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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 Duration:120 minutes
Related Certifications:Databricks Certified Data Engineer Associate
Available Languages:English
Real Exam Qty:59 scored multiple-choice questions
Certificate Validity Period:2 years
Exam Price:USD 200 plus applicable taxes
Exam Format:Test center proctored, Online proctored, Multiple-choice questions
Recommended Training:Advanced Data Engineering with Databricks
Databricks Academy
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. Configure Lakehouse Federation with appropriate governance across supported source systems
    • 2. Demonstrate secure Delta Sharing between Databricks deployments using Databricks-to-Databricks sharing or with external platforms using the open sharing protocol
      • 3. Use Delta Sharing to share live data from the Lakehouse with any computing platform
        Topic 2: Data Modeling- Design and optimize data models
        • 1. Design and implement scalable data models using Delta Lake to manage large datasets
          • 2. Simplify data layout decisions and optimize query performance using liquid clustering
            • 3. Identify the benefits of liquid clustering over partitioning and Z-Ordering
              • 4. Design dimensional models for analytical workloads with efficient querying and aggregation
                Topic 3: Developing Code for Data Processing using Python and SQL- Using Python and Tools for Development
                • 1. Develop User-Defined Functions using Pandas/Python UDF
                  • 2. Manage and troubleshoot external third-party library installations and dependencies, including PyPI packages, local wheels, and source archives
                    • 3. Design and implement a scalable Python project structure optimized for Databricks Asset Bundles, enabling modular development, deployment automation, and CI/CD integration
                      - 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. Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, testing frameworks, and debugging tools
                          • 3. Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for scalable ETL pipelines
                            • 4. Choose appropriate configurations for environments, dependencies, high-memory notebook tasks, and retry behavior
                              • 5. Create and automate ETL workloads using Jobs through the UI, APIs, or CLI
                                • 6. Build and manage reliable, production-ready batch and streaming data pipelines using Lakeflow Declarative Pipelines and Auto Loader
                                  • 7. Create pipeline components using control flow operators such as if/else and foreach
                                    • 8. Use APPLY CHANGES APIs to simplify CDC in Lakeflow Declarative Pipelines
                                      Topic 4: 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 Lakeflow Declarative Pipelines event logs to monitor pipelines
                                            • 2. Use system tables for observability of resource utilization, cost, auditing, and workloads
                                              • 3. Use Query Profile and Spark UI to monitor workloads
                                                • 4. Use Databricks REST APIs and Databricks CLI to monitor jobs and pipelines
                                                  Topic 5: Ensuring Data Security and Compliance- Ensuring Compliance
                                                  • 1. Develop data purging solutions that comply with data retention policies
                                                    • 2. Implement compliant batch and streaming pipelines that detect and mask PII
                                                      - Applying Data Security Mechanisms
                                                      • 1. Apply anonymization and pseudonymization methods including hashing, tokenization, suppression, and generalization
                                                        • 2. Use row filters and column masks to protect sensitive table data
                                                          • 3. Use ACLs to secure workspace objects and enforce the principle of least privilege
                                                            Topic 6: Data Transformation, Cleansing, and Quality- Transform and validate data
                                                            • 1. Develop a quarantining process for bad data with Lakeflow Declarative Pipelines or Auto Loader in classic jobs
                                                              • 2. Write efficient Spark SQL and PySpark code for advanced transformations including window functions, joins, and aggregations
                                                                Topic 7: 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. Analyze errors and remediate failed job runs using job repairs and parameter overrides
                                                                        • 3. Identify diagnostic information using Spark UI, cluster logs, system tables, and query profiles to troubleshoot errors
                                                                          Topic 8: 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 9: 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 10: Cost & Performance Optimization- Optimize cost and performance
                                                                                  • 1. Understand how and why Unity Catalog managed tables reduce operational overhead and maintenance burden
                                                                                    • 2. Use query profiling to identify bottlenecks such as inefficient joins and data shuffling
                                                                                      • 3. Understand Delta optimization techniques such as deletion vectors and liquid clustering
                                                                                        • 4. Understand Databricks query optimization techniques for large datasets, including data skipping and file pruning
                                                                                          • 5. Apply Change Data Feed to address streaming table limitations and improve latency

                                                                                            Databricks Certified Data Engineer Professional Sample Questions:

                                                                                            Question 1

                                                                                            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. 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.
                                                                                            D. 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.


                                                                                            Question 2

                                                                                            What statement is true regarding the retention of job run history?

                                                                                            A. It is retained for 60 days, after which logs are archived
                                                                                            B. It is retained for 60 days, during which you can export notebook run results to HTML
                                                                                            C. It is retained until you export or delete job run logs
                                                                                            D. It is retained for 90 days or until the run-id is re-used through custom run configuration
                                                                                            E. It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3


                                                                                            Question 3

                                                                                            A data engineering workspace was automatically enabled for Unity Catalog, creating a workspace catalog. New team members report they can create tables in the default schema but cannot access table in other schemas within the same workspace catalog. Why are the new team members unable to access tables in other schemas?

                                                                                            A. Workspace catalog permissions are not subject to inheritance rules.
                                                                                            B. Workspace users receive USE CATALOG and specific privileges on default schema only.
                                                                                            C. New users only receive CREATE TABLE privileges on the default schema.
                                                                                            D. Tables in other schemas require additional BROWSEprivileges that new users don't receive automatically


                                                                                            Question 4

                                                                                            A data engineer is tasked with building a nightly batch ETL pipeline that processes very large volumes of raw JSON logs from a data lake into Delta tables for reporting. The data arrives in bulk once per day, and the pipeline takes several hours to complete. Cost efficiency is important, but performance and reliability of completing the pipeline are the highest priorities. Which type of Databricks cluster should the data engineer configure?

                                                                                            A. A job cluster configured to autoscale across multiple workers during the pipeline run.
                                                                                            B. An all-purpose cluster always kept running to ensure low-latency job startup times.
                                                                                            C. A lightweight single-node cluster with low worker node count to reduce costs.
                                                                                            D. A high-concurrency cluster designed for interactive SQL workloads.


                                                                                            Question 5

                                                                                            The data governance team is reviewing code used for deleting records for compliance with GDPR. They note the following logic is used to delete records from the Delta Lake table named users.

                                                                                            Assuming that user_id is a unique identifying key and that delete_requests contains all users that have requested deletion, which statement describes whether successfully executing the above logic guarantees that the records to be deleted are no longer accessible and why?

                                                                                            A. Yes; Delta Lake ACID guarantees provide assurance that the delete command succeeded fully and permanently purged these records.
                                                                                            B. No; files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files.
                                                                                            C. No; the Delta cache may return records from previous versions of the table until the cluster is restarted.
                                                                                            D. No; the Delta Lake delete command only provides ACID guarantees when combined with the merge into command.
                                                                                            E. Yes; the Delta cache immediately updates to reflect the latest data files recorded to disk.


                                                                                            Solutions:

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

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