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

Certified-Data-Engineer-Professional Exam Simulator
  • 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
Topic 1: 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. Use row filters and column masks to protect sensitive table data
        • 2. Apply anonymization and pseudonymization methods including hashing, tokenization, suppression, and generalization
          • 3. Use ACLs to secure workspace objects and enforce the principle of least privilege
            Topic 2: Cost & Performance Optimization- Optimize cost and performance
            • 1. Understand Delta optimization techniques such as deletion vectors and liquid clustering
              • 2. Understand how and why Unity Catalog managed tables reduce operational overhead and maintenance burden
                • 3. Apply Change Data Feed to address streaming table limitations and improve latency
                  • 4. Understand Databricks query optimization techniques for large datasets, including data skipping and file pruning
                    • 5. Use query profiling to identify bottlenecks such as inefficient joins and data shuffling
                      Topic 3: 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
                          Topic 4: 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 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 Modeling- Design and optimize data models
                                  • 1. Design dimensional models for analytical workloads with efficient querying and aggregation
                                    • 2. Design and implement scalable data models using Delta Lake to manage large datasets
                                      • 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 7: 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
                                                Topic 8: Developing Code for Data Processing using Python and SQL- Using Python and Tools for Development
                                                • 1. Manage and troubleshoot external third-party library installations and dependencies, including PyPI packages, local wheels, and source archives
                                                  • 2. Design and implement a scalable Python project structure optimized for Databricks Asset Bundles, enabling modular development, deployment automation, and CI/CD integration
                                                    • 3. Develop User-Defined Functions using Pandas/Python UDF
                                                      - Building and Testing an ETL Pipeline with Lakeflow Declarative Pipelines, SQL, and Apache Spark
                                                      • 1. Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, testing frameworks, and debugging tools
                                                        • 2. Create and automate ETL workloads using Jobs through the UI, APIs, or CLI
                                                          • 3. Choose appropriate configurations for environments, dependencies, high-memory notebook tasks, and retry behavior
                                                            • 4. Explain the advantages and disadvantages of streaming tables compared to materialized views
                                                              • 5. Create pipeline components using control flow operators such as if/else and foreach
                                                                • 6. Use APPLY CHANGES APIs to simplify CDC in Lakeflow Declarative Pipelines
                                                                  • 7. Build and manage reliable, production-ready batch and streaming data pipelines using Lakeflow Declarative Pipelines and Auto Loader
                                                                    • 8. Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for scalable ETL pipelines
                                                                      Topic 9: Debugging and Deploying- 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
                                                                            - 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
                                                                                Topic 10: Monitoring and Alerting- Monitoring
                                                                                • 1. Use Databricks REST APIs and Databricks CLI to monitor jobs and pipelines
                                                                                  • 2. Use Lakeflow Declarative Pipelines event logs to monitor pipelines
                                                                                    • 3. Use Query Profile and Spark UI to monitor workloads
                                                                                      • 4. Use system tables for observability of resource utilization, cost, auditing, and 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

                                                                                            Databricks Certified Data Engineer Professional Sample Questions:

                                                                                            1. A Delta Lake table was created with the below query:

                                                                                            Realizing that the original query had a typographical error, the below code was executed:
                                                                                            ALTER TABLE prod.sales_by_stor RENAME TO prod.sales_by_store
                                                                                            Which result will occur after running the second command?

                                                                                            A) A new Delta transaction log Is created for the renamed table.
                                                                                            B) The table reference in the metastore is updated and no data is changed.
                                                                                            C) All related files and metadata are dropped and recreated in a single ACID transaction.
                                                                                            D) The table reference in the metastore is updated and all data files are moved.
                                                                                            E) The table name change is recorded in the Delta transaction log.


                                                                                            2. Which statement describes a key benefit of an end-to-end test?

                                                                                            A) It pinpoint errors in the building blocks of your application.
                                                                                            B) It makes it easier to automate your test suite
                                                                                            C) It closely simulates real world usage of your application.
                                                                                            D) It provides testing coverage for all code paths and branches.


                                                                                            3. A data engineer is setting up a pipeline to ingest data from a message bus system that occasionally delivers duplicate messages. The duplicate messages can be a week apart. The target is a Databricks Delta Lake table where each record should appear exactly once. Which Databricks ingestion pattern should be implemented to handle potential duplicates where events can arrive outside of the configured watermark?

                                                                                            A) Use Delta Lake time travel to identify and remove duplicates
                                                                                            B) Use Delta Lake's change data feed to filter duplicate records
                                                                                            C) Configure Structured Streaming with dropDuplicates transformation
                                                                                            D) Implement a write operation using MERGE INTO with a unique key


                                                                                            4. A junior data engineer has manually configured a series of jobs using the Databricks Jobs UI.
                                                                                            Upon reviewing their work, the engineer realizes that they are listed as the "Owner" for each job.
                                                                                            They attempt to transfer "Owner" privileges to the "DevOps" group, but cannot successfully accomplish this task.
                                                                                            Which statement explains what is preventing this privilege transfer?

                                                                                            A) Other than the default "admins" group, only individual users can be granted privileges on jobs.
                                                                                            B) Databricks jobs must have exactly one owner; "Owner" privileges cannot be assigned to a group.
                                                                                            C) A user can only transfer job ownership to a group if they are also a member of that group.
                                                                                            D) The creator of a Databricks job will always have "Owner" privileges; this configuration cannot be changed.
                                                                                            E) Only workspace administrators can grant "Owner" privileges to a group.


                                                                                            5. A CHECK constraint has been successfully added to the Delta table named activity_details using the following logic:

                                                                                            A batch job is attempting to insert new records to the table, including a record where latitude =
                                                                                            45.50 and longitude = 212.67.
                                                                                            Which statement describes the outcome of this batch insert?

                                                                                            A) The write will insert all records except those that violate the table constraints; the violating records will be recorded to a quarantine table.
                                                                                            B) The write will fail when the violating record is reached; any records previously processed will be recorded to the target table.
                                                                                            C) The write will insert all records except those that violate the table constraints; the violating records will be reported in a warning log.
                                                                                            D) The write will fail completely because of the constraint violation and no records will be inserted into the target table.
                                                                                            E) The write will include all records in the target table; any violations will be indicated in the boolean column named valid_coordinates.


                                                                                            Solutions:

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

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