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
Data Sharing and Federation- Delta Sharing
  • 1. Share live Lakehouse data with external computing platforms
    • 2. Configure Databricks-to-Databricks Sharing
      • 3. Configure sharing with external platforms using the open sharing protocol
        - Lakehouse Federation
        • 1. Configure Lakehouse Federation with appropriate governance
          Data Modelling- Dimensional Modelling
          • 1. Design dimensional models for analytical workloads
            - Scalable Data Models
            • 1. Understand Liquid Clustering versus partitioning and Z-Ordering
              • 2. Design and implement scalable data models using Delta Lake
                • 3. Optimize data layout using Liquid Clustering
                  Ensuring Data Security and Compliance- Compliance
                  • 1. Develop data purging solutions according to data retention policies
                    • 2. Implement pipelines that detect and mask personally identifiable information
                      - Data Security
                      • 1. Use ACLs to secure workspace objects and enforce least privilege
                        • 2. Use row filters and column masks for sensitive data
                          • 3. Apply anonymization and pseudonymization techniques
                            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. Understand deletion vectors and liquid clustering
                                      • 3. Use Change Data Feed to address streaming table limitations and improve latency
                                        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
                                            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
                                                  Data Transformation, Cleansing, and Quality- Advanced Data Transformation
                                                  • 1. Write efficient Spark SQL and PySpark transformations
                                                    • 2. Apply window functions, joins, and aggregations to large datasets
                                                      - Data Quality
                                                      • 1. Develop data quarantining processes for invalid data
                                                        • 2. Apply data quality controls using Lakeflow Spark Declarative Pipelines or Auto Loader
                                                          Debugging and Deploying- 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
                                                                - Deploying CI/CD
                                                                • 1. Build and deploy Databricks resources using Databricks Asset Bundles
                                                                  • 2. Integrate Git-based CI/CD workflows using Databricks Git Folders
                                                                    Monitoring and Alerting- Monitoring
                                                                    • 1. Use Query Profiler and Spark UI to monitor workloads
                                                                      • 2. Use system tables for resource, cost, audit, and workload monitoring
                                                                        • 3. Use Databricks REST APIs and CLI for monitoring jobs and pipelines
                                                                          • 4. Use Lakeflow Spark Declarative Pipelines event logs for monitoring
                                                                            - Alerting
                                                                            • 1. Configure Lakeflow Jobs notifications for job status and performance issues
                                                                              • 2. Use SQL Alerts for data quality monitoring
                                                                                Developing Code for Data Processing using Python and SQL- Building and Testing ETL Pipelines
                                                                                • 1. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines
                                                                                  • 2. Use APPLY CHANGES APIs for change data capture
                                                                                    • 3. Create and automate ETL workloads using Jobs through UI, APIs, and CLI
                                                                                      • 4. Develop unit and integration tests for data processing code
                                                                                        • 5. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader
                                                                                          • 6. Compare streaming tables and materialized views
                                                                                            • 7. Use control flow operators in pipeline components
                                                                                              • 8. Configure environments, dependencies, memory, and retry behavior
                                                                                                - Using Python and Tools for Development
                                                                                                • 1. Develop User-Defined Functions using Pandas/Python UDFs
                                                                                                  • 2. Design and implement scalable Python project structures optimized for Databricks Asset Bundles
                                                                                                    • 3. Manage and troubleshoot third-party library installations and dependencies

                                                                                                      Databricks Certified Data Engineer Professional Sample Questions:

                                                                                                      Question 1

                                                                                                      A data engineer is configuring a Databricks Asset Bundle to deploy a job with granular permissions.
                                                                                                      The requirements are:
                                                                                                      - Grant the data-engineers group CAN_MANAGE access to the job.
                                                                                                      - Ensure the auditors' group can view the job but not modify/run it.
                                                                                                      - Avoid granting unintended permissions to other users/groups.
                                                                                                      How should the data engineer deploy the job while meeting the requirements?

                                                                                                      A. permissions:
                                                                                                      - group_name: data-engineers
                                                                                                      level: CAN_MANAGE
                                                                                                      - group_name: auditors
                                                                                                      level: CAN_VIEW
                                                                                                      resources:
                                                                                                      jobs:
                                                                                                      my-job:
                                                                                                      name: data-pipeline
                                                                                                      tasks: [...]
                                                                                                      job_clusters: [...]
                                                                                                      B. resources:
                                                                                                      jobs:
                                                                                                      my-job:
                                                                                                      name: data-pipeline
                                                                                                      tasks: [...]
                                                                                                      job: [...]
                                                                                                      permissions:
                                                                                                      - group_name: data-engineers
                                                                                                      level: CAN_MANAGE
                                                                                                      permissions:
                                                                                                      - group_name: auditors
                                                                                                      level: CAN_VIEW
                                                                                                      C. resources:
                                                                                                      jobs:
                                                                                                      my-job:
                                                                                                      name: data-pipeline
                                                                                                      tasks: [...]
                                                                                                      job_clusters: [...]
                                                                                                      permissions:
                                                                                                      - group_name: data-engineers
                                                                                                      level: CAN_MANAGE
                                                                                                      - group_name: auditors
                                                                                                      level: CAN_VIEW
                                                                                                      D. resources:
                                                                                                      jobs:
                                                                                                      my-job:
                                                                                                      name: data-pipeline
                                                                                                      tasks: [...]
                                                                                                      job_clusters: [...]
                                                                                                      permissions:
                                                                                                      - group_name: data-engineers
                                                                                                      level: CAN_MANAGE
                                                                                                      - group_name: auditors
                                                                                                      level: CAN_VIEW
                                                                                                      - group_name: admin-team
                                                                                                      level: IS_OWNER


                                                                                                      Question 2

                                                                                                      A junior developer complains that the code in their notebook isn't producing the correct results in the development environment. A shared screenshot reveals that while they're using a notebook versioned with Databricks Repos, they're using a personal branch that contains old logic. The desired branch named dev-2.3.9 is not available from the branch selection dropdown.
                                                                                                      Which approach will allow this developer to review the current logic for this notebook?

                                                                                                      A. Merge all changes back to the main branch in the remote Git repository and clone the repo again
                                                                                                      B. Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-2.3.9
                                                                                                      C. Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.
                                                                                                      D. Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch
                                                                                                      E. Use Repos to merge the current branch and the dev-2.3.9 branch, then make a pull request to sync with the remote repository


                                                                                                      Question 3

                                                                                                      A data engineer is implementing a job to download multiple PDF files from a third-party provided REST API endpoint by specifying different report types. The REST API is time-consuming and encounters intermittent errors, so the engineer wants to track each download activity to know when it fails and to retry partially, while providing scalable throughput. The engineer needs to download ten report types, and the list can be changed over time. How should the data engineer achieve this?

                                                                                                      A. Define ten Notebook tasks to clearly track which report download failed.
                                                                                                      B. Use a foreach task with a list of report types as its inputs.
                                                                                                      C. Use a Delta Lake table to track each report download status as 10 rows, and use it as a source table to execute the download function as a Pandas UDF.
                                                                                                      D. Define a list variable within a Notebook to loop through the report types to download them, and print the download results. Execute it as a Notebook tasks.


                                                                                                      Question 4

                                                                                                      A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries.
                                                                                                      In which location can one review the timeline for cluster resizing events?

                                                                                                      A. Workspace audit logs
                                                                                                      B. Cluster Event Log
                                                                                                      C. Driver's log file
                                                                                                      D. Ganglia
                                                                                                      E. Executor's log file


                                                                                                      Question 5

                                                                                                      Which statement describes integration testing?

                                                                                                      A. Requires manual intervention
                                                                                                      B. Validates behavior of individual elements of your application
                                                                                                      C. Requires an automated testing framework
                                                                                                      D. Validates interactions between subsystems of your application
                                                                                                      E. Validates an application use case


                                                                                                      Solutions:

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

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