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

  • Exam Code: Certified-Data-Engineer-Professional
  • Exam Name: Databricks Certified Data Engineer Professional
  • Updated: Aug 26, 2026
  • Q & A: 250 Questions and Answers

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

SectionObjectives
Ensuring Data Security and Compliance- 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
      - 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
            Monitoring and Alerting- Monitoring
            • 1. Use Databricks REST APIs and Databricks CLI to monitor jobs and pipelines
              • 2. Use system tables for observability of resource utilization, cost, auditing, and workloads
                • 3. Use Lakeflow Declarative Pipelines event logs to monitor pipelines
                  • 4. Use Query Profile and Spark UI to monitor workloads
                    - Alerting
                    • 1. Use SQL Alerts to monitor data quality
                      • 2. Use the Workflows UI and Jobs API to configure notifications for job status and performance issues
                        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
                            Debugging and Deploying- Deploying CI/CD
                            • 1. Configure and integrate Git-based CI/CD workflows using Databricks Git folders for notebook and code deployment
                              • 2. Build and deploy Databricks resources using Databricks Asset Bundles
                                - 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
                                      Data Ingestion & Acquisition- Design and implement data ingestion pipelines
                                      • 1. Ingest formats including Delta Lake, Parquet, ORC, AVRO, JSON, CSV, XML, text, and binary data from sources such as message buses and cloud storage
                                        • 2. Create an append-only data pipeline capable of handling both batch and streaming data using Delta
                                          Data Sharing and Federation- Share and federate data
                                          • 1. Use Delta Sharing to share live data from the Lakehouse with any computing platform
                                            • 2. Configure Lakehouse Federation with appropriate governance across supported source systems
                                              • 3. Demonstrate secure Delta Sharing between Databricks deployments using Databricks-to-Databricks sharing or with external platforms using the open sharing protocol
                                                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
                                                    Cost & Performance Optimization- Optimize cost and performance
                                                    • 1. Understand Delta optimization techniques such as deletion vectors and liquid clustering
                                                      • 2. Understand Databricks query optimization techniques for large datasets, including data skipping and file pruning
                                                        • 3. Understand how and why Unity Catalog managed tables reduce operational overhead and maintenance burden
                                                          • 4. Apply Change Data Feed to address streaming table limitations and improve latency
                                                            • 5. Use query profiling to identify bottlenecks such as inefficient joins and data shuffling
                                                              Developing Code for Data Processing using Python and SQL- Building and Testing an ETL Pipeline with Lakeflow Declarative Pipelines, SQL, and Apache Spark
                                                              • 1. Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for scalable ETL pipelines
                                                                • 2. Build and manage reliable, production-ready batch and streaming data pipelines using Lakeflow Declarative Pipelines and Auto Loader
                                                                  • 3. Explain the advantages and disadvantages of streaming tables compared to materialized views
                                                                    • 4. Use APPLY CHANGES APIs to simplify CDC in Lakeflow Declarative Pipelines
                                                                      • 5. Create and automate ETL workloads using Jobs through the UI, APIs, or CLI
                                                                        • 6. Choose appropriate configurations for environments, dependencies, high-memory notebook tasks, and retry behavior
                                                                          • 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. Develop User-Defined Functions using Pandas/Python UDF
                                                                                  • 3. Manage and troubleshoot external third-party library installations and dependencies, including PyPI packages, local wheels, and source archives
                                                                                    Data Modeling- Design and optimize data models
                                                                                    • 1. Design and implement scalable data models using Delta Lake to manage large datasets
                                                                                      • 2. Identify the benefits of liquid clustering over partitioning and Z-Ordering
                                                                                        • 3. Design dimensional models for analytical workloads with efficient querying and aggregation
                                                                                          • 4. Simplify data layout decisions and optimize query performance using liquid clustering

                                                                                            Databricks Certified Data Engineer Professional Sample Questions:

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


                                                                                            2. In order to facilitate near real-time workloads, a data engineer is creating a helper function to leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function will automatically detect the schema of the source directly, incrementally process JSON files as they arrive in a source directory, and automatically evolve the schema of the table when new fields are detected.
                                                                                            The function is displayed below with a blank:

                                                                                            Which response correctly fills in the blank to meet the specified requirements?

                                                                                            A)

                                                                                            B)

                                                                                            C)

                                                                                            D)

                                                                                            E)


                                                                                            3. Which Python variable contains a list of directories to be searched when trying to locate required modules?

                                                                                            A) importlib.resource path
                                                                                            B) os.path
                                                                                            C) sys.path
                                                                                            D) pypi.path
                                                                                            E) pylib.source


                                                                                            4. A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.
                                                                                            assert(myIntegrate(lambda x: x*x, 0, 3) [0] == 9)
                                                                                            Which kind of the test does the above line exemplify?

                                                                                            A) Unit
                                                                                            B) Manual
                                                                                            C) functional
                                                                                            D) End-to-end
                                                                                            E) Integration


                                                                                            5. A view is registered with the following code:

                                                                                            Both users and orders are Delta Lake tables.
                                                                                            Which statement describes the results of querying recent_orders?

                                                                                            A) All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
                                                                                            B) All logic will execute when the view is defined and store the result of joining tables to the DBFS; this stored data will be returned when the view is queried.
                                                                                            C) All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
                                                                                            D) Results will be computed and cached when the view is defined; these cached results will incrementally update as new records are inserted into source tables.


                                                                                            Solutions:

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

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