Databricks Certified-Data-Engineer-Professional : 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
Topic 1: Developing Code for Data Processing using Python and SQL- Building and Testing ETL Pipelines
  • 1. Compare streaming tables and materialized views
    • 2. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader
      • 3. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines
        • 4. Configure environments, dependencies, memory, and retry behavior
          • 5. Use APPLY CHANGES APIs for change data capture
            • 6. Create and automate ETL workloads using Jobs through UI, APIs, and CLI
              • 7. Develop unit and integration tests for data processing code
                • 8. Use control flow operators in pipeline components
                  - 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
                        Topic 2: Data Governance- Metadata and Discoverability
                        • 1. Create and maintain descriptions and metadata for enterprise data
                          - Unity Catalog Permissions
                          • 1. Understand the Unity Catalog permission inheritance model
                            Topic 3: Data Ingestion & Acquisition- Design and implement data ingestion pipelines
                            • 1. Ingest data from message buses and cloud storage
                              • 2. Ingest Delta Lake, Parquet, ORC, Avro, JSON, CSV, XML, Text, and Binary data
                                • 3. Build append-only pipelines for batch and streaming data using Delta
                                  Topic 4: Data Transformation, Cleansing, and Quality- Data Quality
                                  • 1. Apply data quality controls using Lakeflow Spark Declarative Pipelines or Auto Loader
                                    • 2. Develop data quarantining processes for invalid data
                                      - Advanced Data Transformation
                                      • 1. Apply window functions, joins, and aggregations to large datasets
                                        • 2. Write efficient Spark SQL and PySpark transformations
                                          Topic 5: Ensuring Data Security and Compliance- Compliance
                                          • 1. Implement pipelines that detect and mask personally identifiable information
                                            • 2. Develop data purging solutions according to data retention policies
                                              - Data Security
                                              • 1. Use ACLs to secure workspace objects and enforce least privilege
                                                • 2. Apply anonymization and pseudonymization techniques
                                                  • 3. Use row filters and column masks for sensitive data
                                                    Topic 6: 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 Query Profiler and Spark UI to monitor workloads
                                                          • 2. Use Databricks REST APIs and CLI for monitoring jobs and pipelines
                                                            • 3. Use system tables for resource, cost, audit, and workload monitoring
                                                              • 4. Use Lakeflow Spark Declarative Pipelines event logs for monitoring
                                                                Topic 7: Debugging and Deploying- Deploying CI/CD
                                                                • 1. Integrate Git-based CI/CD workflows using Databricks Git Folders
                                                                  • 2. Build and deploy Databricks resources using Databricks Asset Bundles
                                                                    - Debugging and Troubleshooting
                                                                    • 1. Use Spark UI, cluster logs, system tables, and query profiles for diagnostics
                                                                      • 2. Use Lakeflow Spark Declarative Pipelines event logs and Spark UI for debugging
                                                                        • 3. Analyze errors and remediate failed job runs
                                                                          Topic 8: Data Modelling- Dimensional Modelling
                                                                          • 1. Design dimensional models for analytical workloads
                                                                            - 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
                                                                                  Topic 9: 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
                                                                                          Topic 10: Cost & Performance Optimisation- Delta Optimization
                                                                                          • 1. Understand deletion vectors and liquid clustering
                                                                                            • 2. Apply data skipping and file pruning techniques
                                                                                              • 3. Use Change Data Feed to address streaming table limitations and improve latency
                                                                                                - 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

                                                                                                      Databricks Certified Data Engineer Professional Sample Questions:

                                                                                                      1. A data engineer is optimizing a MERGE operation on an 800GB UC-managed table that experiences frequent updates and deletions. Which two actions should the engineer prioritize to improve MERGE performance? (Choose two.)

                                                                                                      A) Use ZORDER on high-cardinality columns.
                                                                                                      B) Apply liquid clustering using the merge join keys.
                                                                                                      C) Partition the table by date.
                                                                                                      D) Overwrite the table instead of Merge.
                                                                                                      E) Enable deletion vectors on the table if not already enabled.


                                                                                                      2. All records from an Apache Kafka producer are being ingested into a single Delta Lake table with the following schema:
                                                                                                      key BINARY, value BINARY, topic STRING, partition LONG, offset LONG, timestamp LONG There are 5 unique topics being ingested. Only the "registration" topic contains Personal Identifiable Information (PII). The company wishes to restrict access to PII. The company also wishes to only retain records containing PII in this table for 14 days after initial ingestion.
                                                                                                      However, for non-PII information, it would like to retain these records indefinitely.
                                                                                                      Which of the following solutions meets the requirements?

                                                                                                      A) Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level.
                                                                                                      B) Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory.
                                                                                                      C) Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken.
                                                                                                      D) Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.
                                                                                                      E) All data should be deleted biweekly; Delta Lake's time travel functionality should be leveraged to maintain a history of non-PII information.


                                                                                                      3. A data engineer is analyzing a large, partitioned retail dataset in Databricks, where each row represents a sale made by a salesperson. The dataset contains millions of records with the following schema:
                                                                                                      sales_df: [salesperson_id: string, region: string, sale_amount: double, sale_date: date] The data engineer needs to generate a DataFrame that ranks salespeople within each region based on their total cumulative sales, with the highest seller ranked as 1. If multiple salespeople have the same total sales, they should share the same rank.
                                                                                                      The data engineer wants to implement this logic using a PySpark window function and the dense_rank () function.
                                                                                                      Which code snippet will perform this ranking?

                                                                                                      A)

                                                                                                      B)

                                                                                                      C)

                                                                                                      D)


                                                                                                      4. A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create.

                                                                                                      Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?

                                                                                                      A) One new job named "Ingest new data" will be defined in the workspace, but it will not be executed.
                                                                                                      B) The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.
                                                                                                      C) The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.
                                                                                                      D) Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.
                                                                                                      E) Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily.


                                                                                                      5. When a new Databricks project starts, the central IP team provisions the required infrastructure using Terraform and a Service Principal. This includes creating a Databricks workspace, a Unity Catalog linked to an External Location, and a Databricks group containing all project team members. Project teams must store all assets - e.g., tables and volumes, as Managed assets in Unity Catalog. This model hides infrastructure complexity while giving teams autonomy within their catalog. They can create and manage schemas, tables, volumes, and related objects but cannot rename, delete, or change catalog permissions, those remain under IT's control. Which rights should the project group be granted to enable this model?

                                                                                                      A) The group needs to have ALL PRIVILEGES and the MANAGE on the catalog.
                                                                                                      B) The group needs to have USE CATALOG and USE SCHEMA on the catalog.
                                                                                                      C) The group needs to have ALL PRIVILEGES on the catalog.
                                                                                                      D) The group should be made OWNER of the catalog.


                                                                                                      Solutions:

                                                                                                      Question # 1
                                                                                                      Answer: B,E
                                                                                                      Question # 2
                                                                                                      Answer: D
                                                                                                      Question # 3
                                                                                                      Answer: B
                                                                                                      Question # 4
                                                                                                      Answer: D
                                                                                                      Question # 5
                                                                                                      Answer: B

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