyze errors and remediate failed job runs using job repairs and parameter overrides
                                                            Topic 9: Developing Code for Data Processing using Python and SQL- Building and Testing an ETL Pipeline with Lakeflow Declarative Pipelines, SQL, and Apache Spark
                                                            • 1. Explain the advantages and disadvantages of streaming tables compared to materialized views
                                                              • 2. Build and manage reliable, production-ready batch and streaming data pipelines using Lakeflow Declarative Pipelines and Auto Loader
                                                                • 3. Use APPLY CHANGES APIs to simplify CDC in Lakeflow Declarative Pipelines
                                                                  • 4. Create and automate ETL workloads using Jobs through the UI, APIs, or CLI
                                                                    • 5. Choose appropriate configurations for environments, dependencies, high-memory notebook tasks, and retry behavior
                                                                      • 6. Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for scalable ETL pipelines
                                                                        • 7. Create pipeline components using control flow operators such as if/else and foreach
                                                                          • 8. Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, testing frameworks, and debugging tools
                                                                            - Using Python and Tools for Development
                                                                            • 1. Design and implement a scalable Python project structure optimized for Databricks Asset Bundles, enabling modular development, deployment automation, and CI/CD integration
                                                                              • 2. Manage and troubleshoot external third-party library installations and dependencies, including PyPI packages, local wheels, and source archives
                                                                                • 3. Develop User-Defined Functions using Pandas/Python UDF
                                                                                  Topic 10: Cost & Performance Optimization- Optimize cost and performance
                                                                                  • 1. Understand how and why Unity Catalog managed tables reduce operational overhead and maintenance burden
                                                                                    • 2. Understand Databricks query optimization techniques for large datasets, including data skipping and file pruning
                                                                                      • 3. Apply Change Data Feed to address streaming table limitations and improve latency
                                                                                        • 4. Understand Delta optimization techniques such as deletion vectors and liquid clustering
                                                                                          • 5. Use query profiling to identify bottlenecks such as inefficient joins and data shuffling

                                                                                            Databricks Certified Data Engineer Professional Sample Questions:

                                                                                            Question #1

                                                                                            A workspace admin has created a new catalog called finance_data and wants to delegate permission management to a finance team lead without giving them full admin rights. Which privilege should be granted to the finance team lead?

                                                                                            • A. GRANT OPTION privilege on the finance_data catalog.
                                                                                            • B. ALL PRIVILEGES on the finance_data catalog.
                                                                                            • C. MANAGE privilege on the finance_data catalog.
                                                                                            • D. Make the finance team lead a metastore admin.
                                                                                            Reveal Solution  Discussion  0

                                                                                            Correct Answer: C  🗳️

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                                                                                            Question #2

                                                                                            A data company uses Databricks Unity Catalog and has multiple enterprise data sources, including PostgreSQL, Snowflake, and SQL Server. The central data platform team wants to configure Lakehouse Federation so analysts can query external tables directly in Databricks using Databricks SQL, without duplicating data. Which steps are necessary to configure Lakehouse Federation in a secure and governed manner?

                                                                                            • A. Use Partner Connect to create linked datasets, and apply table ACLs at the source system to govern access through Databricks.
                                                                                            • B. Create external locations and storage credentials to connect to each database, then register foreign tables in Unity Catalog.
                                                                                            • C. Mirror the external datasets into Delta Lake using Auto Loader, and govern them using Data Lineage and System Tables.
                                                                                            • D. Configure connections and foreign catalog in Unity Catalog, then grant access to foreign catalogs, schemas, and tables using Unity Catalog permissions.
                                                                                            Reveal Solution  Discussion  0

                                                                                            Correct Answer: D  🗳️

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                                                                                            Question #3

                                                                                            Incorporating unit tests into a PySpark application requires upfront attention to the design of your jobs, or a potentially significant refactoring of existing code.
                                                                                            Which statement describes a main benefit that offset this additional effort?

                                                                                            • A. Troubleshooting is easier since all steps are isolated and tested individually
                                                                                            • B. Yields faster deployment and execution times
                                                                                            • C. Ensures that all steps interact correctly to achieve the desired end result
                                                                                            • D. Improves the quality of your data
                                                                                            • E. Validates a complete use case of your application
                                                                                            Reveal Solution  Discussion  0

                                                                                            Correct Answer: A  🗳️

                                                                                            Question #4

                                                                                            A security team wants to enforce data protection for a customer table containing customer PII data. To comply with local policies, sales team members should only see customers from their region, while non-admin users should have email addresses masked. Which implementation approach should be used when using Unity Catalog row filters and column masks?

                                                                                            • A. Create SQL UDFs for row filtering based on user region and column masking based on group membership, then apply them using ALTER TABLE SET ROW FILTER and ALTER COLUMN SET MASK commands.
                                                                                            • B. Create a view with dynamic WHERE clauses for region filtering and use string replacement functions for email masking using ALTER COLUMN SET MASK command.
                                                                                            • C. Use table ACLs to restrict access using tags with GRANT SELECT ON table_name WITH TAG command, and rely on application-level filtering for sensitive data based on user region.
                                                                                            • D. Implement row filters with SQL UDFs based on user region only since column masks cannot be combined with row filters on the same table, then apply them be recreating the table with DROP TABLE and CREATE TABLE SET ROW FILTER commands.
                                                                                            Reveal Solution  Discussion  0

                                                                                            Correct Answer: A  🗳️

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                                                                                            Question #5

                                                                                            A data engineer wants to automate job monitoring and recovery in Databricks using the Jobs API.
                                                                                            They need to list all jobs, identify a failed job, and rerun it. Which sequence of API actions should the data engineer perform?

                                                                                            • A. Use the jobs/list endpoint to list jobs, check job run statuses with jobs/runs/list, and rerun a failed job using jobs/run-now.
                                                                                            • B. Use the jobs/list endpoint to list jobs, then use the jobs/create endpoint to create a new job, and run the new job using jobs/run-now.
                                                                                            • C. Use the jobs/cancel endpoint to remove failed jobs, then recreate them with jobs/create and run the new ones.
                                                                                            • D. Use the jobs/get endpoint to retrieve job details, then use jobs/update to rerun failed jobs.
                                                                                            Reveal Solution  Discussion  0

                                                                                            Correct Answer: A  🗳️

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