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Exam Code: Certified-Data-Engineer-Professional

Exam Name: Databricks Certified Data Engineer Professional

Updated: Oct 14, 2026

Q & A: 250 Questions and Answers

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

SectionObjectives
Topic 1: Data Modelling- Scalable Data Models
  • 1. Optimize data layout using Liquid Clustering
    • 2. Understand Liquid Clustering versus partitioning and Z-Ordering
      • 3. Design and implement scalable data models using Delta Lake
        - Dimensional Modelling
        • 1. Design dimensional models for analytical workloads
          Topic 2: Developing Code for Data Processing using Python and SQL- Using Python and Tools for Development
          • 1. Manage and troubleshoot third-party library installations and dependencies
            • 2. Develop User-Defined Functions using Pandas/Python UDFs
              • 3. Design and implement scalable Python project structures optimized for Databricks Asset Bundles
                - Building and Testing ETL Pipelines
                • 1. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines
                  • 2. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader
                    • 3. Use APPLY CHANGES APIs for change data capture
                      • 4. Create and automate ETL workloads using Jobs through UI, APIs, and CLI
                        • 5. Configure environments, dependencies, memory, and retry behavior
                          • 6. Develop unit and integration tests for data processing code
                            • 7. Use control flow operators in pipeline components
                              • 8. Compare streaming tables and materialized views
                                Topic 3: Data Transformation, Cleansing, and Quality- Data Quality
                                • 1. Develop data quarantining processes for invalid data
                                  • 2. Apply data quality controls using Lakeflow Spark Declarative Pipelines or Auto Loader
                                    - Advanced Data Transformation
                                    • 1. Apply window functions, joins, and aggregations to large datasets
                                      • 2. Write efficient Spark SQL and PySpark transformations
                                        Topic 4: Data Ingestion & Acquisition- Design and implement data ingestion pipelines
                                        • 1. Build append-only pipelines for batch and streaming data using Delta
                                          • 2. Ingest Delta Lake, Parquet, ORC, Avro, JSON, CSV, XML, Text, and Binary data
                                            • 3. Ingest data from message buses and cloud storage
                                              Topic 5: Cost & Performance Optimisation- Cost Optimization
                                              • 1. Understand how Unity Catalog managed tables reduce operational overhead
                                                - Query Performance
                                                • 1. Use Query Profile to identify performance bottlenecks
                                                  • 2. Identify inefficient joins and excessive data shuffling
                                                    - Delta Optimization
                                                    • 1. Apply data skipping and file pruning techniques
                                                      • 2. Use Change Data Feed to address streaming table limitations and improve latency
                                                        • 3. Understand deletion vectors and liquid clustering
                                                          Topic 6: Debugging and Deploying- Deploying CI/CD
                                                          • 1. Build and deploy Databricks resources using Databricks Asset Bundles
                                                            • 2. Integrate Git-based CI/CD workflows using Databricks Git Folders
                                                              - Debugging and Troubleshooting
                                                              • 1. Use Lakeflow Spark Declarative Pipelines event logs and Spark UI for debugging
                                                                • 2. Analyze errors and remediate failed job runs
                                                                  • 3. Use Spark UI, cluster logs, system tables, and query profiles for diagnostics
                                                                    Topic 7: Monitoring and Alerting- Alerting
                                                                    • 1. Use SQL Alerts for data quality monitoring
                                                                      • 2. Configure Lakeflow Jobs notifications for job status and performance issues
                                                                        - Monitoring
                                                                        • 1. Use system tables for resource, cost, audit, and workload monitoring
                                                                          • 2. Use Query Profiler and Spark UI to monitor workloads
                                                                            • 3. Use Databricks REST APIs and CLI for monitoring jobs and pipelines
                                                                              • 4. Use Lakeflow Spark Declarative Pipelines event logs for monitoring
                                                                                Topic 8: Data Governance- Unity Catalog Permissions
                                                                                • 1. Understand the Unity Catalog permission inheritance model
                                                                                  - Metadata and Discoverability
                                                                                  • 1. Create and maintain descriptions and metadata for enterprise data
                                                                                    Topic 9: Ensuring Data Security and Compliance- Data Security
                                                                                    • 1. Apply anonymization and pseudonymization techniques
                                                                                      • 2. Use ACLs to secure workspace objects and enforce least privilege
                                                                                        • 3. Use row filters and column masks for sensitive data
                                                                                          - Compliance
                                                                                          • 1. Develop data purging solutions according to data retention policies
                                                                                            • 2. Implement pipelines that detect and mask personally identifiable information
                                                                                              Topic 10: Data Sharing and Federation- Lakehouse Federation
                                                                                              • 1. Configure Lakehouse Federation with appropriate governance
                                                                                                - Delta Sharing
                                                                                                • 1. Configure Databricks-to-Databricks Sharing
                                                                                                  • 2. Share live Lakehouse data with external computing platforms
                                                                                                    • 3. Configure sharing with external platforms using the open sharing protocol

                                                                                                      Databricks Certified Data Engineer Professional Sample Questions:

                                                                                                      Question #1

                                                                                                      When monitoring a complex workload, being able to see the query plan is critical to understanding what the workload is doing. Where can the visualization of the query plan be found?

                                                                                                      • A. In the Query Profiler, under the Stages tab
                                                                                                      • B. In the Spark UI, under the SQL/DataFrame tab
                                                                                                      • C. In the Spart UI, under the Jobs tab
                                                                                                      • D. In the Query Profiler, under Query Source
                                                                                                      Reveal Solution  Discussion  0

                                                                                                      Correct Answer: B  🗳️

                                                                                                      Explanation: Only visible for Actual4Exams members. You can sign-up / login (it's free).

                                                                                                      Question #2

                                                                                                      Each configuration below is identical to the extent that each cluster has 400 GB total of RAM 160 total cores and only one Executor per VM.
                                                                                                      Given an extremely long-running job for which completion must be guaranteed, which cluster configuration will be able to guarantee completion of the job in light of one or more VM failures?

                                                                                                      • A. - Total VMs: 2
                                                                                                        - 200 GB per Executor
                                                                                                        - 80 Cores / Executor
                                                                                                      • B. - Total VMs: 4
                                                                                                        - 100 GB per Executor
                                                                                                        - 40 Cores / Executor
                                                                                                      • C. - Total VMs: 16
                                                                                                        - 25 GB per Executor
                                                                                                        - 10 Cores / Executor
                                                                                                      • D. - Total VMs: 1
                                                                                                        - 400 GB per Executor
                                                                                                        - 160 Cores/Executor
                                                                                                      • E. - Total VMs: 8
                                                                                                        - 50 GB per Executor
                                                                                                        - 20 Cores / Executor
                                                                                                      Reveal Solution  Discussion  0

                                                                                                      Correct Answer: C  🗳️

                                                                                                      Question #3

                                                                                                      A data engineer needs to create an application that will collect information about the latest job run including the repair history. How should the data engineer format the request?

                                                                                                      • A. Call/api/2.1/jobs/runs/list with the run_id and include_history parameters
                                                                                                      • B. Call/api/2.1/jobs/runs/list with the job_id and include_history parameters
                                                                                                      • C. Call/api/2.1/jobs/runs/get with the job_id and include_history parameters
                                                                                                      • D. Call/api/2.1/jobs/runs/get with the run_id and include_history parameters
                                                                                                      Reveal Solution  Discussion  0

                                                                                                      Correct Answer: B  🗳️

                                                                                                      Explanation: Only visible for Actual4Exams members. You can sign-up / login (it's free).

                                                                                                      Question #4

                                                                                                      A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table.
                                                                                                      Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales.

                                                                                                      Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?

                                                                                                      • A. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.
                                                                                                      • B. Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.
                                                                                                      • C. Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af: if this entity exists, Cmd 2 will succeed.
                                                                                                      • D. Both commands will fail. No new variables, tables, or views will be created.
                                                                                                      • E. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.
                                                                                                      Reveal Solution  Discussion  0

                                                                                                      Correct Answer: A  🗳️

                                                                                                      Explanation: Only visible for Actual4Exams members. You can sign-up / login (it's free).

                                                                                                      Question #5

                                                                                                      The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalid latitude and longitude values in the activity_details table have been breaking their ability to use other geolocation processes.
                                                                                                      A junior engineer has written the following code to add CHECK constraints to the Delta Lake table:

                                                                                                      A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed.
                                                                                                      Which statement explains the cause of this failure?

                                                                                                      • A. The activity details table already contains records; CHECK constraints can only be added prior to inserting values into a table.
                                                                                                      • B. The activity details table already exists; CHECK constraints can only be added during initial table creation.
                                                                                                      • C. The current table schema does not contain the field valid coordinates; schema evolution will need to be enabled before altering the table to add a constraint.
                                                                                                      • D. Because another team uses this table to support a frequently running application, two-phase locking is preventing the operation from committing.
                                                                                                      • E. The activity details table already contains records that violate the constraints; all existing data must pass CHECK constraints in order to add them to an existing table.
                                                                                                      Reveal Solution  Discussion  0

                                                                                                      Correct Answer: E  🗳️

                                                                                                      Explanation: Only visible for Actual4Exams members. You can sign-up / login (it's free).

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